Monsieur Yvon Besanger

List of Figures

Acknowledgement

First of all, I would like to thank my thesis director, Benoît Delinchant, for giving me the opportunity to work on this project and for always being available to discuss different problems related to project and thesis. His precious suggestions and guidance helped me to learn a lot over the course of project development and thesis redaction. I would also like to thank my thesis co-director, Béatrice Roussillion, whose guidance assisted me a lot for the work done regarding experimental economics. I would also like to thank my other cosupervisors Frédéric Wurtz and Daniel Llerena, for their unfailing motivation.

Working with them on this project is both enriching and stimulating. Under the supervision of these supervisors, I am able to present a thesis based on the cross-disciplinary impact of experimental economics and electrical engineering.

The rest of the list is long, and I am thankful to all those who supported me through the development of experimental work and thesis redaction. I would specifically like to thanks:

-Tiansi Laranjeira: For her help and support regarding the operation and debugging of experiment architecture.

-Jérôme Ferrari: For a) indicating that we can use EJP/TEMPO as a protocol if there are no more anticipated PP1/PP2 days and b) his assistance regarding creating and deploying a SMS sending plateform.

-Nils Artiges: For his guidance to overcome different obstacles in the project and thesis work. His remarks on reports and presentations help me a lot to improve the presentation skills.

-Adélaïde Fadhuile and Penelope Buckley: For their guidance regarding econometric analysis of the energy consumption.

-Cédric Lanu and Erick Raberanto: For working on the Etude-Elec sub-domain and keeping track of subjects activity on this plateform.

-Manon Boudet, Manon Bacchetta and Emma Papet: For working on the analysis on socio-technical characteristics of the households as part of their course project.

Finally, I would also like to acknowledge and thanks the funding agency to work on this project. This work has been supported by the Auvergne Rhône-Alpes region, with "Pack Ambition Recherche 2017 -Expesigno projet" (17 01110301). This work has also been partially supported by the CDP Eco-SESA receiving fund from the French National Research Agency in the framework of the "Investissements d'avenir" program (ANR-15-IDEX-02).

xiv

Abstract

The energy transition towards renewable energy sources and distributed generation is essential to mitigate climate change. However, the renewable energy sources are intermittent in nature, and therefore it may cause network congestion during peak consumption in future. Therefore, demand side energy flexibility is inevitable in complement to the supply side management and energy storage. The residential sector is the largest energy consumer and is therefore essential to implement demand-side energy flexibility. In addition to the existing approaches of energy flexibility, we present in this thesis a non-monetary "indirect" flexibility through the implementation of a behavioral science experiment with 175 French households. The set of nudges used in this experiment does not oblige the participating households to implement energy flexibility for either monetary gain or loss aversion.

Nudge alerts are generated by predictive algorithms and sent by SMS to a group of households with the aim of carrying out either load shifting or load shedding. The nudge alerts are complemented with the suggestions about using (or not using) committed appliances to implement energy flexibility. After each alert, the group receives graphical feedback in which the load curve measured by LINKY smart meter is superposed by a theoretical reference curve specific to each household. In comparison to a control group, we can quantify the impact of these nudge signals on the energy consumption of the treated group. Finally, an energy disaggregation algorithm is presented that will allow us to detect the equipment used during the flexibility and thus will do an analysis of the commitment of households to look for potential appliances for energy flexibility.

Chapter I

Electricity grid and social challenge: Inevitability of residential consumer participation in energy flexibility

Energy flexibility has been in practice since many decades. However, in a future energy mix predominant with distributed generation of renewable sources, the need of demand side energy flexibility will be inevitable to complement supply side management and energy storage. The residential sector remains the focus of demand response programs, however the traditional demand response technique brings certain constraints towards the decision making of residential consumer.

This chapter emphasizes the need on energy flexibility in the future. In addition to existing demand response technique, a new technique is briefly introduced. The purpose of this technique is to use behavioral insights to aid the decision making of residential consumer without causing any monetary loss.

I.1 Electricity Supply, Associated CO2 Emission and

Necessity of Energy Flexibility

I.1.a Energy transition as a tool to mitigate climate change

The supply of sustainable energy is a necessity for humankind. Solar energy remains the most used form of energy as it provides heat and light throughout the day. The discovery of fossil energy sources and technologies introduced industrial eras in the world. The first industrial era in 19 th century served the humanity with coal for mass scale energy consumption. The second industrial era in 20 th century centralized electricity as bulk energy source for mass scale consumption. Electrical energy is traditionally generated by base load power plants and peak load power plants. The base load power plants (such as hydroelectric, coal-fired and nuclear power plants) are operational all the time. The energy provided by these power plants ensures fulfillment of minimum level of demand on an electrical grid.

Conventionally, the peak load power plants (such as oil-fired and gas-fired power plants)

are connected to the electric grid for balancing peak energy consumption.

The reliance on fossil fuel power plants brings CO2 emission, which is a main contributor in the phenomenon of global warming. This leads to the climate change. The issue of climate change has been in discussion worldwide in a series of conferences since 1992. As a result, 191 head of states ratified 'Paris Agreement' in COP21 1 . This agreement binds the signatories to hold "the increase in the global average temperature to well below 2 °C the pre-industrial levels" by the end of 21st century (United Nations, 2015).

The global warming beyond 1.5 °C is an existential threat to the biosphere and it has a considerable impact on human life. It is estimated by the pledges and targets of various countries that the global warming may reach 2.4 °C by the end of this century; however, with the current policies in place, the global warming may reach 3.1 °C by the end of this century (Climate Action Tracker, 2021). The projections are alarming for the sustainability of biosphere and the impact of climate change will become harsher over the course of time.

Some impacts of climate change that the humanity is currently facing are as follows:

-Increased frequency of direct impacts i.e., drought, flood, heatwave and wildfire; -Increase in indirect impacts i.e., water quality, air quality and ecological change; -Variation in environmental factors i.e., geography and vegetation;

-Social impacts i.e., loss of habitation, poverty and hunger;

-Health impacts like mental stress, undernutrition, respiratory and cardiovascular disease (World Health Organisation, 2018)

The scientists argue that the atmospheric CO2 concentration must not exceed 350 ppm 2 to maintain the global warming significantly less than 1.5 °C, however this threshold is surpassed in 1988 whereas it is exceeded to 410 ppm in 2020 [START_REF] Desing | Reducing climate risks with fast and complete energy transitions: applying the precautionary principle to the Paris agreement[END_REF]. Keeping in account the CO2 emissions pertained to the electricity production, the exigency of mitigating climate change brought an evolution in the energy sector during 21 st century. This evolution is not only caused by the continuous depletion of primary fossil fuel, but it also imparts the quest for consuming sustainable energy. In the third industrial era, the renewable energy (e.g., solar and wind energy) is preferred over the fossil fuel plants at 2 Parts per million grid level as well as at micro-grid level. The transition of energy landscape is under process and is essential to bring sustainable energy to the doorstep of humankind.

The application of renewable sources also bring innovation in the domain of microgrid. Prosumerism (also known as auto-consumption) is one of these innovations, which tend individuals to generate, self-consume and sell electrical energy from renewable source(s) at their local site. Generally, the prosumers install photovoltaic solar panels or biogas plant along with electricity storage at their local site. Local energy communities also came in to being as an extension of individual prosumers. These energy communities produce and self-consume energy from renewable sources. The prosumers sell excess energy to either national grid or make peer-to-peer energy exchange on low voltage microgrid. The decentralization of renewable energy production (with its merits and limitations) makes individuals (and local communities) more self-sufficient in their need of energy consumption via micro-grids, which may also solve the problem of price fluctuation.

I.1.b The quest for sustainable energy mix in European Union and in

France

In pursuit of having a sustainable energy mix, European Union is actively working in coordination with its member countries by issuing climate change directives. For this purpose, the European Union set short term, medium term and long-term goals for 2020, 2030 and 2050 respectively. As a long term goal, the European Union aims "to achieve netzero greenhouse gas emissions by 2050 through a socially fair transition in a cost-efficient manner" (European Commission, 2018). During 2019, the GHG emissions was reduced by 24% (as compared to 1990 level) in 27 EU member states; in which the power sector remained a major contributor with a reduction of 15% GHG emissions (European Commission, 2020). The share of final electricity from renewable energy production is 34% in 2020 [START_REF] Potrč | Sustainable renewable energy supply networks optimization -The gradual transition to a renewable energy system within the European Union by 2050[END_REF]. These achievements set a baseline for EU to move forward for fulfilling the medium-term ambition until 2030. Contrary to this, the marginal cost of one unit of electricity production by renewable sources is near zero, except for biomass-fired power plants [START_REF] Bahar | Cross-Border Trade in Electricity and the Development of Renewables-Based Electric Power: Lessons from Europe[END_REF].

Therefore, the renewable energy sources enter the merit order at the lowest level along with hydroelectric energy. Therefore, the price per unit of electricity remains nearly unchanged for same demand. Consequently, the CO2 emission from fossil fuel power plants is avoided.

However, this is not true all the time. The intermittency of renewable sources may push the electricity generation towards fossil fuel power plants to meet the peak demand. Yet, the generated energy quantity from fossil fuel power plants will be low compared to the conventional merit order. Hence, the CO2 emissions and cost of electricity will also be lower.

(a) (b) In the context of energy transition, the future energy grid will be massively energized with renewable energy. The energy demand is also increasing with time. Therefore, it can be anticipated that the intermittency of renewable energy and the increased demand will frequently pose network congestion on the electrical grid. Where it will bring stress to the electrical network and will be harmful for the sustainable power dispatch to the consumers, it will also bring loss of comfort to the consumer. In addition to this, it will also incur maintenance cost to the electrical grid. With an optimistic outlook of 100% energy transition in France in 2050, ADEME suggests complementing this energy transition with the inclusion of energy storage and demand side energy flexibility. Energy storage is expected to address the intermittency problem of renewable energy sources at supply side.

ADEME suggests three types of energy storage solutions namely intra-day, weekly and seasonal. It is estimated that in a scenario of 100% renewable energy in 2050, the energy storage by weekly, intra-day and seasonal solution would be 7 GW, 12 GW and 17 GW respectively (ADEME, 2015).

The demand side energy flexibility tends the consumer to consume energy within the available capacity. In the case of residential consumer, the energy flexibility is significant

I.2.a Incentive based direct Flexibility and its limitations

Incentive based programs allow the DSO 8 or aggregator to implement direct flexibility. It is also known as direct load control. Keeping in consideration the network 6 History of CUMULUS : https://cumulus-lamarque.com/historique/ 7 United States of America 8 Distribution system operator condition, the DSO or aggregator may achieve direct flexibility by switching the end-user load in accordance with a predefined contractual agreement. It is intrusive and requires installation of switching devices (e.g., relays and circuit breakers) at the end-user location.

In addition to this, it does not consider the participation of energy consumer to implement energy flexibility in real time.

Direct flexibility is significant for industrial sector to curtail big loads during peak hours. For instance, Energy Pool 9 is an aggregator of industrial loads, data centers and hospitals, which is practicing the business of direct flexibility in France. Around 1500 MW of flexible capacity is available to Energy Pool; whereby through an optimal decisionmaking for its clients, Energy Pool identifies flexibility potential of the clients, integrates the demand response and offers load adjustments in different markets [START_REF] Eid | Aggregation of demand side flexibility in a smart grid: A review for European market design[END_REF].

The clients of Energy Pool receive specific payments for their participation in either energy based or capacity based trading [START_REF] Pool | Unlocking energy market flexibility and demand side response[END_REF].

Contrary to the Energy Pool, Voltalis 10 is another aggregator which targets the residential users for load curtailment of electrical heating. The electrical heating accounted for 36% of the annual electricity consumption in French residential buildings during 2017 (ADEME, 2018). When Voltalis receives a signal from the transmission system operator, it curtails the electrical heating of its clients in short time intervals through a device installed at client's premises. The clients have a choice to opt out at any time by pushing a button on the device and use their electrical heating normally. Voltalis trades the aggregated flexibility in balancing markets and demand response mechanisms of the transmission system operator [START_REF] Eid | Aggregation of demand side flexibility in a smart grid: A review for European market design[END_REF]. The clients do not receive any financial benefit for reducing their electric heating load, however, they receive a reduction in their electricity bill as reward of participating in demand response.

Despite its robustness, the direct flexibility has certain limitations. The capacity curtailed through direct load control varies from client to client, therefore, certain concerns exist among the clients regarding the privacy and balanced use of direct load control [START_REF] Haque | Integrating direct and indirect load control for congestion management in LV networks[END_REF]. Additionally, it normally targets big loads like heating and cooling in households, which brings in loss of comfort for the residents. Where it incurs cost to the distribution system operator, it is also intrusive to the customer's energy consumption.

Therefore, the consumers may be reluctant to give the control of their domestic appliances.

9 Energy-Pool : https://www.energy-pool.eu/en/ 10 Voltalis : https://www.voltalis.com/

I.2.b Price based indirect flexibility and its limitations

Contrary to the direct load control, price-based programs are introduced for encouraging the energy consumers to implement energy flexibility themselves. These programs are grouped as indirect load control, therefore provide indirect energy flexibility.

They bring the cognitive burden of flexibility to the consumer and impel the consumer through extrinsic motivation to manage their energy consumption for monetary reasons.

Therefore, in response to any of the price-based instrument and in accordance with the client's degree of flexibility, the client practices load curtailment or load shifting. -TEMPO is a mechanism of critical peak pricing introduced by EDF, in which the electricity price for a day 'D' varies with respect to the color assigned on day 'D-1'. In this regard, the days of normal price of electricity is characterized by white color.

The electricity price on blue colored day is cheaper while it is more expensive for red colored day as compared to the white color days [START_REF] Albadi | Demand Response in Electricity Markets: An Overview[END_REF]).

-EJP is another instrument of critical peak pricing, in which the peak consumption day is color-coded in red and the subscribers pay a low tariff all year round, except for the 21 red days with higher tariff [START_REF] Bivas | La production d'effacement : comment offrir des économies d'électricité à des millions de foyers[END_REF].

Despite that the dynamic pricing is specifically more pertinent for the residential sector, it poses certain difficulties for its effective implementation. It requires installation of a smart meter at consumer site. The smart meter collects energy consumption data of the consumers and log it on the DSO server. This collection can be quite intrusive to a consumer's personal life as one can learn substantially about the habits and endeavors of a person by looking at energy consumption data. Therefore, the skeptic consumer is reluctant to allow the DSO to install smart meter owing to data privacy and security reason [START_REF] Goulden | Smart grids, smart users? The role of the user in demand side management[END_REF].

Another factor that hinders the decision making of a residential consumer is lack of Another constraint of consumer regarding dynamic pricing is the everyday life of a household [START_REF] Dütschke | Dynamic electricity pricing-Which programs do consumers prefer?[END_REF][START_REF] Goulden | Smart grids, smart users? The role of the user in demand side management[END_REF]. To maximize the benefit from the subscribed energy flexibility tariff, the consumer has to take action with respect to the price signal within the defined time slot of the DSO/aggregator. The consumer cannot postpone these actions if the consumer might not be able to follow the signal subjecting to externalities (absence from home, unfavorable ambient temperature etc.). In consequence, neither the peak consumption on the grid is considerably reduced, nor the electricity bill of the consumer. Table I.1 illustrates the merits and limitations of traditional energy flexibility. for weekend mornings and a last one for weekend evenings. The energy consumption in winter is more than in summer due to the use of electric heating. In terms of energy flexibility, it is estimated that the residential sector can contribute up to 1 GW of (direct and indirect) energy flexibility annually by 2030, which is equal to the combined flexibility 13 French national grid operator: https://www.rte-france.com/ 14 Profile coefficients: https://data.enedis.fr/explore/dataset/coefficients-des-profils/information expected by batteries and pumped hydro energy storage by the same year [START_REF] Dréau | A Bottom-Up Model to Evaluate the Flexibility of French Residential Wet Appliances[END_REF]. Which makes it a significant sector to achieve energy flexibility.

As mentioned above, Voltalis targets the electric load of residential sector as an aggregator, while giving a choice to its clients to opt out. Implementing energy flexibility on electric heating is not often exercisable. In case of direct flexibility, it may cause loss of thermal comfort to the residential customer. Furthermore, in the case of indirect flexibility, lack of motivation from dynamic pricing and fear of comfort loss may prevent the residential consumers to dial down their radiators during the hours of low ambient temperature. In such a case, it is more interesting to find out other loads that can actively take part in the process of energy flexibility.

I.3.b Identification of potential appliances for energy flexibility

The household appliances are categorized as follows:

-Continuously consuming appliances: There are some appliances which consumes energy continuously round the year e.g., refrigerator, freezer etc.

-Binary appliances: The binary appliances (e.g. kettle and coffee maker) are ON/OFF appliances and are becomes operational for a few minutes.

-Multi-stage appliances: The appliances such as washing machine and dishwasher are termed as multi-stage appliances as they have multiple cycles of operation for each usage.

Here, the first question arises that which appliance has more potential in achieving the goal of energy flexibility. Usually, the intended duration of energy flexibility is in the range of few minutes to few hours. Therefore, an appliance has more potential of flexibility if it offers large amount of flexible power in a short period. It can be observed from Figure I.9 that the maximum annual energy consumption of each household appliances is 500 kWh (except for water boiler). The average duration of operation of each appliance is different. E.g., the fridge is a continuous load, yet it consumes energy in the same fashion as cloth dryer (which is a multicycle load). However, they consume energy in the same fashion.

Therefore, it is found difficult to identify potential appliances in a household by looking only at their annual energy consumption.

I.3.c Non-monetary indirect energy flexibility: A new mechanism for residential sector

The limitation of direct flexibility and indirect flexibility for residential sector are briefly discussed in section I.2. In the case of dynamic pricing, where the consumer is given a choice to implement energy flexibility, it also brings in the notion that not implementing energy flexibility cause monetary loss. Generally, the residential consumer does not know the purpose of dunamic pricing i.e., reducing peak consumption to meet either intermittency of renewable energy or to avoid fossil fuel generation.

The classical economics suggests that a residential consumer behaves rationally towards the dynamic pricing. However, despite available information on pricing, there exists certain behavioral obstacles and externalities that hinders consumer decision to implement energy flexibility [START_REF] Sirin | Behavioral aspects of regulation: A discussion on switching and demand response in Turkish electricity market[END_REF]. The deviation from rational decision making is not an anomaly, rather it indicates that the decision making structure is influenced by factors like culture, environment and ambient temperature [START_REF] Gowdy | Behavioral economics and climate change policy[END_REF].

In some cases, the DSOs offers dynamic pricing on an opt-in basis, yet the overall consumer participation is low, signaling the weakness of the opt-in design [START_REF] Schneider | Behavioral considerations for effective timevarying electricity prices[END_REF]. In this case, it is required to influence the preference of residential consumer in such a way that it offers zero risk to the residential consumer for implementing energy flexibility.

Unlike classical economics based policy of incentivizing the consumer, the behavioral economics has demonstrated that the decision making can be influenced by nonmonetary and pro-social impulses [START_REF] Pratt | Defeat the Peak: Behavioral insights for electricity demand response program design[END_REF]. A way of doing this is to introduce a competition among the consumers. It has been found that a comparison of home energy usage with a focus on peer consumption decreased energy consumption by 1% to 2% [START_REF] Ayres | Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage[END_REF]. The competition-based programs tend the consumer to win only and therefore are effective during the duration of competition. In case of unlimited duration with no considerable monetary gain, the consumer might abandon its efforts after a period.

Thus, these type of programs does not remain sustainable in a long run.

Another important aspect of energy flexibility that is usually neglected in indirect energy flexibility is to bring the human in the loop through providing feedback. by RTE in collaboration with ADEME in France. This instrument is called mon-ecowatt 15 and it notifies the subscribers to curtail their load for an anticipated peak consumption. -Like price based indirect flexibility, it is non-intrusive.

-No price-based information is given to the households. Instead, the following information is given to the residential consumers o Anticipated network congestion or elevated renewable production.

o Certain actions are suggested to do during this period of network congestion or elevated renewable production -Unlike price based indirect flexibility, the households have zero (monetary) risk to act on the notification. No monetary gain or loss is induced.

-The households are given a system of choice to opt out of their pre-defined commitment with no obligation. Mon-ecowatt does not offer any such procedure.

-Unlike mon-ecowatt, the households are given indirect feedback of their energy flexibility in graphical form of load curve (as illustrated in Figure I.13). 15 Mon-Eco-Watt : https://www.monecowatt.fr/

I.4 Conclusion

The power sector is a major contributor of GHG emissions in the world, and therefore is majorly contributing to the phenomenon of climate change. The energy transition from fossil fuel power plants towards renewable power plants is inevitable to mitigate the effects of global warming. However, the renewable energy sources are intermittent in nature. In a future scenario when renewable energy production will be dominant in the electricity mix and demand will be increasing, the intermittency of renewable sources will pose network congestion problems to the electrical grid. The supply side management and energy storage cope with this challenge to a certain extent and therefore requires a performing demand side management.

The residential sector being one of the biggest consumption sectors offers potential for demand side flexibility. Incentive based direct flexibility is significant for controlling big loads; however it is not agreeable for the residential consumer due to unwanted remote switching. The price based indirect flexibility puts the burden of action on the consumer, however, the limited knowledge of dynamic tariff and uncertain return for the effort limits the implementation of indirect flexibility. In this case, it is required to influence the preference of the residential consumer in such way that it presents zero risk to the consumer The traditional demand response techniques are found to introduce limitations on the decision making of a residential consumer. According to dual process theory, the human brain uses system 2 of thinking in response to dynamic pricing and he poses certain questions to himself, which makes it riskier to act upon the price signal. Behavioral insights is significant in designing a new mode of energy flexibility that tend the energy consumer to use the system 1 of thinking. For this purpose, nudge tool is used that rearranges several choices in a choice architecture and drives individuals towards the desirable behavior.

In this chapter, an overview of the limitation of dynamic pricing are presented.

Following this, nudge-based energy flexibility is introduced. The architecture of a nudge based energy flexibility experiment with households is presented in the later part of the chapter.

II.1 Price based energy flexibility in the light of classical economics

The dynamic pricing has been in practice worldwide since many decades as indirect energy flexibility. The non-linear pricing of this tariff is usually based on a number of factors, i.e. the forecasted intermittent production, forecasted peak consumption etc. It is found in the literature that the predominant intention of introducing dynamic pricing is load curtailment. Where it helps the electric grid to avoid network congestion, it also helps the customer to decrease his energy bill in direct proportion to his energy consumption.

The impact of dynamic pricing for load shifting is studied by; for instance; [START_REF] Valenzuela | Modeling and simulation of consumer response to dynamic pricing with enabled technologies[END_REF]. A dynamic pricing model is also studied "to reduce the overlaps between residential and charging station loads by inspiring the temporal load shifting of electrical vehicles during evening peak load hours" [START_REF] Moghaddam | A Coordinated Dynamic Pricing Model for Electric Vehicle Charging Stations[END_REF] The classical economics presents a theory of rational choice regarding the human behavior towards consumption. This theory states that "all actions are fundamentally rational in character and the people calculate the likely costs and benefits of any action before deciding what to do" [START_REF] Browning | Understanding Contemporary Society: Theories of the Present[END_REF]. It constructs a theoretical model of human, which is called as 'homo economicus'. In a choice paradigm, the 'homo economicus' behaves rationally for his self-interest to pursue his defined goal optimally (Urbina & Ruiz-Villaverde, 2019). The classical economics considers that 'homo economicus' has characteristics like flawless rationality, unlimited cognitive capacity, narrow self-interest and preference consistency.

The cognitive burden of understanding and acting upon the tariff of dynamic pricing rests on the consumer. It has been observed that the consumers show cognitive difficulty in understanding nonlinear price systems i.e. dynamic pricing [START_REF] Bartolome | Which tax rate do people use: Average or marginal[END_REF][START_REF] Ito | Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing[END_REF].

The consumer either lacks information or is unable to process all the information of time varying electricity price in real time. The homo-economicus model suggests that the consumer responds to the actual marginal price; however it has been found that a rational consumer responds to expected marginal price [START_REF] Ito | Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing[END_REF][START_REF] Borenstein | To What Electricity Price Do Consumers Respond? Residential Demand Elasticity Under Increasing-Block Pricing[END_REF]. The expected marginal price is the one that is perceived by the consumer, and it may differ to a larger extent from the actual marginal price. It is also observed that in certain situations despite having updated information, the people do not know what to do in response to a signal. Despite their will to decrease electricity consumption, they do not know which appliances should be effective for either load curtailment during peak hours or load shifting towards off-peak hours. Therefore, they are unable to act upon the signal.

Another challenge comes when the consumer is uncertain about the potential consequences of the flexibility despite having proper information of dynamic tariff. The classical economics deals this situation with the 'expected utility theory'. It states that the decision maker chooses between risky or uncertain prospects by comparing their expected utility values. Through dynamic pricing, it is expected that the high price during peak hour prompts the consumer to implement energy flexibility (usually in the form of load curtailment). However, any exogenous factor (e.g., extreme ambient temperature) may lead the consumer to think otherwise. If a consumer does not make any effort (i.e., load shifting or load curtailment), the consumer might bring undesirable impact towards the electric grid. If the consumer is uncertain about the consequences of the flexibility, the consumer might not choose (or act upon) the dynamic pricing as it seems too risky.

In conclusion, the dynamic pricing acts as an extrinsic motivator to prompt the consumers for implementing energy flexibility. It is found that the households are likely to exhibit energy flexibility in response to higher prices, yet the dynamic pricing has modest to substantial impact to achieve energy flexibility [START_REF] Faruqui | Household response to dynamic pricing of electricity: a survey of 15 experiments[END_REF]. The studies show that dynamic pricing is riskier to respond towards the real time need of energy flexibility, mainly due to cognitive limitations of the consumer. Though the homoeconomicus model fits in the case of classical economics, yet a transition from homoeconomicus towards homo-sapiens is required to drive the residential consumers towards energy flexibility.

II.2 Behavioral insights for indirect energy flexibility

According to behavioral economics, the human thinking is represented by 'dual process theory'. This theory classifies human thinking in two types of systems namely system 1 and system 2. System 1 (of thinking) operates unconsciously, automatically, with little or no effort and with high capacity processes; whereas system 2 (of thinking) operates consciously, in control, with high effort and low capacity process [START_REF] Kahneman | Thinking, fast and slow[END_REF]. Table II.1 illustrates some attributes of dual system of thinking. The human behavior is influenced by several factors. It requires to maintain conformity between the actions or feelings with values, goals and public perception of personality [START_REF] Bénabou | Incentives and Prosocial Behavior[END_REF]. The economics suggests that individuals respond to extrinsic motivators, e.g., monetary incentives, loss aversion etc. However, according to certain studies, the extrinsic motivators undermine the intrinsic motivation of the consumer, has limited impact on treatment and negative impact on the persistence of treatment [START_REF] Bénabou | Intrinsic and Extrinsic Motivation[END_REF][START_REF] Bénabou | Incentives and Prosocial Behavior[END_REF].

Dynamic pricing serves as an extrinsic motivator, with the general purpose of driving the consumers to curtail their load. The consumers are susceptible to be influenced by the context, emotions, short-sightedness or any other driver towards irrationality [START_REF] Shroff | Evidence-based Policymaking in Singapore: A Policymaker's Toolkit[END_REF]. Therefore, the dynamic pricing instigates system 2 of thinking. The cognitive burden of acting on energy flexibility signal is on consumer which enables the consumer to make a controlled and calculated decision after analyzing the situation. The cognitive difficulty of understanding the dynamic pricing makes it riskier to implement following a decision through system 2 of thinking.

Behavioral insights is a toolbox emerged by the interdisciplinary conjunction between economics, psychology and sociology. The consumers are susceptible to be influenced by their immediate environment, emotions and other forms of irrationality. The behavioral insights studies people's cognitive biases and how they make choices and behave in real life situations. By studying these cognitive biases the behavioral economics serves to develop new methods, mechanisms and other interventions that would help people to achieve what they want [START_REF] Shroff | Evidence-based Policymaking in Singapore: A Policymaker's Toolkit[END_REF].

To tend the consumers towards implementing energy flexibility, it is inevitable that certain tools and mechanisms should be developed that instigate system 1 of thinking. In this way, the mechanism of decision making of implementing energy flexibility will be unconscious and automatic. The consumer does not have to do any mental effort to weigh the possibility of outcome or the opportunity cost of energy flexibility. This mechanism can be used in parallel with the monetary mechanism. Such mechanism can be devised by the utility of behavioral insights, in such a way that the mechanism keeps in account the cognitive ability of a residential energy consumer and external influencing factors.

II.2.a Nudge

Nudge is a technique in behavioral insights which provides indirect suggestions to influence the decision making of an individual or a group. The purpose of nudge is to bring positive reinforcement in the decision making. The most prominent definition of nudge is that "it is any aspect of the choice architecture that alters people's behavior in a predictable way without forbidding any options or significantly changing their economic incentives. To count as a mere nudge, the intervention must be easy and cheap to avoid." [START_REF] Thaler | Nudge: Improving decisions about health, wealth, and happiness[END_REF].

Nudge has been in use by governments around the word to make soft policies. These policies can be regulatory, economic or communication of information. For example, a randomized control trial experiment was performed in UK 16 during 2011. The purpose of this experiment is to see whether and how the repayment rate of overdue taxes could be increased. For this purpose, the control group received usual letter while the treated group received a letter with different text. It has been found that "if the recipient was told that most people in UK paid their taxes on time, repayment rates increased by 5%. However, if they were told about the repayment of the people in their town, the figure increased to 15%."

(van Bavel et al., 2013).

The individuals found it difficult to understand the situation and suffers from an imperfect ability to process new information due to the limitations in cognitive processes [START_REF] Momsen | From intention to action: Can nudges help consumers to choose renewable energy[END_REF]. The nudges are distinguished by conventional instruments of policy formation by its virtue of not assuming the economic benefit of rational choice. They are based on dual process theory; where preferences are rearranged in a choice architecture to abridge the thinking process. Nudge instigates the system 1 of thinking for automatic and involuntary decision making. An important element to consider while designing a nudge is the choice architecture offered to an individual or a group for bringing positive reinforcement towards decision making. "The choice architecture refers to the practice of influencing choice by organizing the context in which people make decisions" (Shafir, 2013).

29

It is observed through an experiment that nudge is easy to comprehend, resulting in an immediate reduction of energy consumption, whereas it takes longer for an individual to understand the impact of dynamic price on their energy consumption [START_REF] Buckley | Demand response as a common pool resource game: Nudges or peak pricing[END_REF]. While giving a freedom of choice, the right stimuli are put into focus in choice architecture so that the desired choice is either chosen by default or is easy and automatic to be chosen by the targeted individual or group. The following section demonstrates green nudges and their usability for energy flexibility.

II.2.b Green Nudge

Green nudge is an extension of nudge technique which is attributed to environmental cause and is used for the policy making at national and regional level. The The fossil fuel is under the process of phasing out during energy transition while simultaneously integrating renewable energy in the energy mix. The conventional model of energy provision shows that the generation of electricity follows the demand, i.e., higher the forecasted demand, higher will be the need of electricity generation. However, this might not be the case in the future. In a future energy mix predominant in renewable energy, the intermittency of production will pose a constraint on the provision of energy. The continuity of conventional pattern of energy consumption will not only bring network congestion but might also damage the transmission infrastructure.

The distributed generation of renewable energy is considered as a common pool resource. For instance, [START_REF] Wolsink | The research agenda on social acceptance of distributed generation in smart grids: Renewable as common pool resources[END_REF][START_REF] Wolsink | Distributed Generation Of Sustainable Energy As A Common Pool Resource: Social Acceptance In Rural Settings Of Smart (Micro-)Grid Configurations[END_REF] discusses the characterization and impact of distributed generation as a common pool resource, whereas [START_REF] Gollwitzer | Rethinking the sustainability and institutional governance of electricity access and mini-grids: Electricity as a common pool resource[END_REF] discussed whether the common pool resource theory "can assist in analyzing and designing sustainable institutions for managing electricity provision in rural mini-grids". A common pool resource is a certain stock of particular goods from which a flow of resource units can be drawn within its specific boundaries [START_REF] Ostrom | Governing the Commons: The Evolution of Institutions for Collective Action[END_REF]. The distributed renewable energy generation has the following characteristics which makes it a common pool resource:

-Renewability of stock i.e., energy;

-Scarcity of stock with respect to the optimal location of installation and intermittency of generation;

-Co-production of a common good in the form of energy community [START_REF] Wolsink | The research agenda on social acceptance of distributed generation in smart grids: Renewable as common pool resources[END_REF].

The usual pattern of energy consumption depicts that the individuals draw more than the sustainable amount of resource units from the available stock of electricity. This is considered as an overuse of electricity. However, in the future energy mix predominant with renewable energy sources, this usual practice of energy consumption will cause scarcity of energy, either through intermittency of renewable energy or high demand at a certain time.

The provision of electricity is excludable in nature i.e., an individual can use the electricity if he/she pays for it. However, the energy scarcity is not excludable as everyone connected to the grid will be affected from this deficiency. In this case, the conventional pattern needs to be altered where the demand should follow the intermittent energy production.

To avoid this socio-technical dilemma in the future, it is inevitable that the individuals should show flexible gesture towards energy consumption. Residential sector being the highest energy consumption sector offers an opportunity that the households should be accustomed with the energy flexibility gestures. In the present, encouraging a responsible and eco-friendly behavior towards energy consumption can contribute to the reduction of GHG emissions (and hence climate change). Though, the benefits of reducing GHG emissions are spread temporally, yet the cost should be borne in the present to be able to enjoy present and future benefits. The most prominent non-price nudge field study was performed in USA, in which the energy consumption of a household for a period is compared with the energy consumption of energy efficient neighbors and all neighbors. This comparison is presented in graphical form along with energy conservation tips in the report and is considered as the descriptive norm. An injunctive norm is also added in the report by giving a rating in the form of efficiency standing. This efficiency standing categorized the household as Great, Good or Below Average. Both norms are collectively called as Home Energy Report (HER).

II.2.c Review of nudges used in the field of energy worldwide

It is observed from the study that HER causes an average of 2% reduction in electricity consumption [START_REF] Allcott | Social norms and energy conservation[END_REF], whereas discontinuing the HER leads to decay of energy conservation practice by 10-20% per year [START_REF] Allcott | The Short-Run and Long-Run Effects of Behavioral Interventions: Experimental Evidence from Energy Conservation[END_REF].

Another significant work in this regard is [START_REF] Ito | Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand[END_REF], in which the authors studied the effect of moral suasion and economic incentives for energy conservation using randomized controlled trial in households. The habituation and dishabituation is also The impact of load curtailment signal complemented with two types of incentive schemes is studied by [START_REF] Llerena | Demand response in the workplace: A field experiment[END_REF]). An honorary contest is held in one group with an incentive for social cause. The subject that consumes lowest energy during the predefined load curtailment timeslots was considered winner. As incentive, the reward would be given to the NGO (non-governmental organization); chosen by the winner. In addition to this, the name of winner was publicly displayed on the welcome display of the building. The other contest was monetary in nature; in which half of the reward was given to the winner and the rest was distributed among other subjects. The results show that honorary contest bring a significant load curtailment by the subjects. Whereas the monetary contest seems to have no impact for load curtailment, albeit a chance of winning money. This experimental study brings the idea of testing nudge-based energy flexibility in residential sector. The types of nudges used in the experiment and the architecture of nudges is given as follows.

II.2.d The Set of Green Nudges Used In the Experiments

There are several ways that can be used to design a choice architecture for nudging.

For this experiment, a set of green nudges are conceived. The characteristics of this set is given below, whereas the information about each nudge in the set is given in Table II.3.

-Unlike most of the past experiments, no social or comparative norm is used as nudge. This is a peculiarity of this experiment.

-Unlike the past experiments which usually relies on extrinsic motivators, the purpose of this experiment is to nudge the intrinsic motivation of residential consumer.

-Feedback is added as a nudge in this experiment. This can be considered as a novel nudge.

-The nudges are designed in such a way that the mechanism for each intervention should be simple, equitable, have low or negligible cost and have no risk for the subjects.

Table II. [START_REF]Un mix électrique 100% renouvelable? Analyses et optimisations[END_REF] The nudges used in the experiment

Sr.

No.

Type of Nudge Remarks 1 Information 17

Since a common residential consumer is not aware of the forecasted grid condition, therefore information is given to the subjects of treated group of this experiment. The information pertains either about the forecasted network congestion or about the elevated renewable energy production.

2

Commitment

The commitment nudges tend the individual or group to remain behaviorally consistent by maintaining their commitment.

3 Feedback Feedback of each intervention is given back to the subjects of treated group, as described in detail in section II.3.c.iii. The purpose of this nudge is to introduce salience 18 in the intervention. It is expected from this nudge that it serves to keep the subject motivated for the future interventions.

II.3 An experiment of indirect energy flexibility in residential sector using green nudges

As mentioned before, the experiments related to nudge based indirect energy flexibility were conducted for the prime purpose of energy saving, rather than energy flexibility. One important aspect of these experiments is to distribute the subjects into control group and treated group. The control group acts as a standard against which the 17 Information can also be considered as priming. A primed stimulus is unconsciously held in the associative memory of the decision maker to subsequently influence the decision. 18 A feature salience makes a choice more noticeable than others by emphasizing certain aspects of choice architecture.

impact of treatment given to the treated group is analyzed. Another important aspect of these experiments is giving direct or indirect feedback to the subjects so that they can also know the impact of their action for energy conservation.

This section demonstrates the design of a behavioral science experiment for the purpose of implementing indirect energy flexibility in the residential sector. Contrary to the earlier experiments available in the literature review, our experiment does not use any social norm among peers or ratings for the participating subjects. Among the diverse nudge tools, non-social nudges are chosen. However, like earlier experiments, it includes a control group and indirect feedback towards the participating subjects. The impact of indirect energy flexibility for load curtailment (during peak hours) and load shifting (from peak hours to off-peak hours) is analyzed in the experiment. The section is divided in to following subsections.

-Methodological aspect of the experiment -Protocol of interaction between subjects and experiment organizers -Set of green nudges -Architecture of experiment

II.3.a Methodological aspect of the experiment

A field experiment is conducted to find out the causal effects of real-world behaviors on energy flexibility. Field experiment is defined as "a data collection strategy that employs manipulation and random assignment to investigate preferences and behaviors in naturally occurring contexts" [START_REF] Baldassarri | Field Experiments Across the Social Sciences[END_REF]. Internal and external validities are related to both lab experiment and field experiment. "Internal validity refers to the ability to draw confident causal conclusions from one's research. External validity refers to the ability to generalize from the research context to the settings that the research is intended to approximate" [START_REF] Loewenstein | EXPERIMENTAL ECONOMICS FROM THE VANTAGE-POINT OF BEHAVIOURAL ECONOMICSÃ[END_REF]. Contrary to lab experiment, a field experiment has the benefit of having external validity from the real-world environment in a large-scale experiment pool. However, a field experiment has less internal validity as compared to a lab experiment. In contrary to lab experiment, some variables (that affects the treatment group) cannot be controlled in a field experiment.

This field experiment is conducted in a randomized control trial mode. In this type of field experiment, the subjects are assigned to one (or multiple) treatment conditions. The

II.3.b Protocol of interaction between subjects and experiment organizers

The protocol of the experiment consists of multiple steps that were carried out before and within the duration. It is related to selection and interaction with the subjects of the experiment. It is given as follows.

II.3.b.i Campaign and recruitment of subjects for experiment

The recruitment of subjects was carried out between March and May 2019. A campaign for recruiting subjects was launched by the economists of GAEL 19 -The household should be composed of at least 2 people.

-The household should be in the area of metropolitan Grenoble.

-The household should be equipped with LINKY 20 smart meter. The LINKY smart meter permits the experiment organizers to collect the power consumption data from ENEDIS as per written consent with the subject.

-The household should not have a specific electricity supply contract (i.e. dynamic pricing).

II.3.b.ii Categorization of recruited subjects into groups

The recruited households are distributed into control group and treated group. The control group comprises of 79 households and treated group comprises of 96 households.

The allocation of the selected subjects between the two groups was carried out based on the strictest possible equivalence rule using following criteria.

-The number of people in the household.

-The size of the dwelling.

-The presence of household members in the home at least 3 days per week.

-The number of household appliances in the home.

The treatment group receives treatment in the form of a set of nudges for each intervention. Indirect feedback is also given to the treated group for each intervention. No intervention is made on weekends and French national holidays. The control group does not receive any treatment. It is formed to find out the externalities that are affecting the treatment of treated group during the experiment. It ensures that the energy consumption of treated group is only influenced by the nudge during alert day. 20 LINKY smart meter : https://www.enedis.fr/le-compteur-linky-un-outil-pour-la-transition-ecologique

Depending on their treatment assignment, subjects were invited to attend a study presentation session. The organization of the sessions ensured that no communication between subjects belonging to different treatments took place.

II.3.b.iii Remuneration of participation in the experiment

To keep the subjects engaged in the experiment, a small annual remuneration is given at the end of each experiment year. The amount of remuneration is 40€ per subject per year for 3 years of participations. It should be noted that this remuneration is not given as a reward for implementing energy flexibility, rather the remuneration serves as monetary indemnity against a survey and the consent of using subjects' energy consumption data.

II.3.b.iv Types of Alerts

This field experiment is conducted from November 2019 to September 2021. The following types of alerts are sent to the subjects. For the sake of convenience, the day of treatment is hereafter referred as alert day and is symbolized by 'D'.

-Green alert (GA)

The purpose of this alert is to nudge the treated group to shift their partial load from evening to the duration between noon and 3:00 PM on alert day 'D'.

II.3.b.v Sessions of Instruction

The presentation of the instructions respected the anonymity of the subjects and no communication between the subjects. To avoid any strategic behavior, especially in terms of the responses concerning the subjects' commitments, the principle of increasing information in several phases was adopted. Each subject was given a personal code to keep them anonymous. The sessions included between 16 and 25 subjects from the same treatment. Each session was organized in a strictly identical manner in 6 phases.

1. The first phase of the instructions consisted of presenting the challenge of energy transition towards renewable energy and consumption by households. Particular attention was paid to the gap between production times e.g., during sun hours in case of solar and consumption times i.e., peak of the household in the evening. To cope with this mismatch, the flexibility of household consumption was highlighted as one of the solutions to avoid the use of polluting thermal installations. Based on this information, the objectives of the study were presented. For the treated group, the aim is to better understand the opportunities for greater flexibility among households by testing recommendations and advice. For the control group, only the considerations of a better understanding of consumption behavior were mentioned.

2. The second phase of the instructions concerned the conduct and organization of the study. The presentation of the study process was identical for both groups. It was indicated that the duration of 2 years of study requires the inclusion of further phases where subjects would be asked to update their information about their equipment and usage, as well as (only for treated group) a questionnaire for the collection of information about the recommendations and feedback on the periods of intervention. For the control group, the presentation of the organization of the study simply consisted in underlining the need to collect information on the equipment and its uses from the beginning to the end of the study. For the treated group, the presentation of the organization of the study consisted in describing the critical periods for the consumption of electricity.

3. The third phase of the instructions consisted of collecting information on equipment and its use within households. A comprehensive questionnaire was administered before the subjects were informed of the very existence of a commitment principle that they would be asked to carry out in the next phase. This questionnaire, based on a study of the professional literature and institutional reports on household equipment, provides a fairly accurate representation of the dwelling, the set of equipment and their uses for each subject in the study.

4. The fourth phase of the instructions was primarily consisted of the recommendations for the orange alerts and green alerts. In parallel to this presentation, subjects were asked to indicate: i) which equipment was not present in their dwelling and ii) which equipment was not generally used during the intended period of intervention for orange alerts or was generally used during the intended period of intervention for green alerts. This step was particularly important to avoid subjects making commitments to actions that they could not carry out or were already carrying out. It was only based on the collection of this information, in a second stage that subjects were informed of the principle of commitments to be made for each type of period. These commitments were made based on information on the impacts that these could have in terms of energy consumption.

5.

For the sake of convenience, the periods of intervention for orange alerts will be called as orange periods, whereas the periods of intervention for green alerts will be called as green periods. The fifth phase focused on the alert system implemented throughout the study, for both the orange and green periods. For each orange or green period on day 'D', this system is based on: d. Finally, this fifth phase presented the feedback system on subjects' electricity consumption for the different orange or green periods. Thus, an SMS informs the subjects to view their personal account on the study website where they could find the consumption curves for each day with a period, namely a measured curve, made with the data from the smart meters at a step of ½ hour, and a reference curve which is an estimate of the usual consumption at that time of the day.

6. The sixth and final phase of the instructions concerned the presentation of the operational conditions for data collection, storage and security provisions applied to the data (both relating to the questionnaires and to electricity consumption). This phase ended with the collection of the subjects' consents for sharing of their data from the smart meter according to the regulations in vigor.

II.3.b.vi Survey through questionnaire

A questionnaire is given to each subject to obtain information regarding the presence of various appliances in their respective household. The questions were related to;

-Whether an appliance from given list of appliances is present in the house. The subjects are also asked to mention if any other appliance in present in the house.

-The number of each type of appliances present in the house.

-The time of use of the appliances present in household. For this purpose, a standard day is split into 7 timeslots. In addition to this, the subjects are also asked about the use of appliances during weekdays and weekends.

II.3.b.vii GDPR Consent

A consent is signed with the subjects of the experiment in compliance with GDPR 21every year for a period of 3 years. The subjects might opt out of the experiment after a year if they are not interested in signing the consent. Through this consent, the subject gives rights to the experiment organizers to use his/her data. For collecting power consumption data, the subject provides his/her identity number of electricity dispatch point (in French:

ID point de livraison) and permits the experiment organizers to obtain the power consumption data of the subject from ENEDIS as a third party. This consent binds the experiment organizers to strictly use the data of subject within the limits of experiment. The data includes response to questionnaire, measured load curve, appliance commitments and contact details.

II.3.b.viii Website

A dedicated subdomain Etude-Elec22 has been made available to the subjects of the experiment. The sample screenshots of website are given in Annex A. All the subjects have their personal account on the site, which allows them to;

-Respond to the online questionnaire regarding the presence of equipment.

-Change their default commitment for an alert day 'D'.

-View the result of their effort on alert day as feedback in graphical form.

II.3.c Design of set of green nudges

A set of green nudges is introduced in the experiment. Its purpose is to nudge the subjects to implement energy flexibility on alert day 'D'. The timeline of sending these nudge signals gives ample time to the treated subjects for making a decision. This timeline is given in section II.3.d. These nudges are devised to answer the 4 basic questions of a residential consumer i.e.

-Why should energy flexibility be implemented?

-When to act to implement energy flexibility?

-Which appliances should be used to implement energy flexibility?

-Following the implementation of energy flexibility, how did my effort perform?

II.3.c.i Information

The subjects are primed with an informational message regarding energy production and its environmental impact. "The primed stimulus remains in associative memory and influences behavior in a predictable manner over a certain period of time" (N. [START_REF] Evans | Green Nudging: A discussion and preliminary evaluation of nudging as an environmental policy instrument[END_REF]. For this purpose, a SMS is sent to the subjects of treated group on day before alert day i.e. day 'D-1'. This SMS is hereafter called as 'alert SMS'. The text of alert SMS for each type of alert is as follows:

-Green alert (GA) Tomorrow, from noon to 3 PM, the production of electricity from renewable energy will be maximum.

-Orange alert (OA) Tomorrow, from 6 PM to 8 PM, a consumption peak is expected with maximum use of thermal power plants.

II.3.c.ii Commitment

During the introductory meeting between the experiment organizers and subjects, the subjects filled a questionnaire. This questionnaire contains questions regarding the number of diverse appliances in the household and the use of these appliances during timeslots in a standard day. These timeslots are defined by the experiment organizers e.g., one timeslot is between 6 PM and 8 PM. The purpose of this questionnaire is to make the energy consumption of appliances evident to the subject. Following this, the subjects are given a list of actions that they can possibly implement in the case of receiving an alert. The subjects are asked to give their commitment of using (or not using) one or multiple appliances during treatment period by selecting the actions in the list. In response, the applicants committed appliances in accordance with their prospective degree of comfort during treatment period. In conjunction with alert SMS, a subject specified commitment SMS is also sent to remind each subject of treated group their default commitment on day before alert day i.e. 'D-1'.

This SMS (a green nudge of type commitment) tends the subject to remain behaviorally consistent in accordance with the commitment. However, the subjects are given a choice to modify their pre-defined commitment via their personal account on the Etude-Elec website within an hour or receiving the commitment SMS. With the help of color codes associated to their default commitment, the subjects are conveyed about which actions should be taken by default, or else they can modify their commitment for corresponding alert. The text of commitment SMS for each type of alert is as follows. For the sake of privacy, the dotted line represents the pre-defined commitment, whereas the asterisks represent the etude-elec website link.

-Green alert (GA) Tomorrow, your commitment is to use ……………………. To modify, visit ***************.

-Orange alert (OA) Tomorrow, your commitment is to NOT use …………………….

To modify, visit ***************.

In addition to the alert SMS and the commitment SMS sent to the treated group on day before alert day 'D-1', a reminder SMS is also sent to the treated group on alert day 'D'.

The text of the reminder SMS is as follows.

-Green alert (GA) Reminder: Today, green period from noon to 3 PM -Orange alert (OA) Reminder: Today, orange period from 6 PM to 8 PM

II.3.c.iii Feedback

This green nudge aims to make ecological choices more noticeable by emphasizing specific aspects of the choice architecture. For each intervention (i.e., alert day 'D'), the subjects of the treated group are given indirect feedback in graphical form. The subject specific image contains the measured load curve of the subject household on alert day 'D', which is superposed by a calculated reference load curve of the same household. The calculation of reference load curve is based on the historical consumption of the household.

The formulation of reference load curve will be explained in detail in Section IV.3. Visually, the difference of area between the reference load curve and measured load curve makes the energy flexibility effort noticeable at a glance.

For each subject of treated group, this image (as indirect feedback) is uploaded on his personal account on Etude-Elec website on day 'D+2'. The subjects of treated group is sent an 'image visualization SMS' on the following day 'D+3'. Irrespective of the alert type, the text for all the subjects of treated group is as follows. For the sake of convenience, the dotted line represents the date of the alert day, whereas asterisk represent the link of Etude-Elec website.

-Green alert (GA)

-Orange alert (OA)
You can view the consumption curve for the period of ………………., by logging on to ********.

II.3.d Architecture of Experiment

The architecture of conducting this field experiment is set-up by G2ELab 23

(Grenoble electrical engineering lab) in close coordination with GAEL lab. The architecture is conceived to fulfill the following three prerequisites of experiment.

-How to collect the data of daily measured load curve of the subjects?

-How to identify a day-ahead potential alert?

-What is the protocol of each intervention including giving feedback?

II.3.d.i Collection of data regarding measured load curve of subjects

It is essential for this field experiment that the experiment organizer must have access to the energy consumption data of all the subjects collected by LINKY smart meter.

LINKY transmits the daily measured load curve of the consumer at a resolution of 30 minutes to ENEDIS server [START_REF] Duplex | A better knowledge of electricity consumption for residential customers through the Linky smart meter[END_REF]. In line with a written consent signed by the subjects regarding access to their energy consumption data as a third party, G2ELab

receives the daily measured load curve of each household from ENEDIS in encrypted form.

An automatic script decrypts and anonymizes the data as per GDPR consent signed with each household.

II.3.d.ii Identification of a potential day ahead green alert

For the sake of comprehension, it is recalled here that the purpose of this alert is for nudging the treated group to shift their partial load from evening to the duration between noon and 3:00 PM on alert day 'D'. It is pre-defined that the green alert should only be triggered for the day when the production from renewable energy is maximum. There is no such instrument exist on national level that notifies about maximum renewable production, or at least the renewable production more than a defined threshold. Therefore, the following criteria is set for the identification of a day ahead green alert. To register a green alert, both conditions must be fulfilled.

-The average nebulosity in the afternoon of a potential alert day 'D' in Grenoble must be zero. i.e., the sky will be clear and the solar production will be maximum. For this purpose, the forecasted nebulosity by INFOCLIMAT 25 is accessed via an API a day before 'D-1' at 6 PM -Based on the forecasted (and modelled) French national load curve of residential sector, a coefficient is calculated. This coefficient is a ratio between the day ahead average energy consumption in the evening and day ahead average energy consumption in the afternoon. The forecasting model will be discussed in detail in section II.4. To trigger a green alert, this coefficient should be greater than 1. This implies that the forecasted average evening consumption is greater than forecasted average afternoon consumption. Therefore, the usual energy consumption of the evening can be shifted towards afternoon of the same day (as illustrated in Figure II.4). The coefficient is mathematically given in equation II. [START_REF] Abreu | Using pattern recognition to identify habitual behavior in residential electricity consumption[END_REF].

𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝐺𝐴 = ∑ 𝑃 𝑐𝑜𝑛𝑠𝑢𝑚𝑚𝑒𝑑 8 𝑃𝑀 6 𝑃𝑀 𝑡 1 ⁄ ∑ 𝑃 𝑐𝑜𝑛𝑠𝑢𝑚𝑚𝑒𝑑 3 𝑃𝑀 12 𝑃𝑀 𝑡 2 ⁄ Equation II.1
where 𝑡 1 and 𝑡 2 are number of hours in the evening and in the afternoon respectively.

Their values are 2 and 3 respectively.

II.3.d.iii Identification of a potential day ahead orange alert

For the sake of comprehension, it is recalled here that the purpose of this alert is to nudge the treated group for curtailing their partial load in the evening between 6 PM and 8

PM on alert day 'D'. It is pre-defined that the orange alert should only be triggered for the day when a consumption peak is expected with maximum use of thermal power plants on 25 It is worth mentioning that during the first wave of orange alert, the meteorological conditions in France were not extreme enough that RTE forecasted a congestion on the electrical network for several days. In this case, the rest of the alerts were triggered based on day ahead announcement of TEMPO 27 and/or EJP 28 . TEMPO is a mechanism of critical peak pricing introduced by EDF, in which the tariff for color-coded day 'D' varies according to the color assigned on day 'D-1'. The tariff on day 'D' is cheaper than time of use price for blue color, slightly expensive than time of use price for white color and very expensive for red color [START_REF] Albadi | Demand Response in Electricity Markets: An Overview[END_REF]. EJP is another instrument of critical peak pricing, in which the peak consumption day is color-coded in red and the subscribers pay a low tariff all year round, except for the 21 red days with higher tariff [START_REF] Bivas | La production d'effacement : comment offrir des économies d'électricité à des millions de foyers[END_REF]. However, the triggering of green alert is based on two criteria. One of the criteria of green alert is based on the prediction of day ahead residential energy consumption, which is discussed below.

II.4 Prediction of modelled national load curve of residential sector

The criteria for identifying a potential green alert is discussed in the section II.3.d.ii.

For the sake of convenience, it is recalled here that one of the criteria is that the value of coefficientGA (coefficient of green alert, see equation II.1) should be greater than a threshold value. In this case, the threshold value is 1. The coefficientGA is a ratio between the average energy consumption in the evening and the average energy consumption in the afternoon.

Since the calculation of coefficientGA is based on the day ahead energy consumption (which cannot be measured on day before), therefore it requires forecasting of the day ahead energy consumption.

API for real time prediction of day ahead energy consumption is not available by the French electrical grid operators or a third party. Therefore, a supervised learning model is developed for day ahead prediction of energy consumption. RTE provides the historical gross daily energy consumption 29 data at national level. This data represents the accumulated electricity consumption for all the sectors (residential, industrial, etc.). This might be effective in case of sending a general signal to the consumers of all the energy consumption sectors, however it is more significant if customized signals are sent to the consumers of respective consumption sector, i.e. in our case residential sector.

For this purpose, the historical data of modelled load curve of French residential sector 30 is used. To refine the prediction, the historical data of smoothened national [START_REF] Chen | Big Data Deep Learning: Challenges and Perspectives[END_REF] French daily energy consumption : https://opendata.reseaux-energies.fr/explore/dataset/consommation-quotidiennebrute/api/?sort=-date_heure 30 Modelled load curve of French residential sector: https://data.enedis.fr/explore/dataset/coefficients-desprofils/information/?disjunctive.sous_profil temperature of France 31 is also used. This data is used by ENEDIS and is also accessible on their open data platform via an API. In addition to this, the forecasted temperature of 32 French cities is also accessed via an API of Climacell (rebranded as tomorrow.io 32 ). The detail of these APIs and their corresponding data is discussed in detail in section II. The results of different techniques are studied for the purpose of prediction.

Normally, LSTM prediction gives good results however, it needs a lot of computation, which is not necessarily required for this study. Random Forest regression gives good results as well and needs less computational time than LSTM, Therefore, for the purpose of forecasting the French national load curve of residential sector, random forest regression model is used. The following section presents the working principal of random forest.

II.4.a Random Forest Regression

II.4.b Regression Model Creation

A supervised regression model has three phases. The first phase is related to the training of a model. For this phase, it is necessary to have sufficient historical data. In case of multiple input variables, the data of all the quantities is needed. Following this, the choice of a suitable regression technique is important. A target variable is also defined in this phase, of whom the prediction will be performed. In the training phase, the historical data of (one or multiple) quantities is used as input (mathematically denoted as 𝑋 𝑡𝑟𝑎𝑖𝑛 ), whereas the target is defined as output (mathematically denoted as 𝑌 𝑡𝑟𝑎𝑖𝑛 ). Using the chosen technique, the model learns that each 𝑥 input leads to its respective 𝑦 output (where 𝑥 is the instantaneous input and 𝑦 is the instantaneous output).

The second phase is related to validation of model. Usually, a fraction of training data is kept for validation. Since the data is in vectorial form, therefore rather than having a simple arithmetic difference, a validation indicator is chosen in this phase. For the given input data 𝑋 𝑣𝑎𝑙 , the model performs a prediction 𝑌 ̂. The difference of prediction 𝑌 ̂ with the target 𝑌 𝑣𝑎𝑙 according to the chosen indicator gives us a measure of how well the model is performing. Certain measures can then be taken to improve the model if the model seems to be not working up to the expected standards. The third phase is related to the real time prediction, where a validated model is used to do the prediction 𝑌 ̂ for given input data 𝑋. Assuming these curves as the modelled load curve of residential sector, the dynamic coefficient curve is selected for the forecasting of French national load curve of residential sector.

II.4.c.ii Temperature

This dataset corresponds to the national temperatures of France. Both types of temperature given below is used to calculate a coefficient of proportionality, which is called temperature gradient. It refers to the variation in consumption with respect to variation in 1 °C of temperature. However, it needs to consider the inertia of consumption in relation to temperature variations. For this purpose, the temperature is smoothened to take in to account the thermal inertia of buildings. The purpose of this is to make a hypothetical relation between the ambient temperature and the internal temperature of buildings (without knowing the internal temperature).

II.4.d Regression Model Parameters

II.4.d.iii Temporal Parameters

The following temporal parameters are added in the training dataset with one hot encoding. -'Is_weekday': A Boolean column is added as parameter is_weekday. The value remains 0 if weekday, else 1 for weekend.

-'Is_holiday': A Boolean column is added as parameter is_holiday. The value remains 1 if the day is a French holiday, else it remains 0.

II.4.e Model Evaluation and relative importance of features

II.4.g Conclusion on the prediction of French residential load curve

A demand side management signal to the residential consumer based on the forecasted grid consumption retains the effect of all energy consumptions sectors.

Therefore, it is more significant to send energy flexibility signals to the residential customers solely based on the forecasted condition of residential consumption. One way of doing this is by making a forecast on national load curve of residential sector.

Random forest regression is used to do prediction of modelled French national load curve of residential sector. The analysis of feature importance depicts that the historical value of French national load curve of residential sector is most important, followed by the temperature parameters and temporal parameters. The performance metric suggests that the addition of lagged features related to French national load curve of residential sector and national temperature improves the performance of the model. This prediction serves to calculate the 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝐺𝐴 (green alert coefficient) to identify a day ahead potential alert for load shifting. With a forecasted clear sky and 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝐺𝐴 greater than 1, the treated group is nudged to shift their energy consumption from evening to afternoon on next day.

II.5 Conclusion

III.1 Introduction

In the previous chapter, the architecture of the experiment representing nudge based indirect energy flexibility in the households is presented. The purpose of this experiment is to study the impact of green alerts for load shifting and the impact of orange alerts for load curtailment. It is recalled here that the purpose of green alerts is to increase the energy consumption during the hours of elevated renewable energy production in the afternoon of summer season, whereas the purpose of orange alerts is to reduce the peak energy consumption during the evening in winter season. This chapter deals with analyzing the characteristics of subjects during alert days and non-alert days. Additionally, it also deals with measuring the impact of nudge signals.

III.2 General Statistics of the alerts

There were 175 households that participated in the study during the first session of orange alert and green alert. These households were categorized into control group and treated group based on strictly possible equivalence. Before the commencement of second session of experiment, the subjects were asked to re-sign a consent for continuing their participation in the experiment. Certain subjects left the experiment before the commencement of second session while some other left the experiment amidst the second session. This is owing to their personal choice, mal communication of smart meter or the fact that the households were relocated. The number of households in each group for each session is given in Table III.1. Two sessions of green alerts as well as two sessions of orange alerts were conducted during a period of 2 years. The experiment was conducted between November 2019 and September 2021. The duration of each session and the number of alerts triggered during each session is given in Table III.2. For the sake of comprehension, the following terminologies will be used hereafter:

-Green (or

Orange):

It represents the type of alerts for load shifting (or load curtailment) respectively.

-Session X of green alerts (or orange alerts):

The respective session of green alerts or orange alerts. Here the value of X is either 1 or 2.

-Non-alert days of session X:

The days for which no alert was in place during a session X.

These days represent usual behavior of energy consumption of households. The energy consumption during the period of interest is significant to measure the impact of nudges on alert days.

-Alert Days of session X:

The days for which an alert was in place during a session X.

These are the days when the households are expected to implement energy flexibility during period of intervention.

-

Period of interest (or intervention)

The period of a day (in hours) for which an energy flexibility is expected to be implemented by the households of treated group on alert days. This period is between noon and 3 PM for green alerts and between 6 PM and 8 PM for orange alerts.

To distinguish it for non-alert days and alert days, this period will be hereafter called as period of interest for non-alert days and period of intervention for alert days. No.

Alert

III.2.a Green Alerts

The first session of green alerts was originally planned from April 2020 to September

III.2.b Orange Alerts

The first orange alert was triggered based on the forecasted negative temperature of Grenoble. This served as a test to find out whether the designed mechanism of the experiment works. Following this, the protocol of sending alerts based on the PP1/PP2 notifications was adapted. As the winter was not cold enough to bring network congestion on the national grid, it is deduced that there might be no more PP1/PP2 notifications.

Therefore, EJP and TEMPO notifications are added as secondary protocol. EDF split the territory of France in 4 zones to notify about the status of day ahead EJP. For triggering orange alerts, the notifications of EJP for south zone is used.

Contrary to the 1 st session of orange alert, the winter season during the second session of orange alert was colder and RTE notified 19 PP1/PP2 days. It is defined in the protocol that two consecutive alerts should not be sent for two consecutive days. Therefore, only 12 PP1/PP2 days were considered during 2 nd session of orange alert. Since the number of alert days were enough, therefore no secondary protocol was adapted. Table III.4

demonstrates the basis of each triggered orange alert for both sessions.

III.3 Comparison of statistical variables of control group and treated group

Before discussing the impact of nudge signals for energy flexibility, it is important to assess the resemblance between the groups. Though the groups are formed with respect to strictest possible equivalence, it is pertinent to compare various statistical variables of both groups. The information of households regarding the statistical variables was gathered through the questionnaire filled by the subjects. If statistically significant difference is found between the groups for a number of statistical variables, the later results about the impact of nudge signals will be affected and may not be statistically validated.

The purpose of this section is to compare the similarity (or dissimilarity) of certain statistical variables between two groups. The variables that are analyzed in this section is related to the number of occupants (and appliances) in the households, the presence of occupants in the household and the characteristics of the building. For the sake of convenience, the variables are converted into their corresponding fraction in percentage.

The variables and their percentage share are given in annex B.

A Chi-square test 35 (also known as 'goodness of fit' test) is performed on the statistical variables between the control group and the treated group. It is used to analyze categorical data and to check the homogeneity of two groups. It measures that if the groups are independent, how well the observed distribution of data fits with the expected distribution. A quality of Chi-square test is that it does not require equality of variances among the study groups or homoscedasticity in the data [START_REF] Mchugh | The Chi-square test of independence[END_REF]. Two hypotheses are formulated i.e., the null hypothesis (H0) and the alternative hypothesis (H1). These hypotheses are given below.

-H0: The groups are similar/identical.

-H1: The groups are significantly different.

The retention of null hypothesis (H0) requires that the p-value in the result of Chi-square test should be greater than the value of significance level (denoted by α). The value of α is fixed at 0.05, which means that the test has a confidence level of 95% i.e., we are 95% confident that the observed results are real and are not an error caused by randomness. The results of Chi-square test for various variables are tabulated in Table III.5. The p-value of Chi-square test for each of these variables is greater than 0.05. The case is contrary for the variable "year of construction". Therefore, the null hypothesis is retained for all the variables in Table III.5 except for the variable "year of construction".

The information regarding this variable is collected for the purpose of using thermal loss of the dwellings in the study. However, since the experiment did not consider the modes of space heating, therefore this variable is not important in the study.

It should be noted in Table III.5 that for some variables, an asterisk is placed in the fourth column, next to 'Yes'. These asterisks represent a warning during the execution of test, indicating that the approximation of Chi-square test of independence may not be aptly correct. For a chi-square test, it is assumed that the values in the 'expected' column should be equal to or more than 5 in at least 80% of the cells, and no cell in the 'expected' column should have value less than 1 [START_REF] Bewick | Statistics review 8: Qualitative data -tests of association[END_REF][START_REF] Mchugh | The Chi-square test of independence[END_REF]. The warning might be raised due to non-fulfillment of assumption for respective variables. Though this makes estimation with a warning, yet it gives a result regarding the similarity in the groups.

The social comparison of both groups depicts that there is no statistically significant difference between the groups for most of the socio-technical statistical variables. In the results, there lies a limitation for certain statistical variables due to the non-fulfillment of basic assumption, yet the end results with respect to the data of both groups depicts that there is no statistically significant difference. Since the null hypothesis is retained for most of the variables, therefore it can be concluded that the two groups are statistically similar for multiple social characteristics.

III.4 Comparison of normal energy consumption between two groups

It has been observed in the previous section that the control group and treated group are found identical for several statistical variables. Yet, it is still to be studied that whether the energy consumption of treated groups is like the control group, and if so, then to what extent. The normal (or non-alert days) energy consumption in this case refers to the energy consumption on the weekdays when no energy flexibility is activated. If the normal energy consumption looks similar (or have negligible difference), it validates that the control group can be used as a standard against which the effect of treatment on treated group can be quantified.

For this purpose, meta-analyses is studied between the groups. A meta-analysis is a statistical analysis of a number of analysis results in order to integrate their findings (Crits-Christoph, 1992; [START_REF] Glass | Primary, Secondary, and Meta-Analysis of Research[END_REF]. Its purpose is to address a research question as definitely as possible through combining findings of multiple studies (TARG Bristol, 2017). The metaanalysis of energy consumption for non-alert days is discussed in this section with the methodology given below.

-The consumption data is split for each session as follows.

o To analyze the normal consumption, the measured load curve of non-alert days of each session is taken. The number of non-alert days are given in Table III.2.

-The meta-analysis is performed on the measured load curve of period of interest.

The reason being that we are interested in knowing the impact of the nudge signals during period of intervention on alert day. It is recalled here that:

o For green alerts, this period is between noon and 3 PM.

o For orange alerts, this period is between 6 PM and 8 PM.

-The measured load curve data is originally sampled at 30 minutes. The data is analyzed in the form of average energy consumption per hour (during the period of interest). i.e.

o The average energy consumption per hour is mathematically defined as:

𝐸 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 = ∑ 𝑃 𝑖 𝑛 𝑡=𝑖 𝑛 2 ⁄ Equation III.1
Where 𝑃 𝑖 is the instantaneous power at instant 'i'.

o For green alerts, the number of measurements between noon and 3 PM are o For all the analyses given below, it is found that there is no difference between the results if we either take the energy consumption of the whole period of interest or take average energy consumption per hour during the period of interest. Since it is common practice to present energy consumed per hour, therefore, the average energy consumed per hour is taken as unit.

Besides, it is also helpful to compare the average energy consumption per hour in the evening to that of average energy consumption per hour in the afternoon.

-The analysis is performed using the following three statistical tools.

-Kernel Density Estimation:

To visually compare the statistically distribution of energy consumption of both groups.

-Mann Whitney U Test:

To observe whether the data of both groups is statistically similar or different.

III.4.a Visualization of statistical distribution on normal days

This analysis gives a global picture of the energy consumption during the period of interest on non alert days. The kernel density estimation 36 (shortened as KDE) is used to visualize the statistical distribution. For non-parametric data, KDE is an estimate of probability density function of a random variable (i.e. energy consumption in this case).

Based on a finite number of data, KDE attempts to infer characteristics of a population of data. In our case, the comparison is done by the superposition of KDE distribution of treated group on the KDE distribution of control group.

Figure III.2 illustrates it for the non-alert days of green session 1 and green session 2 respectively. The peak density of energy consumption for both groups is found to be less than 0.5 kWh. It suggests that it is highly probable that the energy consumption of a household remains less than 0.5 kWh. The peak density of control group is higher than the peak density of treated group during 1 st session of green alerts. It might be caused due to readjustment towards normal life during post-confinement, when the subjects had to adjust their work hours at their workplace and in their home. However, comparing to the 1 st session, the peak density of both groups is within proximity in 2 nd session.

Figure III.2 The distribution of non-alert days of green sessions

The distribution of control group is overlapped by the distribution of treated group by 88.2% and 92.5% for green session 1 and green session 2 respectively. The difference of the mean values between both groups is minimal, whereas the coefficient of variation of both groups is also closer. Therefore, it can be concluded that the two groups are similar in terms of energy consumption during non-alert days of green sessions. The coefficient of variation is a ratio of standard deviation and mean value and it gives a measure of dispersion of data relative to the mean [START_REF] Lovie | Coefficient of Variation[END_REF]. The third column entitled 'ratio' in the graphs below represents the values calculated as follows;

ROW, COLUMN FORMULA MEAN, RATIO 𝜇 𝐶 𝜇 𝑇

SD (STANDARD DEVIATION), RATIO б 𝐶 б 𝑇

CV (

Б 𝝁

), RATIO CV 𝐶 CV 𝑇

Where 𝜇 is the mean value, б is the standard deviation, CV is the coefficient of variation and the subscripts C and T represents control group and treated group respectively.

The distribution of non-alert days of orange sessions using KDE is illustrated in -Null hypothesis (H0): The distribution (of energy consumption data) of control group is similar to the distribution (of energy consumption data) of treated group.

-Alternative hypothesis (H1): The distribution (of energy consumption data) of control group is significantly different than the distribution (of energy consumption data) of treated group.

The rejection of null hypothesis (H0) represents the presence of statistically significant difference between the distributions of both groups. Since no intervention is made on nonalert days, therefore our interest is to find out whether the null hypothesis is retained for the energy consumption data of non-alert days. Statistically, it can be observed if the p-value of two-sided Mann-Whitney U Test is greater than α = 0.05. The results of two-sided Mann-Whitney U Test is globally given below for green and orange sessions in Table III.6. Since the p-value for both types of sessions are greater than α, therefore it can be concluded that there does not exist any statistically significant difference in the energy consumption of both groups for non-alert days. In other words, the energy consumption of both groups is found similar for non-alert days. where Obs = number of observations, µ = Mean value of observations, б = standard deviation of observations.

III.5 Analysis of the impact of nudge signals

The visual analysis and statistical analysis in section III.4 concludes that the energy consumption of both groups during the period of interest on non-alert days is similar. It is now pertinent to perform the analysis of impact of nudge signals for the alert days. Firstly, a visual analysis of the energy consumption during the period of intervention on alert days is presented. It tells us whether the distribution curve of treated group is displaced with respect to the distribution curve of control group. Secondly, a statistical analysis is presented to find out whether the distribution underlying treated group is statistically less than (or greater than) the distribution underlying treated group. And finally, a linear regression is presented to find out the impact of each nudge signal in terms of percentage variation.

III.5.a Visualization of statistical distribution on alert days

This analysis presents a global comparison of energy consumption during period of intervention on alert days with respect to the energy consumption during the same period on non-alert days.

III.5.b Assessment of statistical difference in energy consumption on alert days

For this purpose, one-sided Mann-Whitney U Test is used with the following hypotheses.

-Null hypothesis (H0): The distribution (of energy consumption data) of control group is statistically greater than the distribution (of energy consumption data) of treated group.

-Alternative hypothesis (H1): The distribution (of energy consumption data) of control group is statistically less than the distribution (of energy consumption data) of treated group.

Since interventions are made for green alert days, therefore our interest is to find out whether the null hypothesis is rejected for the energy consumption data of alert days.

Statistically, it can be observed if the p-value of one-sided Mann-Whitney U Test is less than α = 0.05, which validates our alternative hypothesis. The results for each alert of green sessions are tabulated above in Table III.7. Despite having negative difference of mean value of control group and treated group for most of green alerts, it is observed that the null hypothesis is only rejected for 5th green alert.

However, the aggregated result of all 6 green alerts rejects null hypothesis i.e., the distribution (of energy consumption data) of control group is statistically less than the distribution (of energy consumption data) of treated group. It means that for all the green alerts globally, the treated group implemented load shifting for energy flexibility between noon and 3 PM. Yet, it seems a weak conclusion; considering that the retention of null hypothesis for 5 out of 6 green alerts is not supportive for aggregated results.

III.5.c Quantification of change in the energy consumption during the period of intervention

It is now significant to quantify the change in the energy consumption during period of intervention on alert days. For this purpose, ordinary least square (OLS) regression test 38 is performed. The least square model is created by estimating the parameters in a regression model by minimizing the sum of the squared residuals. It can be applied to single or multiple explanatory variables and also categorical explanatory variables [START_REF] Hutcheson | The Multivariate Social Scientist: Introductory Statistics Using Generalized Linear Models[END_REF]. For analysis, it is used to quantify the variation in energy consumption for all the alerts globally as well as for individual alerts. The variables for the test and origin of their data are tabulated in Table III.9. It is recalled here that the original set of nudges consists of 3 types of SMS. However, we are only taking the alert SMS for analysis with the assumption that all the other SMS are also well received by treated group. Certain subjects of treated group did not receive alert SMS (owing to mal functioning of 3 rd party dispatch server), therefore these subjects are considered as part of control group for respective alert. The global as well as individual impact of nudge signals for each alert is discussed in this section. The following general interpretations are helpful for understanding the change in the energy consumption. These interpretations are given with respect to the data type of each independent variable. The value of coefficient coef in the OLS regression gives us change in the energy consumption.

-The positive sign of coefficient coef indicates that the dependent variable Y increases with respect to the corresponding independent variable X. Similarly, a negative sign of coefficient coef indicates that the dependent variable Y decreased with respect to the corresponding independent variable X.

-If the dependent variable Y is log-transformed and independent variable X is in its original datatype, then:

o The value of coefficient coef is expontiated i.e. b = exp(coef).
o Then it is transformed into percentage using 𝑎 = (𝑏 -1) * 100.

o The results are interpreted as; the dependent variable Y will be changed by 'a' % for one unit rise in independent variable X.

-If both dependent variable Y and independent variable X are log-transformed, then the dependent variable Y will be changed by coef % with respect to 1% change in the independent variable X. within a confidence interval of (0.9%, 21.4%) overall. This change in energy consumption is attributed to the nudge signal by 11.49% for 1 st green session, whereas it is 7.09% for the 2 nd green session. However, in the case of second green session, the results only take in to account one green alert, therefore due to less data, the results might not be statistically valid despite having a R 2 value of 0.707. The impact of exogenous variables like daily energy consumption and temperature is also found statistically significant during the period of intervention.

Figure III.8 The statistical results of OLS regression for green sessions

We also analyze the change in the energy consumption of treated group during the period of intervention on green alert days versus the same period in previous non-alert days of the same session. Table III.10 gives us the results for both green sessions, where the change in energy consumption can be observed in the column 'Difference on alert day'. e.g.

the energy consumption of treated group between noon and 3 PM on the 1 st alert day increased by 9.7% as compared to the previous non-alert weekdays. And it is statistically significant within the range (0.01, 0.05] of the significant code 39 . However, the results are inverse for alert 3 and alert 5. By looking at the values in Table III.10 and Figure III.9, it can be deduced that no evidence of habit formation is found for load shifting against the green alerts. Overall, a decrease of 18.21% in the energy consumption of treated group is observed by the virtue of nudge signal within the confidence interval of (-25.8%, -10.6%). This change in energy consumption is attributed to the nudge signal by 22.78% for 1 st orange session, whereas it is 14.68% for the 2 nd orange session. The impact of exogenous variables like daily energy consumption is found statistically significant, yet the impact of temperature is found minimal.

A study of similar nature is done in the Monash residence halls in Australia [START_REF] Jorgensen | Reducing Peak Energy Demand among Residents Who Are Not Billed for Their Electricity Consumption: Experimental Evaluation of Behaviour Change Interventions in a University Setting[END_REF], in which the load curtailment is found in the range of 12-20% in response to behavioral science oriented treatment. The result of our study is closer to the study done in Australia, yet the protocol is different.

III.6 Conclusion

Statistical analysis is a mean of measuring the impact of a treatment given to one group against a standard group. By using a statistical method, it is observed that there is no statistically significant difference between both groups with respect to most of the sociotechnical statistical variables. The visual comparison of energy consumption of treated group with control group on non-alert days shows that the energy consumption is quite closer. It is also found that there does not exist any statistically significant difference between the energy consumption of control group with treated group. The similitude of energy consumption of both groups during non-alert days suggests that the impact of the nudge signal on alert days can be visualized and quantified by observing a difference in energy consumption.

Similar to the non-alert days, the comparison of energy consumption of control group and treated group is also done for alert days. The visual analysis shows that the distribution curve of treated group is displaced with respect to control group for alert days.

This gives a mere conclusion that the nudge signals do have impact on the energy consumption during the period of intervention on alert days. In addition to this, the impact for each alert day is also measured to find out habit formation. The statistical test does not conform that all green alerts performed well, however it suggests that all the orange alerts does perform well.

Finally, it is observed that the treated group implemented load shifting of energy flexibility by 11.17% against the green alert SMS as compared to control group. Similarly, a load curtailment of energy flexibility by 18.21% is observed against the orange alert SMS.

Yet, by looking the energy consumption of treated group for each alert day with respect to previous non-alert weekdays, no significant (i.e., either horizontal or linear) pattern is observed regarding habit formation. In a nutshell, an impact of nudge signals is found and quantified for treated group as compared to control group. Yet no evidence of habit formation is found for implementation of energy flexibility by treated group.

IV.1 Introduction

The visualization of measured load curve in comparison to a baseline load curve serves as an easy and quick to understand feedback at a glance. "People can interpret various kinds of charts and tabular presentations if they are well designed" [START_REF] Karjalainen | Consumer preferences for feedback on household electricity consumption[END_REF]. A residential consumer can himself analyze the impact of its effort conveniently before the next alert signal. In this case, the indirect feedback itself serves as a nudge for motivating the residential consumer. This baseline load curve can serve as a reference for measuring the impact on alert days. Hence, hereafter it will be called as reference load curve.

Here, it poses a scientific question as how to formulate a reference load curve that neither extremely underestimates or extremely over-estimates the effort of energy flexibility on alert day 'D'.

To explore the answer to this scientific question, multiple techniques are analyzed to formulate a reference load curve. These techniques include both naïve methods and some machine learning methods. By looking at the meta-analysis in chapter 3, it can be observed that a control group is inevitable to measure the impact of treatment. Therefore, we will try to answer another scientific question as whether the reference load curve can be used as an alternative to control group. Before defining the characteristics of a reference load curve, it is interesting to look at the conventional forms of indirect feedback. In this regard, a literature review of indirect feedback is given below.

IV.2 Literature review of Indirect Feedback

An indirect feedback is given to the customer by the energy utility after processing the raw data of energy consumption for a specific period of time; The purpose of indirect feedback is that the customer learns energy flexibility via reading and reflecting upon habitual energy usage [START_REF] Darby | Making it Obvious: Designing Feedback into Energy Consumption[END_REF]. Despite the fact that the literature is heavily focused on direct feedback through either IHDs (Intelligent home devices) or web services, very few literature is available that talks about the mode of presentation of indirect feedback. Mostly, the indirect feedback is found in the form of frequent bills with either historic comparison (comparing the energy consumption with the past consumption of the same household), or normative comparison (comparing household energy consumption with that of a group of similar households) of energy consumed in kWh.

During the last decade of twentieth century, the indirect feedback was in the form of sending frequent billing with comparative or suggestive information. This includes more frequent energy bills with historical feedback [START_REF] Wilhite | Measured energy savings from a more informative energy bill[END_REF], with normative feedback, with disaggregated feedback or with detailed annually or quarterly energy reports [START_REF] Wilhite | Advances in the use of consumption feedback information in energy billing: the experiences of a Norwegian energy utility[END_REF]. Another form of indirect feedback is also tested by sending SMS and emails to the subjects in an experiment of Danish electricity supplier SYD ENERGI [START_REF] Gleerup | The Effect of Feedback by Text Message (SMS) and Email on Household Electricity Consumption: Experimental Evidence[END_REF]. The SMS and emails are sent to the groups on daily basis, weekly basis or monthly basis. The purpose of the feedback is to inform the subject about deviation in energy consumption from their historical consumption level.

The famous study with home energy reports (HER) also used indirect feedback with historical comparison, normative comparison and injunctive norm (see section II.2.c). A study is also done to find out the consumer preference for feedback on household electricity consumption. It is observed in the study that many people find it difficult to understand the difference between power (W) and energy (kWh). Since the electricity bills are for energy consumption, therefore numerical feedback in the unit of W is not easily comprehensible for a non-professional. The study presents a variety of informational feedbacks and the consumers are found interested in the presentations of costs (over a period of time), appliance-specific consumption breakdown and historical comparison [START_REF] Karjalainen | Consumer preferences for feedback on household electricity consumption[END_REF].

However, the diverse informational feedback causes the problem of information overload for the consumer. This makes it difficult for the consumer to understand the essence of feedback and to make decision accordingly for the future period of interventions.

In a nut shell, the state of the art suggests that the indirect feedback is either historic or normative (F. Wang et al., 2018). It is normally presented in numerical form e.g. kWh, cost, CO2 emissions [START_REF] Fischer | Feedback on household electricity consumption: a tool for saving energy?[END_REF], in the form of clustered baseline load curve of identical households (Abreu, Câmara Pereira, and Ferrão, 2012) or as average daily load curve [START_REF] Ozawa | Determining the relationship between a household's lifestyle and its electricity consumption in Japan by analyzing measured electric load profiles[END_REF]. Following the idea of average daily load curve, some other naïve methods and machine learning models are statistically analyzed to formulate reference load curve of residential consumer.

IV.3 Formulation of Reference load curve

A reference load curve is a prediction of energy consumption for each instant on a non-alert day. Supposedly, a reference load curve serves to represent the usual behavior of energy consumption of a household on a non-alert day. Then the difference of area between the reference load curve and flexibility activated measured load curve gives a measure of energy flexibility on alert day. Besides, it also serves to graphically visualize the measure of energy flexibility, therefore serves as an easy to comprehend tool for indirect feedback of energy flexibility.

IV.3.a Key Performance Indicators

Before discussing the techniques and their efficiency analysis for formulating reference load curve, it is necessary to define key performance indicators (KPIs). In this case, two indicators will be used i.e. energy difference indicator and coefficient of determination (also known as R 2 score). The energy difference indicator will later help us in understanding why alert type specific methods are defined in some technique. These KPIs are briefly discussed below.

IV.3.a.i Energy Difference Indicator

An energy difference indicator is the difference of reference load curve and measured load curve during the period of interest or intervention. Mathematically, it is given as follows:

𝑬𝒏𝒆𝒓𝒈𝒚 𝑫𝒊𝒇𝒇𝒆𝒓𝒆𝒏𝒄𝒆 𝑰𝒏𝒅𝒊𝒄𝒂𝒕𝒐𝒓| 𝒕 𝟏 𝒕 𝟐 = ∑ 𝑹𝒆𝒇𝒆𝒓𝒆𝒏𝒄𝒆 𝑳𝒐𝒂𝒅 𝑪𝒖𝒓𝒗𝒆 -∑ 𝑴𝒆𝒂𝒔𝒖𝒓𝒆𝒅 𝑳𝒐𝒂𝒅 𝑪𝒖𝒓𝒗𝒆 𝒕 𝟐 𝒕 𝟏 𝒕 𝟐 𝒕 𝟏 Equation IV.1
where, -t1: The starting time of period of interest/intervention. For type green, it is noon, whereas for type orange, it is 6 PM.

-t2: The ending time of period of interest/intervention. For type green, it is 3 PM, whereas for type orange, it is 8 PM.

In an ideal case, the energy difference indicator is zero for non-alert days. This is under the assumption that the reference load curve exactly predicts the (usual behavior of) energy consumption. For favorable feedback, we expect that the reference load curve should serve as nudge and its difference with measured load curve should be zero on non-alert days.

However, it is not guaranteed that the prediction exactly estimates the measured load curve on non-alert days. The reference load curve either under-estimate or over-estimate the energy consumption on non-alert days practically. Table IV.1 illustrates the ideal and practical cases graphically. The energy difference is highlighted in blue color between the two curves. It should be noted that the underestimation (or overestimation) of reference load curve is illustrated with respect to the measured load curve. Our interest in the case of green alert is to measure load shifting on alert days, therefore a practical reference load curve that minimally underestimates the measured load curve on non-alert days is suitable in this case. The value of energy difference indicator will be negative in this case. Alternatively, if a reference load curve that overestimates the measured load curve on non-alert days is selected to measure load shifting, it brings error in the calculation of energy flexibility for alert day.

Contrary to this, load curtailment is to be measured in response to orange alerts, therefore a practical reference load curve that minimally overestimates the measured load curve on non-alert days is suitable in this case. A reference load curve that underestimates measured load curve is not suitable in this case as it brings error in the calculation of energy flexibility for alert day. This problem is further discussed in section IV.4 regarding removal of error to measure energy flexibility on alert day.

In a nutshell, practically, a reference load curve that minimally underestimates the measured load curve on non-alert days is suitable to measure load shifting on green alert days. Whereas, a reference load curve that minimally overestimates the measured load curve on non-alert days is suitable to measure load curtailment on orange alert days. We will use this notion in section IV.

IV.3.b Techniques studied to formulate reference load curve

Multiple techniques are studied in this regard to formulate a reference load curve.

None of the technique requires non-supervised modelling, rather they are based on historical data of energy consumption. The techniques are explained in detail in their relevant subsection below, whereas Table IV.2 briefly presents the reason for choice of techniques. 41 The R 2 score is calculated for each day in the data using the 'r2_score' method in the 'metrics' class of scikit-learn python package.

consumption behavior (e.g. with respect to for example temperature).

Machine learning methods 05

Random Forest (Point to Point) These methods are studies to find the impact of two architectural schemes of prediction.

06

Random Forest (Sequence to Sequence)

The following techniques are studied for the formulation of reference load curve.

Among these techniques, two techniques have different statistical variables with respect to green alert and orange alert. Therefore, these techniques will be presented with respect to the type of alerts. It should be noted that the forthcoming analysis is done for the sole purpose of finding a suitable reference load curve that has near zero difference to the measured load curve on non-alert days.

IV.3.b.i The high-density value of Kernel Density Estimation of same timeslot

For an instance 'i', kernel density estimation is plotted based on the recorded consumption of previous 'n' weekdays. The purpose of doing this is to find out the most probable value of the same timeslot to put in reference load curve. In the distribution curve plotted through kernel density estimation, the consumption value with highest peak (i.e. density) is selected as the reference value of the instance 'i' in the reference load curve.

IV.3.b.ii Measured load curve of minimum (or maximum) energy consumption as reference load curve

This technique is type specific; therefore, we first discuss type green and later discuss type orange. As it is aforementioned that a practically suitable green reference load curve should have near zero yet negative error with measured load curve on non-alert day, therefore owing to this logic, the measured load curve having minimum energy consumption between past 5 weekdays is taken as reference load curve. The choice of number of days can vary. In this case, the objective is to investigate the historical energy consumption during previous week, therefore 5 previous weekdays are chosen. Where;

-Min() represents a function that gives the minimum value of a sample as an output.

-𝐸 𝐷-1 is the energy consumption during period of interest on day 'D-1'. Similarly, 𝐸 𝐷-5 is the energy consumption during period of interest on day 'D-5'.

i → n shows the range between 12:30 PM and 3:00 PM with half hourly samples.

-𝑃 𝐷-1,𝑖 represents the power consumption at instantaneous value 'i' on day 'D-1'. Similar for others days from 'D-2' to 'D-5'.

IV.3.b.iv Random Forest Point to Point

In this technique, random forest is used to predict each instance (of period of interest) with respect to the rolling historical values of previous 5 weekdays of the same instance. The training of the model is done using all the historical data of same session.

IV.3.b.v Random Forest Sequence to Sequence

This technique is like the 'Random Forest Point to Point' technique presented in previous section. The only difference is that the prediction of instance is replaced by prediction of a sequence. i.e. rather than predicting each instance and then formulating reference load curve, a sequence is predicted using the sequences of previous 5 weekdays.

IV.3.c Analysis for the suitability of a technique to formulate the reference load curve

In this section, we analyze the suitability of the techniques with respect to both key performance indicators. The reference load curves are calculated using the above techniques with the following characteristics.

-For analysis, the data of measured load curve for 175 households is used.

-Since the original data is sampled at 30 minutes, so we used the original sampling for analysis.

-The reference load curve is calculated for all the weekdays in both sessions of type green and type orange. The data of alert days is omitted to only analyze the non-alert days.

-Only the data corresponding to the duration of sessions is taken for analysis. The only exception is in the case of green session where the last date of data is 2 nd July, 2021 instead of 30 th September, 2021. It is because the fact that the analysis is done multiple times and it was lastly revisited in the month of July, 2021. No change in the results was observed during the multiple rounds of analysis.

-For each technique, the distribution of the energy difference indicator is presented in the form of box plot. The outliers are not presented with the boxplots. The reason for doing this is that the boxplots are strongly pinched by the outliers around OX axis on the graph. Thus, the boxplots become very small and could not be easily analyzed with naked eye.

-For each subject and each day, R 2 is calculated between measured load curve and reference load curve. The R 2 is only calculated for the period of interest. For each technique, the distribution of R 2 is presented in the form of box plot. Similar to the above case, the outliers are not presented with the boxplots.

IV.3.c.i Analysis of green reference load curves

The distribution of energy difference indicator for green reference load curves can be observed in the Figure IV.13. Recalling the characteristics of a suitable green load curve, it is expected from a green reference load curve that the difference should be negative yet arbitrarily closer to zero. In the term of a boxplot, these characteristics can be translated in such a way that:

-The boxplot is less dispersed around zero. It suggests that the reference load curve is closer to the measured load curve.

-Ideally, the boxplot should be beneath zero. Practically, the distribution of most of the data should be negative.

By observing the boxplots in Figure IV.13, it can be observed that the random forest techniques are not suitable as green reference load curve. The reason is that more than 50% data distribution of the boxplot is positive, suggesting that the predicted value of reference load curve is mostly higher than the measured load curve. Besides, they are widely dispersed as compared to the green reference load curve of other three methods. By comparing 2 nd and 3 rd boxplots, it is observed that the 3 rd boxplot is less dispersed as compared to 2 nd boxplot. Therefore according to the energy difference indicator, the 1 st and 3 rd method are suitable to formulate the reference load curve. 2 nd method. Among all the methods, the KDE methods performed better than others. Yet, it is easier to explain the underlying mechanism of 3 rd method to our subjects as compared to KDE method. Therefore, in our experiment we chose the 3 rd method albeit that the KDE method seems more promising for the formulation of a reference load curve to measure the load shifting of energy flexibility. score distribution is of 3 rd method, which is in the range of [-70,1]. On the other hand, the random forest sequence to sequence method is found to be better than the random forest point to point method according to R 2 score distribution. It is evident that the random forest sequence to sequence method of formulating reference load curve outperforms other methods to measure the load curtailment of energy flexibility. Yet, since the machine learning methods are explored at a later stage during experiment, so the 3 rd method was chosen to formulate the reference load curve for the reasons that:

-It has the best results among the first 3 methods.

-It is easy to explain to the subjects.

-It is symmetric to the method chosen for the case of green alerts.

IV.4 Reference load curve in the form of feedback

In our experiment, the initial purpose of reference load curve is to present it as an easy and quick to understand graphical feedback for alert days. Figure IV.17 demonstrates the use of reference load curve for both type of alerts as user interface design of energy feedback.

Formerly, the two methods presented in section IV.3.b.ii and section IV.3.b.iii are analyzed whereas latterly, the two random forest methods are also analyzed. Since the following method (already presented in section IV.3.b.iii) is found suitable in the former analysis, therefore, it is retained to formulate alert day reference load curve as feedback.

-For green alert day 'D', the average of measured load curve of 'D-1' and measured load curve having lowest energy consumption between day 'D-2' and day 'D-5'.

-For orange alert day 'D', the average of measured load curve of 'D-1' and measured load curve having highest energy consumption between day 'D-2' and day 'D-5'. for which the measured load curve data was not recorded on ENEDIS server owing to a technical problem at their end. On day 'D+3' after each alert, the subjects of treated group are sent an SMS stating that they can view feedback of their consumption on alert day 'D' by logging in to their personal account.

Through the log of Etud-Elec site, it is observed that the subjects of treated group only visited their personal account 57 times in total. In percentage term, it makes 2.7% (57/2141) of the total feedback images. The lower number of visits on website might be owing to the time of day of receiving the SMS. Generally, the SMS was sent at noon on day 'D+3', however for certain alerts, it was sent at any time of day. The low number of views of indirect feedback makes it insufficient to statistically analyze the impact of reference load curve as a feedback. Besides serving the purpose of embellishing graphical feedback, the reference load curve also serves to categorize the subjects of treated group with respect to their response towards the nudge signals. However, before doing so, it is significant to determine whether the reference load curve is equally effective.

IV.5 Impact of error ∈ 𝐧𝐨𝐧-𝐚𝐥𝐞𝐫𝐭 in the calculation of energy flexibility

We observed the impact of under-estimation and over-estimation of reference load curve (with respect to measured load curve) for non-alert days in section IV.3.a.i. If an energy difference (owing to underestimation or overestimation) persists for non-alert days, its impact is counted in the calculation of energy flexibility. Therefore, it should be removed for corrected measure of energy flexibility. For the sake of convenience, we call;

-The underestimation (or overestimation) on non-alert days as error ∈ non-alert .

-The energy flexibility achieved on alert day as E flexibility,alert .

To measure the energy flexibility E flexibility,alert . on alert day, the error ∈ non-alert should be removed from the energy difference of reference load curve and measured load curve on alert day. Mathematically, these can be given as;

∈ non-alert | 𝑡 1 𝑡 2 = E reference,non-alert | t 1 𝑡 2 -E measured,non-alert | 𝑡 1 𝑡 2 E flexibility,alert | 𝑡 1 𝑡 2 = (E reference,alert | 𝑡 1 𝑡 2 -∈ non-alert | 𝑡 1 𝑡 2 ) -E measured,alert | 𝑡 1 𝑡 2
Equation IV.4

Equation IV.5

where t1 and t2 are extreme timestamps of the period of interest (or intervention).

The case of underestimating green load curve as well as overestimating orange load curve is illustrated graphically in Table IV.3 below. It should be noted that the area highlighted in blue represents ∈ non-alert , whereas the area highlighted in yellow represents E flexibility,alert .

IV.6 Analysis regarding reference load curve as an alternative of control group

As mentioned earlier, it is observed in the section of meta-analyses that the energy consumption of treated group is compared to that of control group. The analyses are done on group level, rather than on individual level for each subject. The meta-analysis emphasizes the requirement of control group to measure the impact of treatment. Here it raises a scientific question whether it is possible to replace the control group with another tool that is able to measure the impact of treatment on individual level.

To explore this question, we put the reference load curve in place of control group and compare it with the results in section III.5.b. In this regard, the following two methods of formulation of reference load curve are analyzed. The results of former method is presented and discussed below whereas the results of later method is presented in Annex D and briefly discussed here.

-The average of measured load curve of 'D-1' and the measured load curve having o Lowest energy consumption between day 'D-2' and day 'D-5' (for green alert).

o Highest energy consumption between day 'D-2' and day 'D-5' (for orange alert).

-Random forest sequence to sequence -One-sided Mann-Whitney U Test is used with the following hypotheses.

-Null hypothesis (H0): The distribution (of energy consumption data) of control group is statistically greater than the distribution (of energy consumption data) of treated group.

-Alternative hypothesis (H1): The distribution (of energy consumption data) of control group is statistically less than the distribution (of energy consumption data) of treated group.

In the case of green alert days, our interest is to find out whether the null hypothesis is rejected for the energy consumption data of alert days. Statistically, it can be observed if the p-value of Mann-Whitney U Test is less than α = 0.05, which validates our alternative hypothesis. Table IV. [START_REF]Climat, Air et énergie[END_REF] shows the results for the reference load curve used in the experiment, where 'µ' represents mean value, 'б' represents the standard deviation, subscript 'r' and subscript 't' represents reference load curve and measured load curve of treated group respectively. It should be noted that the effect of error ∈ non-alert is removed from the reference load curve of alert days in all the analyses given below. green alert and all green alerts combined. On the other hand, the results in Table IV. [START_REF]Climat, Air et énergie[END_REF] shows that the distribution of reference load curve is statistically greater than the distribution of treated group for all alerts (and at aggregated level). The results in Table D.1 also shows same results. Therefore, the results do not emphasize that a reference load curve can be used as a replacement of control group to measure the load shifting of energy flexibility. In the case of orange alert days, our interest is to find out whether the null hypothesis is retained for the energy consumption data of alert days. Table IV.5 shows the results where the test is performed on the reference load curve used in this experiment as compared to the measured load curve of treated group. The number of observations for both groups are same. The difference of mean values 'µr -µT' in Table IV.5 is found to be slightly higher than the difference of mean values 'µC -µT' in Table III emphasizes that the reference load curve can replace control group to measure the load curtailment of energy flexibility.

In a nutshell, the one sided mann whitney test does not emphasize that a reference load curve can replace control group to measure load shifting. However, the result of same test emphasizes that the reference load curve can replace control group to measure load curtailment. Since it is demonstrated that the reference load curve can effectively measure load curtailment, therefore in the following section, an analysis of measure of energy flexibility (i.e. load curtailment) with respect to the reference load curve is presented for individual subjects.

IV.7 Measure of response of individual subjects towards orange alerts with respect to the reference load curve

It is discussed earlier that the energy flexibility against a treatment is measured through the comparison of treated group against a control group. This analysis does not give information on individual level for each subject of treated group. Therefore, the results of group are generalized for each subject. Contrary to this, the reference load curve is calculated for each individual subject. Subjected to the validation of reference load curve, it permits to analyze the response of each subject towards the call of energy flexibility.

Therefore, the subjects of treated group can be identified as those who:

acted positively in response to nudge signals.

did not act positively in response to nudge signals.

consumption did not change (or negligibly changed) in response to nudge signals OR the response to nudge signals cannot be determined.

To analyze this problem, the following methodology is adopted.

-The reference load curve that we used in our experiment is taken for analysis.

-Since the formulation of reference load curve is based on the historical consumption of previous 5 weekdays of alert day 'D', therefore for the sake of comparison, the energy consumption data of these days is taken.

-An average error ∈ non-alert,average | 6 PM 8 PM is calculated for the error ∈ non-alert,D-n | 6 PM

PM

of each non-alert day between 'D-1' to 'D-5', where n = 1 to 5.

-For each subject of treated group, the E flexibility,alert | 6 𝑃𝑀 8 PM is calculated using Equation IV.5, in which ∈ non-alert | 6 PM 8 PM is replaced by ∈ non-alert,average | 6 PM 8 PM .

-The results are presented in the unit of average energy per hour (Wh) during the period of intervention. The results are only presented for those subjects of treated group whose reference load curve is successfully calculated for all 21 orange alerts.

-For each subject and orange alerts altogether, the results are presented in the form of boxplots.

To identify how the subjects of treated group responded towards the call of energy flexibility, it is imperative to fix a threshold. There is no universal rule for fixing threshold.

Therefore, a sensitivity analysis is performed for multiple threshold values and is presented in Table IV.6. It is observed that the number of undetermined subjects increases with increasing the threshold value, whereas the values of other two variable consequently decreases. It suggests that a suitable threshold should be selected after due diligence of the appliances and the potential of energy flexibility. For the sake of presentation, the threshold values of -200Wh/200Wh are explored.

The average value of distribution in the box plot is considered for analysis of

IV.8 Conclusion

A reference load curve is a predicted load curve on non-alert day. It is assumed that it represents the normal behavior of energy consumption of a household. Its superposition with flexibility driven measured load curve of alert day gives a visual assessment of the effort of energy flexibility. Ideally, the difference of reference load curve with measured load curve should be zero on non-alert day, yet practically it is not possible. Therefore, multiple techniques are analyzed to formulate a reference load curve. By analyzing the distribution of each technique with respect to KPIs, it is concluded that the classical method (using kernel density estimation) performs better than the others for measuring the impact of load shifting on alert day. In the case of load curtailment, it is relatively emphasized by the R2 score indicator that a machine learning based method performed better than the others. Yet it is a future prospect to improve the performance of machine learning model with the introduction of supplementary parameters.

The original purpose of formulating reference load curve is to send as indirect feedback towards the treated group and analyze the impact of this feedback. However, the smaller number of views of indirect feedback makes it insufficient to statistically analyze the impact of reference load curve as feedback. Besides, an aspect of reference load curve is also explored where it is analyzed whether the reference load curve can replace control group in energy flexibility programs. Through our analysis, we are unable to conclude that the reference load curve could replace control group for load shifting, yet it is found effective to replace the control group for load curtailment.

Traditionally, the effort of energy flexibility is assessed on group level by comparing treated group with a control group. However, a suitable and valid reference load curve can serve to measure the effort of energy flexibility at individual level. This individual assessment serves to identify the subjects that have or have not positively responded towards the nudge signal of energy flexibility. Therefore, in complement to the statistical meta-analysis that quantifies the energy flexibility at group level, this instrument serves to quantify and categorize the individual effort of energy flexibility.

V.1 Introduction

In the previous two chapters, we explored the impact of nudge signals on the energy flexibility of the treated group, and we explored an approach where a reference load curve can serve in energy flexibility programs. However, it is interesting to look for the potential appliances for energy flexibility among the appliances that are committed by the treated group. In this chapter, we discuss a proof of concept (and the factors bringing constraints to it) to look for the appliances that have potential for energy flexibility.

We recall here the section I.3.b in which an analysis of various appliances is done.

This analysis is performed based on the annual energy consumption and the nominal power of the appliances. It is deduced from this theoretical analysis that the white appliances (i.e.

washing machine, dishwasher and cloth dryer) might have highest potential of energy flexibility within a period of intervention. This is owing to their highest nominal power as compared to others. However, it can be observed by using non intrusive load monitoring (NILM). Generally, the energy disaggregation is done on load curves with a much higher granularity (e.g. 1 second, 8 seconds etc.); however the granularity in our experiment is much lower (i.e. 30 minutes). Therefore, we would like to implement it on a 30 minutes resolution. In the following section, the state of the art of non-intrusive energy disaggregation is presented. Whereas, in the rest of the chapter, we will discuss a proof of concept that can be used to detect potential appliances for energy flexibility using the NILM energy disaggregation technique.

V.2 Non-Intrusive Load Monitoring

Energy disaggregation is the process of acquiring the device level energy consumption data; correlated with the global energy consumption data of smart meter. It can be either hardware centric or software defined approach. The former is known as intrusive load monitoring (or in short ILM); in which energy consumption recording sensors are connected to the appliances. In this case, it is easy to acquire the energy consumption data of appliances individually at the expense of costly hardware materials and propriety communication protocols. The later is known as non-intrusive load monitoring (or in short NILM 42 ); in which the appliance level energy consumption data is extracted from the global energy consumption data. Rather than the installation of costly sensors, this approach contends on the machine learning techniques to disaggregate the global consumption data.

V.2.a Energy disaggregation datasets

The intrusive load monitoring significantly complements the NILM approach to gather data. Using the ILM approach, public (and private) datasets of energy consumption data are collected. These datasets are used to train NILM models and to validate these models via performance metrics. In various research works, the data is collected for a sampling period of as low as 1 second and as high as 30 minutes throughout the world. Table V.1 illustrates some open-source datasets for residential sector.

V.2.b NILM Techniques

NILM is widely considered as a single channel blind source separation problem.

Most of the research work tackles NILM as a regression problem, in which the individual models of appliances predict the energy consumption based on the global consumption as input. The state of the art also generally prefers supervised learning over non-supervised learning. However, some research work also tackles it as a classification problem, where the appliance (ON/OFF) state is detected and classified using the energy signature of the appliance [START_REF] Basu | Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach[END_REF]. In essence, it should be a combination of both in which the appliance state should be detected and then a prediction regarding its consumption should be made.

The techniques of NILM are categorized as optimization problem, probability statistics, graph signal processing, machine learning and deep learning [START_REF] Zhuang | An Overview of Non-Intrusive Load Monitoring: Approaches, Business Applications, and Challenges[END_REF]. Some research work of these techniques are given as follows:

-Optimization problem: Aided linear integer programming [START_REF] Bhotto | Load Disaggregation Based on Aided Linear Integer Programming[END_REF] and evolutionary optimization algorithm [START_REF] Egarter | Evolving Non-Intrusive Load Monitoring[END_REF]

V.2.c Remarks on the state of the art

Following observations are made in the state of the art of non-intrusive load monitoring.

-The research on energy disaggregation approaches is extensively tested on 4 publicly available datasets. i.e. REDD, REFIT, AMPDs and UK-DALE.

-It is not found that energy disaggregation models are trained on an ensemble of datasets. Rather, the models are trained on individual datasets separately.

-The energy disaggregation algorithms are seldom available publicly for reproducibility of results.

-There does not exist a universal energy disaggregation algorithm. Rather, each research article explores the efficiency of its proposed algorithm. The efficiency is presented against one (or multiple) benchmark algorithms using chosen key performance indicators.

-The models trained and tested on 1 dataset might not be equally efficient for other datasets. This is an open research question under investigation regarding transfer learning. This point will be further investigated in section V.6 and section V.7.

-A number of benchmark algorithms tested on publicly available datasets are available by the name of NILM-TK 43 [START_REF] Batra | NILMTK: an open source toolkit for non-intrusive load monitoring[END_REF]. However, it is found difficult to reproduce the results of these benchmark algorithms for a dataset other than those available in toolkit. Besides, it has the limitation of disaggregating for 5 highest consuming appliances in the household.

Before discussing the proof of concept using NILM, the methodology of exploring an open-source algorithm is presented in section below. 43 NILM-TK is publicly available at https://github.com/nilmtk/nilmtk.

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V.3 Methodology

The following schematic diagram shows the methodology adapted to evaluate the models for energy disaggregation.

V.4 Committed appliances by the subjects

As mentioned in Section II.3.c.ii, the information regarding the presence of diverse appliances present in the households is collected. In addition to this, the information regarding the time of use of appliances with respect to defined timeslots is also collected.

The purpose of collecting this information and having a special session with the subjects is to make the energy consumption and the nominal power of appliances visible to the subjects. Once the subjects of treated group are enlightened about the impact of individual appliance, they are requested to choose appliances from a given set that they can commit to use (in response to green alert) or not use (in response to orange alert). both green alerts and orange alerts respectively. For instance, the washing machine is the appliance that is mostly committed by the subjects of treated group for both green alerts and orange alerts. The commitment frequency of washing machine is followed by dishwasher and electronic gadgets. It is significant to see that the treated group committed the cooking appliances mostly for load curtailment. It is the same case for space heating appliances, however the frequency of commitment of space heating is less as compared to the cooking appliances. Since the frequency of appliance commitment of the experiment is analyzed, it is now significant to explore an energy consumption dataset. This dataset serves to train the model of respective appliances for energy disaggregation. The dataset must have maximum number of appliances in common with the committed appliances of the experiment. For this purpose, IRISE dataset is used, which is demonstrated in the following section. This

database is not open-source, therefore it is not mentioned in the Table V.1.

V.5 The characteristics of IRISE dataset

IRISE dataset is a subset from European database of residential consumption as part of REMODECE project 44 [START_REF] Enertech | Mesure de la consommation des usages domestiques de l'audiovisuel et de l'informatique -Project REMODECE[END_REF], for residential monitoring to decrease energy use and carbon emissions in Europe [START_REF] Basu | Nonintrusive Load Monitoring: A Temporal Multilabel Classification Approach[END_REF]. The dataset is private in nature. As part of IRISE dataset, the energy consumption data of 98 houses for a span of a year is recorded. The data is sampled by 10 minutes. The dataset has data of site consumption as well as energy consumption of individual appliances. The site consumption also includes the unmeasured load which were either intentionally (or non-intentionally) added to the dataset. Therefore, in addition to the site consumption, the aggregated consumption is calculated by summing up the energy consumption of known appliances at each instance.

V.5.a Committed appliances of treated group in the IRISE dataset

Before using IRISE dataset, it is significant to find whether the energy consumption data of the committed appliances by the treated group is available in the IRISE dataset. In this regard, the following 6 appliances are exactly found in the IRISE database. It is more probable that these appliances could be easily traced in the energy consumption data of 1 seconds), the REDD dataset is down sampled to a resolution of 8 seconds. 45 It is available at https://github.com/MingjunZhong/transferNILM. 46 The python libraries of tenorflow and keras are used for algorithm formulation. 47 Data source: https://pureportal.strath.ac.uk/en/datasets/refit-electrical-load-measurements-cleaned 48 Data source: http://redd.csail.mit.edu/ The following performance metrics are used to match the reproducible results with the claimed results. These performance metrics are chosen since they are used in the original article of the algorithm. Therefore, it is easier to compare the results.

-Mean Absolute Error (MAE): It evaluates the absolute difference between the prediction 𝑥 ̂𝑡 and ground truth 𝑥 𝑡 at each instance and calculates the mean value. Besides, there is also impact of other appliances in the aggregated consumption. Therefore, these constraints bring difficulty in the transfer learning of energy disaggregation.

V.7 NILM on IRISE dataset

As the reproducibility of results using transfer-NILM algorithm is verified, it is now significant to create disaggregation models for the appliances that are discussed in the section V.5.a. These are the appliances that are present in the EXPESIGNO dataset and are either found in the IRISE dataset or certain appliances in IRISE dataset are found closer to the appliances in EXPESIGNO. It should be noted that transfer-NILM algorithm makes unique prediction model for each appliance. Before training models on IRISE dataset, the data is checked for the presence of outliers. Certain outliers are found in the data which represents the abnormal energy consumption of appliance at a certain instance. The outliers might be caused owing to the mal communication of sensor. These outliers are removed by the technique mentioned in the section below.

V.7.a Removing outliers from IRISE dataset

For an appliance X, these outliers are removed using the following technique.

-The energy consumption of an appliance X is sorted in ascending order. The highest consumption being 'Xn'.

-A scatter plot is created. The ordinate and abscissa represent the energy consumption of appliance X. Therefore, for the sake of convenience, the reproduced results are compared with the claimed results using the same performance metrics. In the case of detecting potential appliances for energy flexibility, the most significant element is to detect the state of appliance (i.e. ON/OFF) at each instant in the period of intervention. The prediction of extent of consumption is a matter of secondary importance in this regard. Therefore, our interest here is to detect the percentage of correctly predicted instances. The results for 10 minutes sampling and 30 minutes sampling are presented in Table V.11 and Table V.12 respectively. For the sake of presentation, the values are rounded off, except for the highlighted appliances for whom it is found difficult to present results with rounded off numbers. Therefore, the results of these appliances are rounded off to 2 decimal digits. the ON instances, the model also predicts a large number of ON instances when the appliance was actually OFF. In addition to this, the percentage of false negatives (FN) is also (more or less) equal to the true positive (TP). False negative (FN) means that there were certain instances when the appliance was in ON state, however the model predicted that it is OFF. The same pattern is observed in the Table V.12 for the data sampled at 30 minutes.

This makes a model less reliable as we expect that the number of true positives (TP) and true negatives (TN) should be much higher than false positives (FP) and false negatives (FN). Despite having considerable percentage of false positives (FP), the F1 score of certain appliances suggests them to be good models yet sensitive to the fixed threshold. The electric heating, electric oven, dishwasher and TV are notable in this regard.

V.8 Transferability on the EXPESIGNO dataset

The principal objective of creating predictive models of energy disaggregation is to use them on the energy consumption data of treated group during the period of intervention on alert days. In this way, the transfer learning of the predictive models could be analyzed.

The created predictive models are based on deep learning. Due to its ability to identify shared factors present in the input, deep learning is deemed good for transfer learning.

"Although preliminary experiments have shown much potential of deep learning in transfer learning, applying deep learning to this field is relatively new and much more needs to be done for improved performance" (Chen & Lin, 2014).

The proof of concept is presented and tested on a household of IRISE dataset for each appliance. The proof of concept works, though more reliable predictive models are sought. We need more reliable models to detect more true positives and less false positives so that transfer learning of predictive models from an open-source dataset (or ensemble of datasets) towards the EXPESIGNO dataset can be analyzed. For validation of predictive models, the performance of predictive models will be analyzed by using the intrusive load monitoring data of 4 households in EXPESIGNO dataset. It should be noted that instantaneous energy consumption data is recorded as appliance level for these 4 households as part of a side project of EXPESIGNO (known as DEEP-EXPESIGNO).

Following the validation, the measured load curve during period of intervention will be disaggregated to identify potential appliances.

V.9 Conclusion

Energy disaggregation (particularly non-intrusive load monitoring) can serve as a helpful tool to analyze the impact of energy flexibility at appliance level. This does not only validate the potentiality of appliances for energy flexibility, but it also helps in verifying the impact of commitment for energy flexibility (in case commitment exists). In our experiment regarding nudge-based energy flexibility, the white appliances are mostly committed appliances.

Several interventions are made in our experiment. The principal objective is to analyze the impact of nudges at appliance level, however as a starting point, a proof of concept is presented. This proof-of-concept deals with the non-intrusive load monitoring through supervised learning using a dataset of recorded consumption at global level as well as at appliance level. The results of an open source NILM algorithm are reproduced to verify the claimed results before training the predictive models using IRISE dataset. The algorithm produces unique model for each appliance with respect to the given parameters.

The test results of IRISE dataset (by models trained on same dataset) depicts that the models can equally (or to a larger extent) predict false positives as compared to true positives. The F1 score of certain appliances are found well albeit these appliances having more false positives as compared to true positives. Though the models are not found reliable enough to implement on our experiment case, yet we found that NILM can serve as a promising tool to measure the potential of appliances during period of interventions. Here we have explored only a small part of the scientific objectives of such a study. There still needs many points to be addressed in order to ensure the quality of the results that we will be able to obtain on our EXPESIGNO experiment.

VI.1 General Conclusion

Energy flexibility has been in practice worldwide in the form of demand response programs since many decades. These programs are implemented on the electric grid predominantly energized by conventional fossil fuel plants. However, the energy mix is in transition and it tends towards renewable energy sources through distributed generation.

Where the integration of distributed renewable generation mitigates climate change to benefit environment, it also poses technical constraint of intermittent production (leading to possible network congestion). Regarding the indirect energy flexibility, the cognitive burden of acting on price signal is on consumer which enables the consumer to make a controlled and calculated decision. The dual process theory of thinking suggests that the dynamic pricing instigates system 2 of thinking, which operates consciously, in control, with high effort and lowcapacity process. It makes the consumer to think about the implementation and consequences of responding to the price signal with certain questions i.e., a) why to act? b) when to act? c) how to act? and d) how did it go? This makes it riskier to implement price based indirect energy flexibility. Besides, the distributed generation of renewable energy is considered as a common pool resource. In the anticipated energy mix predominant with renewable energy sources, the usual energy consumption will cause scarcity of energy, either through intermittency of renewable energy or high demand at a certain time.

Therefore, it is inevitable that the individuals should implement flexibility in energy consumption to avoid future dilemma. The practice of energy flexibility in present will eventually become a habit in future.

Chapter II begins with a discussion on the socio-cognitive constraints of human being. As a complement to the traditional demand response mechanisms, behavioral insights based energy flexibility mechanism is then presented with the introduction of "Nudge" (and eventually "Green Nudge") as a tool. The orange alerts are based on the forecasted grid congestion whereas the green alert requires prediction of load curve of residential sector, which is presented in the later section of this chapter.

Chapter III begins with the general statistics of alerts and a comparison of sociotechnical variables of both groups. Both groups are found identical in all the statistical variables except the year of construction. However, this statistical variable does not have any concern with our experiment. Following this, the distribution of energy consumption of both groups is compared for normal days. The normal energy consumption of both groups is found similar by the visualization of distribution curve and statistical test of significant difference. The last section of this chapter deals with the meta-analysis of impact of nudge signals. It is observed that the distribution curve of treated group is shifted towards right for green alerts when superposed on the distribution curve of control group. In the case of orange alerts, the distribution curve of treated group is shifted towards left as compared to control group. It fairly gives a notion that the nudge signal has an impact on the energy consumption during the period of intervention on alert days.

The statistical test concludes that the distribution of consumption of treated group for all green alerts is greater than the distribution of control group, yet the result of individual green alert does not support this. On contrary, the result of individual orange alert supports the aggregated result that the distribution of consumption of treated group for all green alerts is less than the distribution of control group. The variation in the energy consumption is quantified for both types of alerts. On aggregated level, the treated group implemented load shifting of energy flexibility by 11.17% in response to green alerts, whereas the group implemented load curtailment by 18.21% in response to orange alerts.

The result regarding load curtailment of 18.21% sounds better compared than the study of moral suasion which yielded 8% [START_REF] Ito | Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand[END_REF], whereas it is closer to the results of a study of similar nature which yielded load curtailment in the range of 12-20% [START_REF] Jorgensen | Reducing Peak Energy Demand among Residents Who Are Not Billed for Their Electricity Consumption: Experimental Evaluation of Behaviour Change Interventions in a University Setting[END_REF]. It is worth mentioning that the subjects of our experiment are environment conscious, therefore, the results of green nudges could be different for a bigger sample of subjects having different environment related ideologies.

Chapter IV presents a tool that can serve to provide feedback as well as to detect the effort of energy flexibility for each intervention. This tool is named as reference load curve. The original purpose of reference load curve is to provide the treated group graphical indirect feedback of each intervention and to measure its impact on the energy flexibility.

However, the low number of views by the treated group is insufficient to measure this impact. Yet to present the feedback, the question raised as how to formulate reference load curve. In this regard, a classical method, two naïve methods and two machine learning methods are analyzed by energy difference indicator and R 2 score. According to the indicators, a classical reference load curve based on kernel density estimation is found suitable for measuring load shifting while a machine learning based reference load curve is found suitable for measuring load curtailment. In our experiment, we selected a naïve method to measure both types of energy flexibility since its results were best among the formerly studied methods and it is easy to explain to the treated group.

Unlike the comparison of control group and treated group, the reference load serves to measure the energy flexibility at individual level. To do so, it is imperative to first validate whether the reference load curve can potentially replace control group. An analysis is done in this regard, and it is observed that the reference load curve can replace control group for load curtailment. The later section presents the utility of reference load curve to detect the individual effort of energy flexibility and to categorize the consumers into groups i.e., a) who acted positive in response to nudge signal, b) who did not act positive in response to nudge signal and c) those for whom the impact is negligible or cannot be determined.

Chapter V demonstrates the frequency of committed appliances for both (green and orange) alert by the treated group. It serves to identify the appliances that were potential to implement energy flexibility during the period of intervention. A proof of concept is presented using NILM energy disaggregation. The idea is to use NILM for disaggregating the measured load curve of our experiment to identify the potentiality of an appliance during the period of intervention. Yet many analyses are required to explore in order to ensure the quality of the results that we will be able to obtain on our experiment.

In a nutshell, the green nudge based non-monetary energy flexibility mechanism is found effective for energy flexibility of load shifting as well as load curtailment. The designed mechanism answers the curious questions of residential consumer upon receiving an energy flexibility alert. This mechanism complements the existing mechanisms of direct load control and dynamic pricing. The results for downward flexibility are found more concrete as compared to the results of load shifting. A reference load curve can replace the control group to detect the individual effort of energy flexibility. To identify the potentiality of an appliance, the NILM energy disaggregation can be used, however it needs to be explored in order to ensure the quality of results on our experiment.

VI.2 Future Prospects

This thesis offers several future prospects in multiple axes. The axes can be categorized based on use of other nudges in behavioral science, architecture development, reference load curve and data analysis in depth. We discuss each of these below.

VI.2.a Use of other nudges of behavioral science for energy flexibility

In our study, we used a set of green nudges for motivating people to implement energy flexibility. It includes information (of forecasted grid condition), default commitment, reminder and feedback. However, several other nudges should also be explored to implement energy flexibility. For instance, the intuitive metrics can be used to express information and/or the consequences of energy flexibility can be reframed in terms people care about [START_REF] Yoeli | Behavioral science tools to strengthen energy & environmental policy[END_REF].

In addition to this, the nudge of personalized target can be explored. These nudges are designed for everyone separately. Each individual subject could be communicated about the impact of switching an appliance present in his/her household. The impact of feedback as a nudge is an interesting subject to explore. We have seen in our experiment that the subjects are seldom interested in looking at their feedback. For instance, anchoring could be used during the information sessions in which the subjects should be informed about the importance of feedback. The feedback could be intertwined with few Yes/No questions to be responded by the subject. This in return also give data about the extent of easiness of implementing energy flexibility for each subject which could then be analyzed statistically.

VI.2.b Architecture development for experiment

There are certain developments that can be made as future prospect in the architecture. Some of these are outlined as follows.

-The orange alerts in our experiment are based on forecasted network congestion (PP1/PP2) on French national grid. Like EJP which is notified with respect to the part of the France (north, south, east or west), the forecasted network congestion can be further disintegrated at department level (in France). For local energy communities, it is essential to be more realistic and problem-oriented that the forecasted network congestion of its mini-grid should be used.

-Regarding the green alerts, a threshold of nebulosity is used as one of the criteria. It represents the solar energy, whereas criterion should also be explored for other types of renewable energy e.g., threshold for wind speed etc.

-Auto-consumption could be taken into account while sending nudges.

-In the architecture of our experiment, we used the modelled residential load curve (of national level) to evaluate a criterion of green alerts. This load curve is periodic within the constraints of its coefficient values. However, it would be more significant to model the residential load curve at experiment level. It is possible that this modelled load curve could not be as periodic as the national residential load curve.

-In the architecture of our experiment, we only took into account the smoothened national temperature for the prediction of modelled residential load curve. The historical data of pseudo-radiation (in percentage) is also given at national level. The impact of this variable could be explored for the prediction of residential load curve.

Besides, we used random forest for prediction (with good results), however other techniques should also be explored, for instance deep learning (though it needs a large amount of data).

VI.2.c Reference Load curve

Reference load curve is a significant tool that has been explored for the sake of this experiment at a greater extent. However, according to our information, the work in this regard is preliminary and offers a lot of prospects. Some of these are outlined here.

-Since reference load curve is a prediction, therefore methods other than Random Forest should also be explored. For instance, deep learning with a sufficient amount of data might give better results than machine learning models.

-Only two performance indicators are explored in this thesis. Other performance indicators should also be explored for better presentation of suitability of a method to formulate reference load curve.

-With our analysis, we cannot show that the reference load curve can replace control group to detect and measure load shifting. However, this area of research deserves a lot of improvement and several methods of formulating reference load curve should be analyzed in this regard in future.

-Based on a sensitivity analysis of the thresholds for detecting positively acting subjects, a most suitable threshold value should be selected.

VI.2.d In-depth data analysis of potential appliances for energy flexibility

In Chapter V, a proof of concept regarding in-depth analysis of potential appliances for energy flexibility is presented. It offers several future prospects, of which some are mentioned below.

The impact of resolution on NILM should be evaluated in depth. It should be evaluated in depth that whether the models trained on higher resolution are equally Finally, the models should be tested on the EXPESIGNO dataset to identify and quantify the potentiality of appliances during the period of intervention. A naïve method of quantification in terms of percentage could be using the formula below.

B.1.ii House/Apartment

This variable concerns the type of housing of the household (apartment or house).

B.1.iii Surface of household

This variable represents the living area of the household of both groups. In order to use this variable, it was necessary to make groupings into classes. The surface areas are grouped by classes of 50, whereas the last grouping is between from 150 to 400.

B.1.iv Year of construction

This variable provides information on the period of construction of the household.

B.1.v Number of persons in the household

This variable concerns the number of persons per household.

B.1.vi Presence in the household at least 3 days per week (during weekdays)

This variable corresponds to the household response for the question; "In your dwelling is there a person during the day at least 3 days a week (excluding weekends)?"

B.1.vii Types of electric heating

This variable provides information on the type of heating used by households.

B.2.ii Air Conditioning

This variable provides information on the presence of air conditioning in households.

B.2.iii Heat Pump

This variable provides information on presence of heat pump in the household.

B.2.iv PV panels

This variable allows us to see whether or not households have photovoltaic panels.

B.3.ii Dishwasher

This variable provides information on the number of dishwashers per household.

B.3.iii Washing Machine

This variable shows the number of washing machines present in the homes.

B.3.iv Cloth Dryer

This variable provides information on the number of clothes dryers owned by households.

B.3.v Electric Vehicle

This variable tells us whether or not households have an electric vehicle.

B.4.ii Electric Oven

This variable corresponds to the use of the oven on weekdays.

B.4.iii Dishwasher

This variable corresponds to dishwasher use on weekdays.

B.4.iv Washing Machine

This variable refers to the use of the washing machine on weekdays.

B.4.v Cloth Dryer

This variable refers to the use of the cloth dryer on weekdays.

B.4.vi Electric hotplates

This variable refers to the use of the induction plates for the purpose of cooking on weekdays.

B.4.vii Radiator

This variable refers to the use of the electric radiator for space heating on weekdays.

B.5.ii Electric Oven

This variable corresponds to the use of the oven on weekend.

B.5.iii Dishwasher

This variable corresponds to dishwasher use on weekends.

B.5.iv Washing Machine

This variable refers to the use of the washing machine on weekend.

B.5.v Cloth Dryer

This variable refers to the use of the cloth dryer on weekend.

B.5.vi Electric hotplates

This variable refers to the use of the induction plates on weekend.

B.5.vii Radiator

This variable refers to the use of the electric radiator for space heating on weekdays.

Figure I. 1 4 Figure I. 2 6 Figure I. 3 7 Figure I. 4 ( 8 Figure I. 5 9 Figure I. 6 10 Figure I. 7 11 Figure I. 8 15 Figure I. 9 17 Figure I. 10

 142637485961071181591710 Figure I.1 Global Warming Projection w.r.t different scenarios until 2100 (Climate Action Tracker, 2021) ............................................................................................................ 4 Figure I.2 Annual Power Production (TW) in France ...................................................... 6 Figure I.3 Annual CO2 Emission (MTCO2 eq.) in France ............................................... 7 Figure I.4 (a) Conventional merit order (b) New merit order including renewable energy sources (Bahar & Sauvage, 2013) .......................................................................................... 8 Figure I.5 Maturity of Energy Storage Technologies until 2017 (Nguyen et al., 2017) .... 9 Figure I.6 Flexibility solutions relevant to intermittent renewable production (International Energy Agency, 2018) .................................................................................. 10 Figure I.7 Hierarchy of demand response (Alasseri et al., 2021) .................................... 11 Figure I.8 Modelled average daily load curve of residential sector (ENEDIS, 2021) .... 15 Figure I.9 Annual Energy Consumption (kWh) of household appliances (Energie Douce, 2021; Energie Mobile, 2021; Réseau de Transport d'Électricité France, 2017, 2018, 2019, 2020a) 17 Figure I.10 Nominal power of household appliances (Energie Developpement, 2013; Energie Douce, 2021; Energie Mobile, 2021) ...................................................................... 17

Figure I. 11 20 Figure I. 13 21 Figure II. 1 26 Figure II. 2 29 Figure II. 3 35 Figure II. 4 37 Figure II. 5 38 Figure II. 6 41 Figure II. 7 44 Figure II. 8 45 Figure II. 9 45 Figure II. 10 46 Figure II. 11 52 Figure II. 16 53 Figure II. 17 55 Figure II. 18 56 Figure II. 19 56 Figure II. 20 58 Figure II. 21 Relative 58 Figure II. 22 Relative 59 Figure II. 23 Relative 59 Figure II. 24 60 Figure III. 1 67 Figure III. 2 74 Figure III. 3 75 Figure III. 4 77 Figure III. 5 77 Figure III. 6 78 Figure III. 7 78 Figure III. 8 83 Figure III. 9 87 Figure IV. 1 93 Figure IV. 2 A 93 Figure IV. 3 97 Figure IV. 4 98 Figure IV. 5 99 Figure IV. 6 Figure IV. 7 99 Figure IV. 8 100 Figure IV. 9 100 Figure IV. 10 101 Figure 101 Figure 101 Figure 104 Figure IV. 15 106 Figure IV. 17 107 Figure IV. 18 114 Figure IV. 19 Figure V. 1 Figure V. 2 123 Figure V. 3 124 Figure V. 4 125 Figure V. 5 131 xFigure V. 7 132 Figure V. 8 132 Figure V. 9 134 Figure V. 10 138 xi

 112013211262293354375386417448459451046115216531755185619562058215822592359246016727437547757767877888398719329339749859967998100910010101101101104151061710718114191212331244125513171328132913410138 Figure I.11 Average household energy saving with respect to type of feedback(Ehrhardt-Martinez & Donnelly, 2010) ................................................................................................ 19 Figure I.12 Behavioral Insight based signal to implement indirect energy flexibility ..... 20 Figure I.13 Indirect feedback in graphical form of load curve ......................................... 21 Figure II.1 Factors pertaining to decision making .......................................................... 26 Figure II.2 Green nudge to reduce the excessive towel paper use (Lars Biesewig & Annie Krautkraemer, 2018) ........................................................................................................... 29 Figure II.3 An example of randomized controlled field trial (Haynes et al., 2012) ......... 35 Figure II.4 Load shifting from evening peak hours to afternoon .................................... 37 Figure II.5 Load curtailment during peak hours in the evening ..................................... 38 Figure II.6 Curious questions and corresponding designed nudge treatment ................ 41 Figure II.7 Feature salience through comparison of load curves .................................... 44 Figure II.8 Schematic Diagram of project "EXPESIGNO" .............................................. 45 Figure II.9 Timeline illustrating the signaling on day 'D-1' ............................................. 45 Figure II.10 Timeline illustrating the reminder SMS and period of action on day 'D' .. 46 Figure II.11 Timeline illustrating the indirect feedback on day 'D+3' .......................... 46 Figure II.12 Calendar of the months for orange alerts based on EJP ............................ 49 Figure II.13 Calendar of the months for orange alert based on TEMPO for 1st session49 Figure II.14 Random Forest Regression ........................................................................ 51

Figure I. 1

 1 Figure I.1 represents the global warming projection until 2100. It is therefore required to decrease the atmospheric CO2 concentration by decreasing the CO2 emissions.

Figure I. 1

 1 Figure I.1 Global Warming Projection w.r.t different scenarios until 2100 (Climate Action Tracker, 2021) During 2018, an increase of 1 °C in the global temperature of the earth is observed with respect to the average global temperature of pre-industrial period (1850 to 1900), whereas an increase of 2.1 °C is observed in metropolitan France with respect to the average temperature of the period between 1961 and 1990 (Manuel Baude et al., 2021). The power generation from fossil fuel is a major contributor of CO2 emissions in the world. During 2018, the electricity production accounted for 41% of CO2 emission in the world, whereas 6.67% of total CO2 emission in France is caused by electricity production (Manuel Baude et al., 2021; Réseau de Transport d'Électricité France, 2018).

  percentage share of fossil fuel power plants has been decreasing since 2017. It can be observed by Figure I.2 that the renewable power production (from hydro-electric, wind and solar) increased by 17%, whereas the non-renewable power production (i.e. fossil fuel) decreased by 23% in 2018 as compared to previous year. Consequently, the CO2 emission decreased by 27% in 2018 as compared to previous year (as illustrated by Figure I.3).

Figure I. 2

 2 Figure I.2 Annual Power Production (TW) in France 4

Figure I. 4 (

 4 Figure I.4 (a) Conventional merit order (b) New merit order including renewable energy sources (Bahar & Sauvage, 2013) I.1.d Intermittency of renewable energy and need of energy flexibility Where the renewable energy mitigates the effects of climate change, it poses constraint to electricity grid. Unlike the conventional power plants, the energy produced by renewable energy is intermittent in nature. e.g. the solar plants can only produce energy in daylight, the wind turbines rotates with wind speed and run of river plants produces electricity with respect to the volume of flowing water.

Figure I. 5

 5 Figure I.5 Maturity of Energy Storage Technologies until 2017 (Nguyen et al., 2017)

  to curtail the peak by either load curtailment or load shifting. Valley filling can also be practiced in response to a DSO or aggregator signal and is more significant for industrial consumers or aggregated load. In its study of 100% energy transition, ADEME identifies 4 potential loads that can actively participate in the challenge of energy flexibility. These potential loads are water heating, white appliances (i.e., washing machine, cloth dryer and dishwasher), electric vehicle and space heating. With an estimated annual peak demand of 134 GW, the demand side energy flexibility potential of aforementioned loads would be 3 GW, 3 GW, 7 GW and 25 GW per annum respectively(ADEME, 2015). This brings a potential demand side flexibility of 28.36%. It is cost-effective and brings in consumer participation to meet the challenge of sustainable energy use. The solution is also cheaper than supply side management and energy storage. It can traditionally lessen the impact of intermittency in the same time scale of 10 seconds and 30 minutes.

Figure I. 6

 6 Figure I.6 Flexibility solutions relevant to intermittent renewable production (International Energy Agency, 2018) I.2 Energy Flexibility and its usual modes of implementation The definition of the term energy flexibility trickles down from a consumption sector (i.e. Industry, Agriculture …) to the individual consumer. It is also widely presented by the terms of demand side management and demand response. As the buildings are massive energy consumers, therefore the definition of energy flexibility is generally attributed to buildings. "The energy flexibility of a building is the ability to manage its demand and generation according to local climate conditions, user needs, and energy network requirements" (Jensen et al., 2017).

Figure I. 7

 7 Figure I.7 Hierarchy of demand response (Alasseri et al., 2021)

  motivation. This lack of motivation occurs owing to limited information and the uncertainty about the potential consequences of the flexibility (Dütschke & Paetz, 2013; Goulden et al., 2014; Krishnamurti et al., 2012). In a longer run, this lack of motivation does not cause any formation of habit in response to dynamic signal pricing. The third constraint is that the consumer often deems a low, invisible and uncertain return from their effort, especially with dynamic tariff (Hargreaves et al., 2010).

A

  number of loads, depending upon the degree of flexibility of consumer Intervention may cause loss of comfort? May be It depends upon the degree of flexibility of consumer I.3 The challenge of energy flexibility in residential sector I.3.a Potentiality of residential sector According to RTE 13 , the residential sector is the biggest electricity consumption sector in France. During the period between 2017 and 2019,The share of total annual energy consumption of residential sector increased from 36% to 38% (Réseau de Transport d'Électricité France, 2017, 2018, 2019, 2020a). The residential sector remained focal energy consumption sector in energy flexibility programs worldwide (Ehrhardt-Martinez & Donnelly, 2010; Hatton & Charpentier, 2014).Based on the measured power consumption of participating entities throughout France, ENEDIS models a load curve of each consumption sector. These modelled load curves are called profile coefficients 14 , whereas "a profile reflects a pattern of consumption or generation, i.e. how an average customer consumes or generates electricity over time" (Réseau de Transport d'Électricité France, 2020b).

Figure I. 8

 8 Figure I.8 Modelled average daily load curve of residential sector (ENEDIS, 2021) The modelled average daily load curve of residential sector is illustrated in Figure I.8. It is based on the historical data of profile coefficient of residential sector. Independent of the season, three major consumption peaks are observed: one for weekday evenings, one

Figure I. 9

 9 Figure I.9 Annual Energy Consumption (kWh) of household appliances (Energie Douce, 2021; Energie Mobile, 2021; Réseau de Transport d'Électricité France, 2017, 2018, 2019, 2020a) Figure I.10 suggest that the nominal power of fridge is around 10 times less than the cloth dryer. Therefore, for a short intended period of interventions, cloth dryer offers more potential than fridge. If we recall the study of ADEME regarding the potential appliances for energy, it reflects that around 3 GW of energy flexibility can be obtained from these appliances in year 2050 (ADEME, 2015).

Figure I. 10

 10 Figure I.10 Nominal power of household appliances (Energie Developpement, 2013; Energie Douce, 2021; Energie Mobile, 2021)

Figure I. 11

 11 Figure I.11 Average household energy saving with respect to type of feedback(Ehrhardt-Martinez & Donnelly, 2010) As an alternative to price based indirect energy flexibility, the behavioral economics provides a key to influence the preference of a residential consumer while offering zero risk at the same time. For this purpose, non-monetary and pro-social signals should be sent to the consumer. The ecological signals are found most effective in relatively liberal communities (Costa & Kahn, 2013). This thesis presents the design and results of a conceived indirect energy flexibility mechanism based on behavioral insights. In this regard, a new non-monetary, pro-environmental indirect energy flexibility mechanism is designed and tested with several households. A similar instrument is introduced in November 2020

Figure I. 12 Behavioral

 12 Figure I.12 Behavioral Insight based signal to implement indirect energy flexibility In this thesis work, pro-environmental nudge signals are introduced to implement indirect energy flexibility with the following characteristics.

Figure I. 13

 13 Figure I.13Indirect feedback in graphical form of load curve

--

  in implementing energy flexibility. Behavioral economics provide a way to design nonmonetary, pro-social and environmental mechanisms of indirect energy flexibility. This thesis presents an indirect energy flexibility mechanism for residential consumers, based on behavioral insights and indirect feedback. For validation, the mechanism is experimented with a batch of French households using non-monetary and pro-environmental interventions. The following chapters will discuss: The significance of behavioral insight (specifically green nudges) in designing a new energy flexibility mechanism -The design of experiment (including the conceived set of nudges and the architecture for each intervention) The day-ahead forecast of modelled national load curve of residential sector -The measure of effectiveness of nudge signals -A proof of concept to detect potential appliances for energy flexibility Chapter II Case Study: Nudge signals for implementing indirect energy flexibility in French households

Figure II. 1

 1 Figure II.1 Factors pertaining to decision making

  purpose of a green nudge is to use behavioral biases in order to encourage citizens to adopt lifestyles showing a greater respect for the environment (Centre d'analyse stratégique, 2011). An example of green nudge is illustrated in Figure II.2.

Figure II. 2

 2 Figure II.2 Green nudge to reduce the excessive towel paper use (Lars Biesewig & Annie Krautkraemer, 2018)

  studied. It has been found that moral suasion induced a short-run reduction in peak-hour electricity usage by 8%, economic incentive produced electricity consumption reduction of 14 to 17%. In the case of moral suasion, "the treatment effect diminishes after repeated interventions but can be restored to the original level by a sufficient time interval between interventions. Economic incentives induce larger treatment effects, little habituation, and significant habit formation" (Ito et al., 2018). (Buckley & Llerena, 2018) played demand response as a common pool resource game in a lab experiment to do a comparison between nudge and peak pricing-based interventions.It is found that in the absence of an energy conservation policy, the individuals do not achieve socially optimal level of consumption. "The nudges were quick, easy to understand and resulted in an immediate reduction in consumption in the period following initial feedback. On the other hand, individuals took longer to understand the effect of the increased price on their consumption and so took longer to integrate it into their decision making process"[START_REF] Buckley | Demand response as a common pool resource game: Nudges or peak pricing[END_REF].

  effectiveness of treatment interventions is evaluated by comparing treated subjects with those in a control group (or those who received a different treatment). These interventions affect the normal approach of subjects towards the problem under study (in this case energy consumption). The motive of these interventions is to encourage individuals for taking actions to improve their individual and social well-being (Banerje & Duflo, 2011). Figure II.3 illustrates an example of randomized field trial. The randomized control trial is used toimplement energy conservation for moral suasion of subjects in Japan[START_REF] Ito | Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand[END_REF]. The future electricity grid needs energy flexibility at consumer side; therefore, it is necessary to understand the consumer behavior towards this challenge. For this purpose, this experiment is targeted to know the residential consumer behavior towards the challenge of energy flexibility. It should be noted that though the methodological aspect of this experiment is same as that of[START_REF] Ito | Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand[END_REF], however the approach i.e. the protocol is different.

Figure II. 3

 3 Figure II.3 An example of randomized controlled field trial (Haynes et al., 2012)

  lab in collaboration with the social scientists and pro-environment associations. Distribution of flyers, personal interactions and sending emails to the subject already presented in the laboratory panel was the mode of communication of campaigning. The persons listed as student in the laboratory panel were excluded from the recruitment process. The interested candidates were offered to fill in an online form. The gathered information was concerned about the type of housing, the composition of the household, the socio-professional category, the presence of a smart meter, the type of heating, etc. 686 candidates filled the online form to participate in the experiment. At this stage of the recruitment process, the information given to individuals was limited to the launch of a study on energy consumption and the development of renewable energy. A selection criterion is decided in order to select the potential subjects among the candidates. Out of 686 candidates, 175 households are recruited for the purpose of experiment. The selection criteria is as follows:

Figure II. 4

 4 Figure II.4 Load shifting from evening peak hours to afternoon -Orange alert (OA) The purpose of this alert is to nudge the treated group to curtail their load between 6:00 PM and 8:00 PM on alert day 'D'.

Figure II. 5

 5 Figure II.5 Load curtailment during peak hours in the evening

  a. an alert via SMS on day 'D-1' between 6:45 PM and 7:15 PM b. a second SMS inviting subjects to consult the commitments made at the start of the study and, if necessary, to modify them for the coming period between 7.30 PM and 10.30 PM on day 'D-1'. The consultation of the commitments, and their modifications for the current period, was carried out via a dedicated website with personal and secure access. c. a final reminder SMS on day D, at 7.30 am.

Figure II. 6

 6 Figure II.6Curious questions and corresponding designed nudge treatment

Figure II. 7

 7 Figure II.7 Feature salience through comparison of load curves

Figure II. 8

 8 Figure II.8 illustrates the schematic diagram of the experiment. The schematic diagram briefly demonstrates the collection of data, accessing the historic data from ENEDIS as well as accessing the day-ahead forecasted notification via web-services API 24 . However, it does not demonstrate the protocol of each intervention in the form of timeline. The schematic diagram is described in detail in the sub-sections below, whereas the protocol of each intervention is given from Figure II.9 to Figure II.11.

Figure II. 8

 8 Figure II.8 Schematic Diagram of project "EXPESIGNO"

Figure II. 12

 12 Figure II.12 Calendar of the months for orange alerts based on EJP EDF notifies subscribers on day 'D-1' regarding the day-ahead color of TEMPO and EJP. Therefore, EJP and TEMPO are added to the protocol of orange alerts for the months of February and March 2020. Like PP1/PP2, the day-ahead notification is obtained via the APIs of EJP and TEMPO at 6:00 PM on day 'D-1'. The pre-defined condition for a triggered alert based on EJP and TEMPO is that either of the two instruments notified the day ahead in red color. Figure II.12 and Figure II.13 demonstrate the color-coded days during the months of February and March 2020 for EJP and TEMPO respectively.

Figure II. 13

 13 Figure II.13Calendar of the months for orange alert based on TEMPO for 1st session

  4.c. Numerous techniques are available in the literature for short term load forecasting. Moghram & Rahman (1989) presented an overview of 5 short term load forecasting techniques namely; multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter. Liu et al. (1996) presented fuzzy logic, neural networks, and autoregressive (AR) models in their article. Taylor & McSharry (2007) presented autoregressive approaches such as ARIMA and periodic AR, or Holt-Winters exponential smoothing and a method based on the principal component analysis (PCA). Recently, recurrent neural network (RNN) based short term load forecasting has been presented in multiple articles. Bianchi et al. (2017) presents a comparative analysis of RNN which includes Long Short Term Memory (LSTM) and gated recurrent unit (GRU).

  Random forest is an ensemble supervised learning method for classification and regression. "It operates by constructing a multitude of decision trees at training time and outputting the class that is mean/average prediction (regression) of the individual trees" (Tin Kam Ho, 1995, 1998). Figure II.14 represents the schematic diagram of random forest regression.

Figure

  Figure II.14 Random Forest Regression

Figure

  Figure II.15 Principal of regression model creation, validation and use in real time For the purpose of prediction, scikit-learn package (Pedregosa et al., 2011) of python language is used. The script access data via APIs of ENEDIS and Climacell (weather

Figure II. 16

 16 Figure II.16Load curve of residential profile coefficient33 by ENEDIS

  data. For each French weather station, the API offers 109 hours of forecast with a time step of 1 hour. RTE France (2020) presents the method of smoothing the temperature of 32 French weather stations, which is illustrated in Figure II.17. The smoothening is carried out by successive application of the following formulae.

Figure II. 17

 17 Figure II.17The procedure of smoothening the measured temperature of 32 cities

Figure II. 18

 18 Figure II.18 gives a schematic diagram of the regression model.

Figure II. 18

 18 Figure II.18 The prediction model for predicting profile coefficient of day 'D' II.4.d.i Dynamic Profile Coefficient The historical data of dynamic profile coefficient obtained by ENEDIS API French national load curve of residential sector is used as input 'X' for training the model. In addition to this, 2 parameters of lagged dynamic profile coefficient are introduced in the dataset as part of input 'X'. One parameter of the model is dynamic profile coefficient lagged by 14 days, while other parameter is dynamic profile coefficient lagged by 28 days. The frequency of availability of new data for these curves is variable. The data is generally available until day D-n where n = 1…..10. Normally RTE updates the data on their opensource platform either weekly or every 10 days. This is the reason that lag of 14 days and 28 days are selected respectively rather than a lag of 1 day or 7 days. Figure II.19 shows a correlation between the actual profile coefficient and the lagged profile coefficient by 14 days and 28 days respectively. It shows the periodicity of the curve of profile coefficient.

Figure II. 19

 19 Figure II.19The correlation of profile coefficient with lagged profile coefficients

  One hot encoding is a process by which categorical variables are converted into binary columns. e.g. a column having 7 day of week can be transformed into 7 binary columns representing each day of week. -Hour of day: 24 one hot encoded columns are introduced to the training dataset for the parameter of hour of the day. -Day of week: 7 one hot encoded columns are introduced to the training dataset for the parameter of day of week. -Day of year: 365 one hot encoded columns are introduced to the training dataset for the parameter of day of year. It should be noted that data of leap day from year 2020 was removed from the model for the sake of convenience.

Figure II. 20

 20 Figure II.20 illustrates the prediction of load curve for weekdays as well as weekend.Since the model upgrades with the availability of new data, therefore for the purpose of illustration, the results are based on training of a historical data of 3 years and 2 months. It can be seen in the figure that the model is also capable of predicting the weekend, though the aim of prediction for the experiment is to have good prediction for weekdays.

Figure II. 20

 20 Figure II.20 Prediction of French residential load curve by random forest regression It is worth mentioning that initially a model consisting of temporal features as input and dynamic profile coefficient as output is analyzed. Following this, the lagged features of dynamic profile coefficient are added in the model. Lastly, the temperature and lagged temperature features are also added in the model. An interesting factor to look at is the relative feature importance of the input parameters. The analysis of base model with temporal features and subsequent introduction of new features is illustrated below with respect to feature importance.

Figure II. 21 Relative

 21 Figure II.21 Relative Importance of temporal features It can be observed in Figure II.21 that hour of day is relatively most important feature among the temporal features. However, with the introduction of lagged features of dynamic coefficient, the temporal feature of day of year appears to be relatively most

Figure II. 22 Relative

 22 Figure II.22 Relative Importance of temporal features and lagged profile coefficient Yet it can be observed in Figure II.23, a temperature feature lagged by 12 hours isrelatively more important than the temporal features. Despite this, it is found that the temporal features are significant to improve the model performance. It can also be noticed that the feature illustrating whether the target day is a holiday does not seem to have any relative importance among all the features. Likewise, the features illustrating whether the target day is a weekday seems to be least important among all the features relatively.

Figure II. 23 RelativeEquation II. 4 Figure

 234 Figure II.23Relative Importance of all features in model

Figure II. 24

 24 Figure II.24The comparison of performance metric

  Dynamic pricing is a tool to motivate consumer towards moderate energy consumption. The residential consumer faces constraint of limited knowledge and uncertain utility. If a consumer has enough information, the consumer does not know how to implement energy flexibility. The consumer makes decision according to system 2 of thinking by calculating the outcome and opportunity cost of energy flexibility. The residential consumer should be motivated to make automatic decision through system 1 of thinking while keeping in account the social, psychological, and ambient factors. Since the distributed generation serves as common pool resource, the residential consumer should be enlightened about the requirement of energy flexibility.The general questions of the consumer regarding energy flexibility are identified i.e. why to act, when to act, how to act and how did it go. A green nudge-based energy flexibility mechanism is experimented with a pool of French households. A set of green nudges are used to answer the above questions. It includes information, commitment, and feedback. The purpose of this set of nudges is to intrinsically motivate the treated group.Two types of interventions are experimented i.e. load curtailment and load shifting.The signal of load curtailment is based on the day ahead forecasted network congestion of the French electric grid. One of the criteria for load shifting signal is based on the forecasted modelled load curve of national residential sector. A prediction model is created using random forest regression. It is observed that by adding the lagging features of historical curve and temperature, the performance of prediction model is improved up to 57% of the baseline model only containing temporal features. It demonstrates the significance of lagged features of historical data for better prediction. Following the presentation of experimental architecture of nudge-based energy flexibility, the next chapter will discuss the efficiency of these nudge signals.

  2020. However, due to the first national lockdown in France caused by COVID-19 pandemic, the first session was re-planned from July 2020 to September 2020. It was done to not bother the subjects during the lockdown (and immediate post-lockdown) period. As it can be also observed in Figure III.1, the weekly average consumption in France during the lockdowns remained different comparing to the range of weekly average consumption of previous 5 years (Réseau de Transport d'Électricité France, 2020a). Therefore, triggering any alert during the lockdown period would not be effective to measure the impact of energy flexibility regarding usual energy consumption behavior. Besides, the behavior of energy consumption during the lockdown would be different than the normal behavior of energy consumption in the households, which would influence the experiment.It is recalled here from section II.3.d.ii that two criterions were put in place for triggering a green alert. The principal criterion is related to the day-ahead forecasted nebulosity during the afternoon in Grenoble. The nebulosity should be zero. The second criterion is based on calculating day-ahead 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 𝐺𝐴 . This coefficient is a ratio of average evening energy consumption and average afternoon energy consumption. It should be greater than 1. It was expected that there will be a significant number of green alerts during the second session. However, contrary to the expectations, only 2 green alerts were triggered by the automatic system between April 2021 and September 2021. This was since the sky remained cloudy almost throughout the summer 2021 in Grenoble. TableIII.3 demonstrates the basis of each triggered green alert for both sessions.

Figure III. 1

 1 Figure III.1 The impact of lockdown on French energy consumption during 2020 (Réseau de Transport d'Électricité France, 2020a)

6 .

 6 The measurements are added and then divided by 3 to obtain the average energy consumption per hour. o Similarly for orange alerts, the number of measurements between 6 PM and 8 PM are 4. The measurements are added and then divided by 2 to obtain the average energy consumption per hour.

Figure III. 3 .

 3 Figure III.3. The distribution of control group is overlapped by the distribution of treated group by more than 90%. Like the case of green sessions, the difference in mean values is minimal whereas the ratio of coefficient of variation is also closer to 1. Therefore, it can be

Figure III. 3

 3 Figure III.3 The distribution of non-alert days of orange sessions III.4.b Assessment of statistical difference in the normal energy consumption This analysis gives a global demonstration of the energy consumption during the period of interest on non-alert days. As observed from the Figure III.2 and Figure III.3, the data is asymmetric and does not follow normal distribution. Therefore, a statistical test for non-parametric data, which is known as Mann-Whitney U Test 37 is performed. With respect to the alternative hypothesis (H1), the test is further classified into one-sided or twosided. Here, our interest is to check whether the distribution underlying control group is similar (or significantly different) than the distribution underlying treated group. A twosided Mann-Whitney U Test is done in this regard with the following hypotheses.

Figure III. 4 and

 4 Figure III.5 represents the distribution of energy consumption for green session 1 and green session 2 respectively. The distribution curve of treated group displaced towards left as compared to distribution curve of control group. For both green sessions, the ratio of mean energy consumption between control group and treated group decreased during alert days as compared to non-alert days. It merely suggests that the treated group increased their energy consumption during the period of intervention. In the case of green session 1, the ratio of coefficient of variation has negligible difference between non-alert days and alert days. It means that the data is near-identically dispersed around the mean value. Despite, the difference of ratios is considerable for green session 2, the data of measured load curve of 2 nd green alert was not registered by ENEDIS owing to a technical problem in its server. Therefore, Figure III.5 only represents the 1 st green alert of 2 nd session, which is insufficient for current and forthcoming analysis of green session 2.

Figure III. 4 1 Figure III. 5

 415 Figure III.4The distribution of non-alert days and alert days of green session 1

Figure III. 6 Figure III. 7

 67 Figure III.6The distribution of non-alert days and alert days of session 1 of type 'orange'

Figure III. 8

 8 Figure III.8 summarizes the global results, whereas the detailed results are presented in annex C in tabular and graphical form. For green alerts, the increase in the energy consumption of treated group by the virtue of nudge signal is observed by 11.17%

Figure III. 9

 9 Figure III.9 The percentage change in the energy consumption on alert day as compared to non-alert day Similar to the green alerts, the global impact of all the orange alerts comprising of both sessions (as well as individual session) is analyzed. Figure III.10 summarizes these results, whereas the detailed results are presented in annex C in tabular and graphical form.

Figure III. 10

 10 Figure III.10 The statistical results of OLS regression for orange sessions Finally, we analyze the change in the energy consumption of treated group during the period of intervention on orange alert days versus the same period in previous non-alert days of the same session. Table III.11 gives us the results for both orange sessions, where the change in energy consumption can be observed in the column difference on alert day. e.g., the energy consumption of treated group on the 2 nd orange alert day decreased by 23.8% as compared to the previous non-alert weekdays. And it is statistically significant within the range [0, 0.001] of the significant code 40 . The difference of alert day is graphically presented in Figure III.11 and Figure III.12 for orange session 1 and orange session 2 respectively. Like the green alerts, no evidence of habit formation is found for both alerts. However, the difference of energy is considerable as compared to the green alerts.

Figure

  Figure III.11The percentage change in the energy consumption on alert day as compared to non-alert day for 1st session of orange alerts

Figure IV. 1

 1 Figure IV.1 An ideal reference load curve Figure IV.1 represents an ideal case of reference load curve. Ideally, it is expected from the reference load curve that the difference between reference load curve and measured load curve at each instant of measurement should be zero on non-alert day. In other words, the reference load curve exactly predict the energy consumption pattern of a household on a non-alert day. However, the formulation of reference load curve is practically based on predictive methods. Therefore, it may either under-estimate or overestimate the energy consumption on a non-alert day. In other words, it suggests that practically, the difference between reference load curve and measured load curve at each instant of measurement is not zero (as illustrated in Figure IV.2). Since no technique fulfills the ideal condition of reference load curve, therefore it is required to choose a suitable method of formulating a reference load curve. A suitable method should formulate a reference load curve that should be as close as possible to measured load curve on non-alert days.

Figure IV. 2 A

 2 Figure IV.2A practical reference load curve

  3.b.ii and IV.3.b.iii to distinguishing the methods of formulation for green alert and orange alert. IV.3.a.ii Coefficient of determination (R2 Score 41 ) R-square is a statistical measure to find out about how close the data is to the fitted regression line. "The coefficient of determination can take values in the range (-∞, 1] according to the mutual relation between the ground truth and the prediction model" (Chicco et al., 2021). In complement to the energy difference indicator, the R 2 score suggests the suitability of the method. If the predicted value is equal to actual value, then R 2 score becomes 1. If a model disregards the input features and always predicts the expected value, then the R 2 score becomes 0.

Figure IV. 3

 3 Figure IV.3 illustrated an example of orange period of interest. It shows an example where the kernel density estimation curve is plotted for the 'n' previous weekdays for timeslot 6:30 PM. The consumption value with highest density on kernel density estimation curve is taken as the reference value on reference load curve for timeslot 6:30 PM. The same procedure is used for the green period of interest.

Figure IV. 3

 3 Figure IV.3 Formulation of reference load curve using Kernel Density Estimation

Figure IV. 4 represents

 4 measured load curve having minimum energy consumption between day 'D-1 and day 'D-5' as reference load curve on day 'D'. It can be mathematically represented as: 𝑀𝑖𝑛(𝐸 𝐷-1 : 𝐸 𝐷-5 ) = 𝑀𝑖𝑛 (∑ 𝑃 𝐷-

Figure IV. 4 Figure IV. 5 5 '

 455 Figure IV.4 Selecting the minimum energy measured load curve as reference load curve Using the same logic that the orange reference load curve should have minute positive error on non-alert days, the measured load curve having maximum energy consumption between past 5 weekdays is taken as reference load curve. Figure IV.5 represents measured load curve having maximum energy consumption between day 'D-1' and day 'D-5' as reference load curve on day 'D'. It can be mathematically represented as:

Figure IV. 6

 6 Figure IV.6 Green reference load curve formulation by taking average of measured load curve of day 'D-1' and day having minimum consumption between day 'D-2' and day 'D-5'Similar to the green reference load curve, an orange reference load curve is also formulated. However, for an orange reference load curve, an average is taken between the measured load curve of day 'D-1' and the measured load curve having maximum energy consumption between day 'D-2' and day 'D-5'.

Figure IV. 7

 7 Figure IV.7 Orange reference load curve formulation by taking average of measured load curve of day 'D-1' and day having maximum consumption between day 'D-2' and day 'D-5'

Figure IV. 8

 8 Figure IV.8 represents this technique for formulation of green reference load curve. e.g. to predict the reference value at 12:30 PM on day 'D', the measured energy consumption value at 12:30 PM during previous 5 weekdays is given as an input to the random forest prediction model. Similarly, the reference value at 1:00 PM, 1:30 PM, 2:00 PM, 2:30 PM and 3:00 PM are calculated to formulate the green reference load curve of day 'D'.

Figure IV. 8

 8 Figure IV.8 Random Forest Point to Point technique for creation of green reference load curve

Figure IV. 10

 10 Figure IV.10 Training the model and predicting the instance 6:30 PM for a day 'D'

Figure IV. 11

 11 Figure IV.11 and Figure IV.12are representing the techniques for green reference load curve and orange reference load curve respectively. Like random forest point to point, it should be noted that no additional parameter (e.g. temperature etc.) is added to the model, rather the historical consumption is only used for the prediction of reference load curve.

Figure IV. 11

 11 Figure IV.11 Random Forest Sequence to Sequence for creation of green reference load curve

Figure IV. 13

 13 Figure IV.13 Distribution of energy difference indicator for green reference load curves The R 2 score distribution boxplots for both random forest techniques (in Figure IV.14) also conforms the results of energy difference indicator in Figure IV.13. Though, the R 2 score of 2 nd and 3 rd method are close, yet the less dispersed data favors 3 rd method over

Figure IV. 14 -

 14 Figure IV.14 Distribution of R2 score for green reference load curves IV.3.c.ii Analysis of orange reference load curves The distribution of energy difference indicator for orange reference load curves can be observed in the Figure IV.15. Recalling the characteristics of a suitable orange load curve from section IV.3.a.i, it is expected from an orange reference load curve that the difference should be positive yet closer to zero. In the term of a boxplot, these characteristics can be translated in such a way that:

Figure IV. 15

 15 Figure IV.15 Distribution of energy difference indicator for orange reference load curves The R 2 score boxplot of kernel density estimation in Figure IV.16 suggests it to be the most suitable reference load curve formulating method, yet the result of energy difference indicator is contradictory to this deduction. The R 2 score distribution of 2 nd method is in the range of [-180,1], which is lower comparing to the others. The 2 nd lower R 2

Figure IV. 16

 16 Figure IV.16 Distribution of R 2 score for orange reference load curves

Figure IV. 17

 17 Figure IV.17 Indirect feedback using reference load curve for (a) Green Alert and (b) Orange Alert As compared to displays and bills, the web-based feedback is found more effective as a tool of feedback for load curtailment (Vassileva et al., 2012). On day 'D+2' of each alert day 'D' and subjecting to the provision of measured load curve of alert day, an image of reference load curve superposed on measured load curve (of alert day) is uploaded on the personal space of subjects of treated group on Etud-Elec website. A total number of 2141 feedback images are created and presented to subjects of treated group individually on their personal account on Etud-Elec site. This does not include the 2 nd green alert of 2 nd session,

  alerts) ∈ non-alert = Positive E flexibility,alert = Positive

E

  flexibility,alert | 6 𝑃𝑀 8 PM . It is recalled here that the difference is calculated between reference load curve and measured load curve, therefore a positive value indicates load curtailment whereas a negative value indicated no positive effort for load curtailment during the period of intervention. Based on the selected threshold, Figure IV.18 demonstrates the distribution of energy flexibility of the subjects who acted positively for most of alerts. The black dots represent the outliers in the distribution made for boxplot, whereas the blue dotted line represents the threshold value of 200 Wh. It can be observed that the median value of subject 399 and subject 404 lies beneath the threshold line, yet the outliers made the average energy flexibility of these subjects greater than the threshold value. Therefore, these subjects are considered as positively acting subjects against the nudge signal of load curtailment. In total, 20 (out of 79) subjects are found in this category.

Figure IV. 18

 18 Figure IV.18The energy flexibility of subjects who curtailed their load for most of the orange alerts

Figure IV. 19

 19 Figure IV.19 The energy flexibility of subjects who did not remain positive for most of the orange alerts Finally, Figure IV.20 demonstrates the distribution of energy flexibility of the subjects who remained within the range of [-200 Wh, 200Wh]. We can further split it in to 2 sub-categories. The first category represents those whose energy consumption remains stable during alert days as compared to non-alert days. In other words, we can say that the effect is zero or negligible. In this regard, those subjects are identified whose average energy flexibility is in the range of [-25Wh, 25Wh]. In total, 9 (out of 79) subjects are identified in this category. For the rest of the 42 subjects, the energy flexibility activity cannot be determined with respect to the fixed threshold.

Figure IV. 20

 20 Figure IV.20The energy flexibility of subjects whose effort of energy flexibility cannot be determined

Figure V. 1

 1 Figure V.1 Non-intrusive load monitoring (Parson, 2017) NILM is initially studied in 1992 through a detailed analysis of current and voltage of smart meter data (Hart, 1992). Since the mass deployment of smart meters, the research work in NILM become more significant. The state of the art discusses two aspects of NILM, namely; open-source datasets and the algorithms. These are briefly discussed below.

Figure V. 2

 2 Figure V.2 Schematic diagram of energy disaggregation of measured load curves on alert days

Figure V. 3

 3 Figure V.3 shows the frequency of committed appliances by the treated group for

Figure V. 3

 3 Figure V.3 Frequency of appliances committed for green alert The subjects of treated group are given a choice to change their default commitment before the alert day. However as already mentioned in section IV.4, it is observed that the subjects of treated group very rarely logged in to their personal account on Etude-Elec. A total of 8 modifications were made for all the alerts. The low number of modifications of indirect feedback makes it insufficient to statistically analyze the impact of modified commitment against default commitment. Therefore, for the sake of argument, we consider that the subjects of treated group stick to their default commitment reminded to them by the commitment SMS.

Figure V. 4

 4 Figure V.4Frequency of matched appliances in IRISE dataset with committed appliances

1 - 2 - 3 - 4 V. 6 .

 12346 Signal aggregate Error (SAE): It indicates the relative error of the total energy. 'r' denotes the total consumption of the appliance and 𝑟̂ the predicted total energy. Energy per day (EpD): This metric indicates the absolute error of the predicted energy used in a day, which is typically useful when the household users are interested in the total energy consumed in a period. Where 𝑒 = ∑ 𝑥 𝑡 𝑡 denotes the energy consumed in a day period and D is the total number of days. Normalized disaggregation error (NDE): This metric measures the normalized error of the squared difference between the prediction and the ground truth of the appliances. a Training and testing on same dataset (REFIT) REFIT dataset is sampled at 8 seconds. It consists of a total of 43 million data points in the training subset of washing machine. For processing 43 million data points, it requires a compatible hardware solution in memory and computing capacity. That's why 2 approaches are compared i.e. a complete dataset (of 43 million data points) processed on a remote server with parallelization capacity on GPU and a reduced subset for training on personal computer. The following results are observed regarding the time of trainingwithout and with GPU installed on the server. NVIDIA RTX6000 GPU is installed on the server machine. The results show that with a GPU installed on a computer machine, the time taken to train a model considerably reduce to a greater extent.

Figure V. 7

 7 Figure V.7 The comparison of peak energy consumption of washing machine in REFIT dataset with REDD dataset The models of washing machine and microwave that are previously trained on REFIT dataset is used to disaggregate data of REDD dataset. The reproduced results are found closer to the claimed results (D'Incecco et al., 2020) in both cases. The result of washing machine is presented in Figure V.8.

Figure V. 8

 8 Figure V.8 Energy disaggregation of washing machine via model trained on REFIT dataset and tested on RED house no. 3. Grey curve represents aggregated consumption, red curve represents the actual consumption of washing machine and blue curve represents the predicted curve of washing machine

Equation V. 5 -o

 5 Figure V.9 illustrates this scatter plot. -Starting from the highest value 'Xn', if the following equation holds, 𝑋 𝑛 -𝑋 𝑛-1 i.e. the distance between two highest consumption values is greater than the average distances of subsequent 5 values, than either; o Replace the outlier value with zero if the preceding and succeeding values in the unsorted vector are zeros. Replace the outlier value with the average of preceding and succeeding values in the unsorted vector, if both preceding and succeeding values are non-zero, otherwise replace the outlier with zero.

Figure V. 9

 9 Figure V.9 Detection of outliers in washing machine of a household in IRISE dataset V.7.b Predictive model creation based on IRISE dataset To train a predictive model of an appliance for energy disaggregation, the energy consumption data of respective appliance and aggregated data is collected from 'n' households in IRISE dataset. where 'n' represents the total number of houses in which the data of respective appliance is found. The data is split for training, validation and testing of model. The data of 'n-2' household is kept for training; one household is kept for validation and one household is kept for test. The open-source algorithm with sequence to point neural network architecture (D'Incecco et al., 2020) as discussed in section V.6 is used to train the models. The reproducibility of the claimed result of this algorithm is already verified in the same section. The same procedure is adapted for IRISE dataset as it was earlier used for the energy disaggregation of REFIT and REDD dataset.

Figure V. 10

 10 Figure V.10 Test result for dishwasher on IRISE house 997 at sampling rate of 30 minutes

  control. Contrary to this, the indirect energy flexibility offers price-based approaches that acts as extrinsic motivator for the consumers. However, there are certain factors that hinders the decision making of a residential consumer to implement price-based energy flexibility. It includes privacy, cognitive difficulty in understanding non-linear price system, information imperfection in the price-based signal, lack of motivation and uncertainty about potential consequences. Chapter I begins with a discussion on the changing landscape of energy mix and the constraints of traditional energy flexibility mechanisms, which leads us to the significance non-monetary energy flexibility mechanism and a brief introduction of implementation of its gist in this thesis.

(

  or near equally) effective to disaggregate the aggregated load curve of lower resolution. The impact of training NILM models on an ensemble of open-source datasets (for a fixed value of resolution) should be evaluated. With respect to the performance indicators, the impact of training on individual datasets should be compared with the training on ensemble of datasets.

F. 1 F. 2 F. 3 F. 4 F. 5 F. 6 F. 7 F. 8 F. 9

 123456789 Test result for hotplate on IRISE house 993 at sampling rate of 30 minutes Test result for electric heating on IRISE house 990 at sampling rate of 30 minutes Test result for electric oven on IRISE house 997 at sampling rate of 30 minutes Test result for electric cooker on IRISE house 902 at sampling rate of 30 minutes Test result for clothes drier on IRISE house 996 at sampling rate of 30 minutes Test result for dishwasher on IRISE house 997 at sampling rate of 30 minutes Test result for microwave oven on IRISE house 997 at sampling rate of 30 minutes Test result for tv on IRISE house 997 at sampling rate of 30 minutes Test result for washing machine on IRISE house 997 at sampling rate of 30 minutes RÉSUMÉ La transition énergétique vers les sources d'énergie renouvelables est essentielle pour atténuer le changement climatique. Mais, l'énergie renouvelable est intermittente en nature, ce qui peut entraîner à l'avenir une congestion du réseau lors des pics de consommation. Par conséquent, la flexibilité d'énergie est inévitable en complément de la gestion de l'offre et du stockage de l'énergie. Le secteur résidentiel est le plus gros consommateur d'énergie et il est donc essentiel de mettre en oeuvre la flexibilité d'énergie.En complément des approches existantes, nous présentons dans cette thèse une flexibilité "indirecte" non monétaire par la mise en oeuvre d'une expérience de science comportementale avec 175 ménages français. L'ensemble des "nudges" utilisés dans cette expérience n'oblige pas les participants à mettre en oeuvre la flexibilité énergétique pour des générées par des algorithmes prédictifs et envoyées par SMS à un groupe de ménages dans le but d'effectuer soit un transfert de charge, soit une effacement d'énergie. Les alertes sont complétées par des suggestions sur l'utilisation (ou non) d'appareils engagés pour mettre en oeuvre la flexibilité d'énergie. Après chaque alerte, le groupe reçoit un retour graphique dans lequel la courbe de charge mesurée par le compteur intelligent LINKY est superposée à une courbe de référence théorique propre à chaque ménage. En comparaison avec un groupe de contrôle, nous pouvons quantifier l'impact de ces signaux de "nudge" sur la consommation d'énergie du groupe traité. Enfin, un algorithme de désagrégation énergétique est présenté qui nous permettra de détecter les équipements utilisés lors de la flexibilité et ainsi faire une analyse de l'engagement des ménages à rechercher des appareils potentiels pour la flexibilité énergétique.

Table I .

 I 1 Short Term and Medium Term Targets set by EU to mitigate climate change (Amanatidis, 2019) ................................................................................................................ 5 Table I.2 French multi-year energy programs (Ministry of ecological transition and solidarity France, 2019) .................................................................................................. 6 Table I.3 Merits and Limitations of traditional energy flexibility ................................... 14 Table II.1 Attributes of dual system of thinking (J. S. B. T. Evans, 2008) .................... 27 Table II.2 The nudge based indirect energy flexibility experiments in households ...... 31 Table II.3 The nudges used in the experiment ............................................................... 33 Table II.4 Statistics of performance metric for each type of model ............................... 61 Table III.1 Number of households in each group with respect to each year ................... 65 Table III.2 Number of alert days with respect to each session and alert type................. 66

Table III

 III

	Table III.10	The assessment of impact of green alert on alert day as compared to non
	alert days	84
	Table III.11	The assessment of impact of orange alert on alert day as compared to non-
	alert days	86

.3 Basis of triggered green alerts ....................................................................... 68

Table III.4 Basis of triggered orange alerts ..................................................................... 68 Table III.5 Chi-square test of independence of various variables ................................... 70 Table III.6 The Mann-Whitney U Test for non-alert days of both types of sessions ...... 76 Table III.7 The Mann-Whitney U Test for green alert days of both sessions.................. 79 Table III.8 The Mann-Whitney U Test for orange alert days of both sessions .............. 80 Table III.9 The variables of ordinary least square test .................................................... 81

Table IV

 IV

.1 Ideal and practical case of energy difference indicator on non-alert days .... 95 Table IV.2 The choice of techniques to formulate reference load curve ......................... 96

Table IV .

 IV 3 Green and orange load curves to measure energy flexibility Eflexibility, alert 109 Table IV.4 The Mann-Whitney U Test for green alert days of both sessions using 𝑀𝑒𝑎𝑛𝑃𝐷 -1, 𝑖 , 𝑀𝑖𝑛𝑃𝐷 -2, 𝑖, 𝑃𝐷 -3, 𝑖, 𝑃𝐷 -4, 𝑖, 𝑃𝐷 -5, 𝑖 reference load curve in place of control group ..................................................................................................................... 110 Table IV.5 The Mann-Whitney U Test for orange alert days of both sessions using 𝑀𝑒𝑎𝑛𝑃𝐷 -1, 𝑖 , 𝑀𝑎𝑥𝑃𝐷 -2, 𝑖, 𝑃𝐷 -3, 𝑖, 𝑃𝐷 -4, 𝑖, 𝑃𝐷 -5, 𝑖 reference load curve in place of control group ...................................................................................................................... 111

Table IV

 IV

.6 Sensitivity analysis of thresholds of identifying responses .......................... 113

Table V.1 Some open source NILM datasets ............................................................... 120 Table V.2 Statistics for IRISE appliances at 10 minutes sampling .............................. 127 xii

Table V .

 V 3 Statistics for IRISE appliances at 30 minutes sampling.............................. 127 Table V.4 The length of data subsets in REFIT and REDD dataset ............................. 129 Table V.5 The comparison of model creation with and without GPU ......................... 130 Table V.6 Trained and tested on REFIT dataset at sample period of 8 seconds ......... 130

	Table V.7	The statistical characteristics of appliances in REFIT and REDD dataset ...131
	Table V.8	Trained on REFIT and tested on REDD dataset at sample period of 8 seconds
		132
	Table V.9	The length of data subsets for training the predictive model of each appliance
		135

Table V .

 V 10 The performance indicators for testing the predicted models .................... 135 Table V.11 The results with respect to performance indicators for data sampled at 10 minutes 136

Table V .

 V [START_REF] Bahar | Cross-Border Trade in Electricity and the Development of Renewables-Based Electric Power: Lessons from Europe[END_REF] The results with respect to performance indicators for data sampled at 30

	minutes	137
		xiii

Table I

 I

.1 Short Term and Medium Term Targets set by EU to mitigate climate change

[START_REF] Amanatidis | European policies on climate and energy towards 2020[END_REF]

Directives Targets Climate and Energy Package 2020 Climate and Energy Framework 2030

	GHG emission reduction target (below 1990 levels)	20%	40%
	Increase in share of energy consumption from renewable sources	20%	27%
	Improving energy efficiency to reduce the use of primary energy compared to forecasted levels	20%	27%

Table I

 I

	.2	French multi-year energy programs (Ministry of ecological transition and solidarity France,
		2019)
		Program	Energy Program	Energy Program
			2019-2023	2024-2028
	Targets
	GHG emission from energy consumption (as compared to 2016)	14%	30%
	Increase in share of energy consumption from renewable sources (as compared to 2017)	50%	100%
	Decrease in the primary energy consumption from fossil fuels (with respect to 2012)	20%	35%
	In line with the United Nations and European Union directives, France has also set
	targets for reducing GHG emissions and increasing the share of renewable energy for

electricity production. A study from ADEME 3 suggests 47% and 82% share of renewable energy in electricity mix until 2030 and 2050 respectively (ADEME, 2014). In this context, the installed capacity of wind and solar energy has been increasing in France, while the

  Time-of-use (TOU) is one of the price-based energy flexibility instruments. As indicated by the name, electricity price is set in accordance with the time of use. In TOU tariff, the electricity price is higher during peak hours and is lower during off-peak hours. It encourages the clients to consume less energy during peak hours for economic saving.

HC/HP (Heures Pleines/Heures Creuses) is a time of use tariff offered by the energy providers in France. More than 40% of the subscribers of EDF 11 are subscribed to this tariff.

The price of electricity is relatively cheaper for 8 hours of a day than the rest of the day. The time slots for these off-peak hours are defined by ENEDIS

12

and vary from city to city.

Real time pricing (RTP) is a price-based energy flexibility instrument, which is related to electricity price in real time on hourly basis. Real time pricing is in practice by some DSOs in Nordic countries. Critical peak pricing (CPP) is another price-based energy flexibility instrument. EDF has introduced two color coded critical peak pricing mechanisms and sends day ahead notifications to its residential subscribers.

Table I

 I

		.3		Merits and Limitations of traditional energy flexibility
	Characteristics		Direct Flexibility	Indirect Flexibility
				Merits
	Nature of signal		Incentive based	Price based
	Significant sectors		Industrial	Residential
	Type of response		Automated	Cognitive burden for the occupant
	Response time			Fast Response	Delayed Response
	Guarantee of load control	Yes	Possibly
	Anticipated	type(s)	of	Load curtailment	Load curtailment, load shifting,
	flexibility				Possibly valley filling
				Limitations
	Is it intrusive?			Yes	Possibly, but normally no
	Installation of switching	Yes	No
	devices needed?
	Target number of loads		A few loads, mainly electric
				heating for domestic users

Table II

 II

	.1	Attributes of dual system of thinking (J. S. B. T. Evans, 2008)
	Characteristics	System 1	System 2
		Unconscious	Conscious
		Implicit	Explicit
		Automatic	Controlled
	Consciousness	Rapid	Slow
		Low Effort	High Effort
		Default Process	Inhibitory
		Holistic, perceptual	Analytic, reflective
		Associative	Rule based
	Functionality	Domain specific Pragmatic	Domain general Logical
		Parallel	Sequential

Table II

 II

	.2	The nudge based indirect energy flexibility experiments in households

Reference Sample Size (Households) and Location Type of treatment Results

	(Brandon	&	120	Comparative norm,	4.6% decrease in energy usage
	Lewis, 1999)		UK	intervention
	(Schultz et al.,	270	Descriptive Norm,	Descriptive norm led to decrease of
	2007)		USA	Injunctive Norm	1.22 to 1.72kWh/day, boomerang effect
							disappears with inclusion of injunctive
							norm.
	(Costa, 2010)		35000	Descriptive Norm,	On average 2% reduction in electricity
			USA	Injunctive Norm	consumption (1.7% for conservatives,
							2.4% for liberals).
	(Allcott, 2011)		600000	Descriptive Norm,	On average 2% reduction in electricity
			USA	Injunctive Norm	consumption
	(Ayres et al.,	169000	Comparative		On average 1.2 to 2.1% reduction in
	2013)		USA	feedback			electricity consumption
	(Allcott & Rogers,	78887	Descriptive Norm,	Average 1-1.3% immediate reduction
	2014)		USA	Injunctive Norm	in consumption, discontinuing nudge
							leads to a decay of effect by 10-20% per
							year
	(Dolan	&	569	Social	descriptive	On average 6% reduction in electricity
	Metcalfe, 2015)		UK	norm			consumption
	(Graffeo et al.,	300	Social	descriptive	Social norm led to an intention to
	2015)		Israel	norm			conserve energy by 12.1 to 34.4% in
							treated group as compared to control
							group
	(Ito et al., 2018)	691	Moral Suasion		Moral suasion induced a short-run
			Japan				reduction in peak-hour electricity
							usage by 8%, economic incentive
							produced electricity consumption
							reduction of 14 to 17%.
	(Buckley	&	240	Comparison of nudge	Nudge does not bring welfare loss of
	Llerena, 2018)		France	and price based		individual or group, price based
							incentive does
	(Pratt	&	16149	Gamification		Percentage collective change of 4.34 to
	Erickson, 2020)	USA				11.98 % on treatment days w.r.t control
							days for households.
	(Jorgensen et al.,	143	Information	about	Reduction in peak consumption by
	2021)		Australia	peaks in consumption	12% to 20%.

  The orange alert is based on the day ahead announcement of PP1/PP226 . These days correspond to high electricity consumption determined by RTE. The day PP1 is based on the forecasted day ahead extreme meteorological condition whereas PP2 is based on forecasted day ahead network congestion.The announcement of PP1/PP2 days serves in capacity mechanism to ensure the supply of electricity for forecasted network congestion. The capacity mechanism has been regulated in France since January 2017 to address the growing energy demand while maintaining energy balance in the grid and ensuring secure supply during peak energy consumption. The energy generation companies and load curtailment operators are obliged to commit the availability of their production and load curtailing capacity during these peak periods. By default, RTE can attribute at maximum 15 PP1 days per year and 25 PP2 days

INFOCLIMAT API: https://www.infoclimat.fr/api-previsions-meteo.html?id=3014728&cntry=FR alert day 'D'. per year (ENOPTEA, 2018) in winter season. For identifying a potential day ahead orange alert 'D', the PP1/PP2 announcement is accessed via an API at 6 PM on day 'D-1'. If a PP1/PP2 day is declared, the program registers an orange alert for next day 'D'.

  For our study, we have selected profile coefficient curves of category Residential with sub-category RES1_BASE. This sub-category consists of the residential consumers which have contracted power ≤ 6 kVA. This is a classical contracted power which is being subscribed by around 7 out of 10 French households[START_REF] Pinon | 6 kVA : puissance de compteur la plus répandue en France[END_REF]). These households do not have electrical heating in their home. In our experiment, around 8 out of 10 households does not have electrical heating in their home. Therefore, this sub-category is close to the experimental case. Further, there exist 3 types of profile coefficient curves in this subcategory. All these curves are sampled at 30 minutes.-The prepared coefficient depicts a model about how an average customer of this category consumes electricity over time at normal temperature. The normal temperature is further described in section II.4.c.ii. The normal temperature is

33

Enedis Data Visualisation : https://data.enedis.fr/pages/accueil/?id=dataviz-profils established by RTE for a period of 10 years based on historical measured temperature of previous 30 years. Using the normal temperature, the values are adjusted for special days. The data is available for this coefficient since 26 December

2015;

-The adjusted coefficient depicts a model about how an average customer of this category consumes electricity over time at national temperature (instead of normal temperature). The national temperature is further described in section II.4.c.ii.

Using national temperature, the values are adjusted for special days and weather.

The data is available for this coefficient since 26 December 2015;

-The dynamic coefficient is the result of a more precise modelling. This coefficient directly take into account weather and special days (i.e. holidays). The data is available for this coefficient since 1 st July 2018 (Réseau de Transport d'Électricité France, 2020b).

  The national temperature is calculated by a weighted aggregation of measured temperature for 32 French cities where weather stations are installed. The data is sampled at 30 minutes. There are two types of temperatures available in this dataset.To forecast the French national load curve of residential sector, the measured national temperature dataset is taken. The data of this dataset is available until day 'D-2' every day.

	-The normal temperature corresponds to a weighted average of smoothened
	normalized temperature of the 32 weather stations. The normal temperatures per
	station are established for a period of 10 years based on temperatures observed over
	a period of 30 years;
	-The national temperature corresponds to a weighted average of smoothened
	measured temperatures of 32 weather stations on day 'D-2' (Réseau de Transport
	d'Électricité France, 2020b).
	II.4.c.iii	Weather Forecast API

Since we only have data until day 'D-2' from ENEDIS API of temperature and pseudo-radiation, we need to collect forecasted temperature of the 32 French weather stations. For this purpose, we use the API of Climacell to gather the forecasted temperature

Table II .

 II 4 Statistics of performance metric for each type of model

		Model
	Aggregate	Input	Temporal features	Temporal and profile coefficient features	Temporal, profile coefficient and temperature features
	Function
	Count (Days)		808	808	808
	Average (%)		9.49	6.43	4.11
	Standard Deviation (%)	6.77	3.48	2.08
	Minimum (%)		1.28	1.70	1.29
	Maximum (%)		31.45	21.95	14.34

Table III

 III

		.1	Number of households in each group with respect to each year
	Sr. No.	Type of group Year	Control Group	Treated Group	Total
	01	Year 1		79	96	175
	02	Year 2		76	87	163

Table III

 III

.2 Number of alert days with respect to each session and alert type Sr.

Table III

 III

			.3	Basis of triggered green alerts
	No. of Alert	Date	Average Evening Consumption Energy	Average Afternoon Consumption Energy	𝑪𝒐𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕 𝑮𝑨
				Green Session 1
	01	27 July, 2020		1.15	0.98	1.17
	02	25 August, 2020		1.19	1.02	1.17
	03	14 September, 2020		1.3	0.98	1.33
	04	17 September, 2020		1.26	0.92	1.37
	05	22 September, 2020		1.28	0.84	1.52
				Green Session 2
	06	14 June, 2021		1.18	0.98	1.20
	07	06 September, 2021		1.19	0.92	1.29

Table III

 III

	05	19 February, 2020	TEMPO
	06	27 February, 2020	EJP (South Zone)
	07	02 March, 2020	EJP (South Zone)
	08	04 March, 2020	EJP (South Zone)
	09	09 March, 2020	EJP (South Zone)
			Orange Session 2
	10	30 November, 2020	PP1/PP2
	11	02 December, 2020	PP1/PP2
	12	04 December, 2020	PP1/PP2
	13	07 December, 2020	PP1/PP2
	14	09 December, 2020	PP1/PP2
	15	18 December, 2020	PP1/PP2
	16	04 January, 2021	PP1/PP2
	17	06 January, 2021	PP1/PP2
	18	08 January, 2021	PP1/PP2
	19	11 January, 2021	PP1/PP2
	20	26 January, 2021	PP1/PP2
	21	11 February, 2021	PP1/PP2
		.4	Basis of triggered orange alerts
	No. of Alert	Date		Basis of alert
			Orange Session 1
	01	14 November, 2019	Manual Alert (Temperature < 0)
	02	20 December, 2019	PP1/PP2
	03	20 January, 2020	EJP (South Zone) 34
	04	06 February, 2020	EJP (South Zone)

Table III

 III

		20	Dishwasher		0.64	Yes
		21	Washing Machine	0.60	Yes
		22	Cloth Dryer		1	Yes
		23	Electric hotplates	0.50	Yes
		24	Radiator		1	Yes
				Energy Consumption during the weekend
		25	Pressing Iron		0.51	Yes
		26	Electric Oven		0.51	Yes
		27	Dishwasher		1	Yes
		28	Washing Machine	1	Yes
		29	Cloth Dryer		1	Yes
		30	Electric hotplates	0.63	Yes
		31	Radiator		1	Yes
			.5	Chi-square test of independence of various variables
	Sr. No.		Variable Name	p-value	Null hypothesis retained?
				Household characteristics
	01	Owner/Tenant		1	Yes
	02	House/Apartment		0.44	Yes
	03	Surface of household		0.24	Yes
	04	Year of construction		0.04	No
	05	Number of persons in the household	0.83	Yes
	06	Presence in the household at least 3 days per week (during weekdays)	0.52	Yes
	07	Types of electric heating		1	Yes*
				Presence of appliances
	08	Electric water heater		0.49	Yes
	09	Air Conditioning		0.82	Yes
	10	Heat Pump		0.99	Yes*
	11	PV panels			0.31	Yes*
	12	Double glazing of windows		0.16	Yes*
			Number of appliances in household
	13	Electric Oven		0.98	Yes*
	14	Dishwasher		0.57	Yes
	15	Washing Machine		0.30	Yes*
	16	Cloth Dryer		0.26	Yes
	17	Electric Vehicle		0.56	Yes*
			Energy Consumption during weekdays
	18	Pressing Iron		0.49	Yes
	19	Electric Oven		0.08	Yes

Table III

 III

		.8		The Mann-Whitney U Test for orange alert days of both sessions
			Control Group	Treated Group	Mann-Whitney U Test
	Sr. No	Alert Date	Obs	µC	бC	Ob s	µT	бT	Stat	p-valu e	Null hypo-thesis retained	µC -µT
						Orange Session 1
	1	14-11-2019	78	1.41 1.37	92	1.32	2.39	4212	0.97	Yes	0.09
	2	20-12-2019	77	1.25	1.4	90	0.92	0.74	3830	0.88	Yes	0.33
	3	20-01-2020	77	1.65 1.76	92	1.3	1.57	4174	0.98	Yes	0.35
	4	06-02-2020	79	1.41	1.6	91	1.11	1.31	4174	0.97	Yes	0.3
	5	19-02-2020	79	1.47 1.61	91	1.19	1.29	4071	0.93	Yes	0.28
	6	27-02-2020	78	1.26 1.33	91	1.23	1.32	3760	0.75	Yes	0.03
	7	02-03-2020	78	1.38 1.42	90	0.93	1.07	4450	1.00	Yes	0.45
	8	04-03-2020	79	1.22 1.37	91	0.98	1.06	4026	0.91	Yes	0.24
	9	09-03-2020	78	1.32 1.32	90	1.26	1.91	3992	0.94	Yes	0.06
						Orange Session 2
	10	30-11-2020	75	1.55 1.67	88	1.21	0.9	3449	0.69	Yes	0.34
	11	02-12-2020	75	1.69 1.71	89	1.4	1.06	3511	0.72	Yes	0.29
	12	04-12-2020	75	1.68 1.83	89	1.25	1.15	3801	0.94	Yes	0.43
	13	07-12-2020	75	1.52 1.46	89	1.4	1.43	3664	0.86	Yes	0.12
	14	09-12-2020	75	1.74 1.93	89	1.48	1.32	3468	0.67	Yes	0.26
	15	18-12-2020	75	1.33 1.37	88	1.19	1.05	3513	0.76	Yes	0.14
	16	04-01-2021	73	1.63 1.71	88	1.57	1.79	3506	0.84	Yes	0.06
	17	06-01-2021	74	1.74 1.89	88	1.61	2.11	3600	0.88	Yes	0.13
	18	08-01-2021	74	1.61 1.93	88	1.4	1.97	3798	0.97	Yes	0.21
	19	11-01-2021	74	1.86 2.11	88	1.75	1.92	3578	0.86	Yes	0.11
	20	26-01-2021	73	1.74 1.92	87	1.37	1.23	3420	0.80	Yes	0.37
	21	11-02-2021	74	1.53 1.71	87	1.23	1.44	3496	0.83	Yes	0.3
					Aggregated Result
		Aggregated	1595	1.52 1.66	187 6	1.29	1.51	16672 24	1.00	Yes	0.23
		The results for each alert of orange sessions are tabulated above in Table III.8. It is
	expected that the treated group curtail their load between 6 PM and 8 PM. Though, this can
	be observed by the positive difference in the mean of control group and treated group. Yet,
	the one-sided Mann-Whitney U Test validates it by retaining the null hypothesis, i.e. the
	distribution (of energy consumption data) of control group is stochastically greater than the
	distribution (of energy consumption data) of treated group. The null hypothesis is retained

for energy consumption of individual alerts as well as for aggregated energy consumption of all alerts. It suggests that globally and for individual alerts, the treated group implemented load curtailment for energy flexibility between 6 PM and 8 PM.

Table III

 III

			.9	The variables of ordinary least square test
	Sr. No.	Variable Name	Description of variable and origin of data	Type of Data
				Dependent Variable (Y)
				For each subject, the natural log of energy
	1	Ln (Period Energy)	consumption during the period of intervention on alert	Float (Decimal)
				day.
				Independent Variables (X)
	2	Ln (Day Energy)		For each subject, the natural log of energy consumption during whole alert day.	Float (Decimal)
	3	Sent SMS	The alert SMS sent to the subject of treated group on day 'D-1'. 1 for sent SMS and 0 for no sent SMS.	Boolean
			The average temperature in Grenoble on alert day. The
			unit of temperature is degree Celsius. The temperature
	4	Temperature	is recorded at GreEn-ER. GreEn-ER is a research	Float (Decimal)
			supporting living lab with microgrid interaction in
				Grenoble (Delinchant et al., 2016).

Table III .

 III [START_REF] Amanatidis | European policies on climate and energy towards 2020[END_REF] The assessment of impact of green alert on alert day as compared to non alert days

	Sr. No.	Alert Date	Obs	R 2	Condition Number	Difference on alert day	Log (Energy of day)	Temperature (°C)
				Session 1 -Green Alerts
	1	27-07-2020	1969	0.728	370.19	0.097*	1.067***	-0.012
	2	25-08-2020	2185	0.741	341.881	0.004	1.069***	-0.009*
	3	14-09-2020	3547	0.727	317.336	-0.017	1.062***	0.002
	4	17-09-2020	3716	0.721	319.488	0.004	1.06***	0.0
	5	22-09-2020	4063	0.717	320.414	-0.033	1.066***	0.002
				Session 2 -Green Alerts
	6	14-06-2021	6189	0.693	190.825	0.059	0.992***	-0.002

Table III .

 III [START_REF] Ayres | Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage[END_REF] The assessment of impact of orange alert on alert day as compared to non-alert days

	Sr. No.	Alert Date	Obs	R 2	Condition Number	Difference on alert day	Log (Energy of day)	Temperature (°C)
				Session 1 -Orange Alerts
	1	14-11-2019	1181	0,696	149.721	-0.09	1.058***	0.003
	2	20-12-2019	4333	0,672	146.638	-0.238***	1.032***	0.003
	3	20-01-2020	6934	0,693	129.438	-0.106	1.036***	0.005*
	4	06-02-2020	8375	0,693	129.682	-0.134*	1.026***	0.004
	5	19-02-2020	9462	0,689	131.124	-0.131*	1.018***	0.005**
	6	27-02-2020	10082 0,687	131.659	-0.118	1.016***	0.004*
	7	02-03-2020	10351	0,688	132.009	-0.24***	1.017***	0.004*
	8	04-03-2020	10441	0,687	132.036	-0.172**	1.016***	0.004*
	9	09-03-2020	10798	0,689	132.263	-0.091	1.016***	0.003
				Session 2 -Orange Alerts
	10	30-11-2020	2536	0,702	159.553	-0.065	0.983***	0.007*
	11	02-12-2020	2625	0,701	157.091	-0.045	0.982***	0.007*
	12	04-12-2020	2713	0,705	157.56	-0.21***	0.986***	0.007*
	13	07-12-2020	2889	0,708	150.887	-0.109	0.986***	0.009***
	14	09-12-2020	2976	0,706	150.29	-0.052	0.978***	0.009***
	15	18-12-2020	3672	0,704	145.66	-0.192***	0.981***	0.008***
	16	04-01-2021	5060	0,726	134.413	0.018	1.008***	0.01***
	17	06-01-2021	5148	0,727	133.575	-0.046	1.009***	0.01***
	18	08-01-2021	5235	0,725	132.967	-0.107	1.006***	0.01***
	19	11-01-2021	5409	0,727	131.205	0.041	1.007***	0.01***

Table IV .

 IV 1 Ideal and practical case of energy difference indicator on non-alert days

	Sr. No.	Case	Graphical Illustration	Sign of energy difference indicator
	01	Ideal Case		Zero
	02	Practical underestimation		Negative
	03	Practical overestimation		Positive

Table IV

 IV

		.2	The choice of techniques to formulate reference load curve
	Sr. No.		Technique	Remarks
			Classical method
	01	Kernel Density Estimation	This technique serves to find the most probable value of energy consumption.
			Naïve methods
	02	𝑀𝑖𝑛(𝐸 𝐷-1 : 𝐸 𝐷-5 ) 𝑀𝑎𝑥(𝐸 𝐷-1 : 𝐸 𝐷-5 )	These methods are explored following kernel density estimation method. These methods are
		𝑀𝑒𝑎𝑛[𝑃 𝐷-1,𝑖 , 𝑀𝑖𝑛(𝑃 𝐷-2,𝑖 , 𝑃 𝐷-3,𝑖 , 𝑃 𝐷-4,𝑖 , 𝑃 𝐷-5,𝑖 )]	easily implementable and require less amount of
		𝑀𝑒𝑎𝑛[𝑃 𝐷-1,𝑖 , 𝑀𝑎𝑥(𝑃 𝐷-2,𝑖 , 𝑃 𝐷-3,𝑖 , 𝑃 𝐷-4,𝑖 , 𝑃 𝐷-5,𝑖 )]	near past energy consumption data of alert day
	03			'D' as compared to classical method. Since these methods only takes the energy consumption
				data of previous 5 weekdays, therefore they also
				takes into account the variation in energy

Table IV .

 IV 3 Green and orange load curves to measure energy flexibility E flexibility,alert

Table IV .

 IV [START_REF]Climat, Air et énergie[END_REF] The Mann-Whitney U Test for green alert days of both sessions using 𝑀𝑒𝑎𝑛[𝑃 𝐷-1,𝑖 , 𝑀𝑖𝑛(𝑃 𝐷-2,𝑖 , 𝑃 𝐷-3,𝑖 , 𝑃 𝐷-4,𝑖 , 𝑃 𝐷-5,𝑖 )] reference load curve in place of control groupThe results of TableIV.4 are found considerably different than the results of Table III.7. A comparison of control group with treated group (in Table III.7) suggests that the distribution of control group is statistically less than the distribution of treated group for 5 th

	S		Reference load curve	Treated Group	Mann-Whitney U Test
	r. N o	Alert Date	Obs	µr	бr	Ob s	µT	бT	Stat	p-value	Null hypo-thesis	µr -µT
											retained
						Green Session 1
	1	27-07-2020	70	0.71 0.67	71	0.99	1.21	2296	0.22	Yes	-0.28
	2 25-08-2020	7	0.66	0.45	34	0.81	0.64	110	0.38	Yes	-0.15
	3 14-09-2020	82	0.71 0.76	88	0.79	0.83	3607	0.5	Yes	-0.08
	4 17-09-2020	88	0.71 0.54	88	0.77	0.68	3894	0.53	Yes	-0.06
	5	22-09-2020	88	8 0.6	0.49	88	0.79	0.96	4053	0.7	Yes	-0.11
						Green Session 2
	6 14-06-2021	85	0.74	0.62	85	1.01	1	3328	0.19	Yes	-0.27
	7 06-09-2021	-	-	-	-	-	-	-	-	-	-
						Aggregated Result
		Aggregated	420	0.71 0.62	454	0.86	0.93	93171 0.28	Yes	-0.15

Table IV .

 IV [START_REF] Al Shaqsi | Review of energy storage services, applications, limitations, and benefits[END_REF] The Mann-Whitney U Test for orange alert days of both sessions using 𝑀𝑒𝑎𝑛[𝑃 𝐷-1,𝑖 , 𝑀𝑎𝑥(𝑃 𝐷-2,𝑖 , 𝑃 𝐷-3,𝑖 , 𝑃 𝐷-4,𝑖 , 𝑃 𝐷-5,𝑖 )] reference load curve in place of control group

			Reference load curve	Treated Group	Mann-Whitney U Test
	Sr. No	Alert Date	Ob s	µr	бr	Ob s	µT	бT	Stat	p-valu e	Null hypo-thesis retained	µr -µT
						Orange Session 1
	1	14-11-2019		1.68	2.44	91	1.32	2.41	4968	0.99	Yes	0.36
	2	20-12-2019		1.03	1.04	90	0.92	0.74	4415	0.85	Yes	0.11
	3	20-01-2020		1.24	1.35	92	1.3	1.57	4317	0.59	Yes	-0.06
	4	06-02-2020		1.37	1.4	91	1.11	1.31	4896	0.98	Yes	0.26
	5	19-02-2020		1.29	1.11	91	1.19	1.29	4680	0.94	Yes	0.1
	6	27-02-2020		1.19	1.17	91	1.23	1.32	4307	0.73	Yes	-0.04
	7	02-03-2020		1.19	1.22	90	0.93	1.07	4828	0.99	Yes	0.26
	8	04-03-2020		1.2	1.4	91	0.98	1.06	4698	0.94	Yes	0.22
	9	09-03-2020		0.88	1.32	90	1.26	1.91	3501	0.06	Yes	-0.38
						Orange Session 2
	10	30-11-2020		1.39	1.1	88	1.21	0.9	4236	0.86	Yes	0.18
	11	02-12-2020		1.53	1.47	89	1.4	1.06	4073	0.63	Yes	0.13
	12	04-12-2020		1.37	1.3	89	1.25	1.15	4204	0.76	Yes	0.12
	13	07-12-2020		1.29	1.19	89	1.4	1.43	3886	0.42	Yes	-0.11
	14	09-12-2020		1.31	1.11	89	1.48	1.32	3814	0.34	Yes	-0.17
	15	18-12-2020		1.47	1.4	88	1.19	1.05	4410	0.94	Yes	0.28
	16	04-01-2021		1.53	1.68	88	1.57	1.79	3992	0.64	Yes	-0.04
	17	06-01-2021		1.64	1.54	88	1.61	2.11	4297	0.9	Yes	0.03
	18	08-01-2021		1.62	1.9	88	1.4	1.97	4301	0.9	Yes	0.22
	19	11-01-2021		1.46	2.09	88	1.75	1.92	3361	0.06	Yes	-0.29
	20	26-01-2021		1.37	1.26	87	1.37	1.23	3818	0.54	Yes	0
	21	11-02-2021		1.4	1.63	87	1.23	1.44	4036	0.78	Yes	0.17
						Aggregated Result
	Aggregated	187 4	1.35	1.49	187 5	1.29	1.51	18667 90	1	Yes	0.06

  .8; albeit having more observations of reference load curve in Table IV.5 as compared to observations of control group in Table III.8. Yet, the results of one-sided Mann-Whitney U Test in Table IV.5 are found closer to the results of Table III.8. The technique of random forest sequence to sequence is found to be the best technique to formulate a reference load curve for load curtailment (i.e. orange alerts) in our analysis presented in section IV.3.c.ii. The results of this technique is presented in Table D.2 in appendix D The value of 'µr -µT' for certain alerts in Table D.2 is negative, although the one-sided Mann-Whitney U Test results show that the null hypothesis is retained for these alerts. Overall, the results of Table IV.5 and table D.2 in comparison with Table III.8

Table IV

 IV

	07	Positive			E flexibility,alert | 6 𝑃𝑀 8 PM > 150 Wh	25
	08	Not positive		E flexibility,alert | 6 𝑃𝑀 8 PM < -150 Wh	10
	09	Cannot be determined	-150 Wh ≤ E flexibility,alert | 6 𝑃𝑀 8 PM ≥ 150 Wh	44
			Lower threshold = -200Wh, Upper threshold = 200Wh
	10	Positive			E flexibility,alert | 6 𝑃𝑀 8 PM > 200 Wh	20
	11	Not positive		E flexibility,alert | 6 𝑃𝑀 8 PM < -200 Wh	8
	12	Cannot be determined	-200 Wh ≤ E flexibility,alert | 6 𝑃𝑀 8 PM ≥ 200 Wh	51
				.6	Sensitivity analysis of thresholds of identifying responses
		Sr. No.	Response		Criterion	Number of identified subjects
			Lower threshold = -50Wh, Upper threshold = 50Wh
		01	Positive		E flexibility,alert | 6 𝑃𝑀 8 PM > 50 Wh	36
		02	Not positive		E flexibility,alert | 6 𝑃𝑀 8 PM < -50 Wh	27
		03	Cannot be determined		-50 Wh ≤ E flexibility,alert | 6 𝑃𝑀 8 PM ≥ 50 Wh	16
			Lower threshold = -100Wh, Upper threshold = 100Wh
		04	Positive		E flexibility,alert | 6 𝑃𝑀 8 PM > 100 Wh	29
		05	Not positive		E flexibility,alert | 6 𝑃𝑀 8 PM < -100 Wh	18
		06	Cannot be determined		-100 Wh ≤ E flexibility,alert | 6 𝑃𝑀 8 PM ≥ 100 Wh	32
			Lower threshold = -150Wh, Upper threshold = 150Wh

Table V

 V

			2017)			UK		20	2 yrs	8 sec
			Individual		household
		05	electric consumption Data Set power	France	1	4 yrs	1 min
			(Hebrail & Berard, 2012)
		06	AMPDs (Makonin et al., 2016)	Canada	1	2 yrs	1 min
					.1	Some open source NILM datasets
	Sr. No.	Name of dataset		Country of origin	Number of households	Duration of data (yrs=years, m=months)	Sampling period (min=minutes, sec=seconds)
	01	REDD Johnson, 2011) (Kolter	&	USA		6	Several months	1 sec
	02	DEDDIAG (Wenninger et al., 2021)	Germany		15	3.5 yrs	1 sec
	03	UKDALE(J. Kelly & Knottenbelt, 2015)	UK		5	> 1yr	6 sec

  Various deep learning architectures are also extensively researched for the purpose of non-intrusive load monitoring. In this regard, various architectures of auto-encoder (Bonfigli et al., 2018; Tsai et al., 2018), recurrent neural networks (J. Kim et al., 2016; Le et al., 2016) and convolutional neural network (D. Kelly, 2016; Penha & Castro, 2017) are studied.The application of recurrent neural network is also compared with convolutional neural network (Pedro Paulo Marques do Nascimento, 2016). Most of the research in the field of energy disaggregation yields individual model for each appliance. However, the research is also done in order to make a unique model for disaggregation of multiple appliances[START_REF] Faustine | UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM[END_REF]. However, this research is limited.

	are among
	optimization techniques studied for NILM.
	-Probability Statistics: Hidden Markov model is a widely studied probability
	statics technique for NILM (H. Kim et al., 2011; X. Wang et al., 2018; Zhong et al.,
	2014). Among probability statics techniques, Viterbi algorithm (Zeifman, 2012) and
	Bayesian approach (Srinivasarengan et al., 2013) are also studied.
	-Graph Signal Processing: NILM research work is also explored using graph
	signal processing (He et al., 2018; Stankovic et al., 2014; Zhao et al., 2015, 2016).
	-Machine Learning: The machine learning techniques are used for pattern
	recognition in NILM. Random Forest (Y. Liu et al., 2021; Wu et al., 2019) and
	support vector machine (Moradzadeh et al., 2020; Schirmer & Mporas, 2019) are
	among the machine learning techniques that are studied.

-Deep Learning:

Table V

 V The data of energy consumption in our (EXPESIGNO) experiment is collected by LINKY smart meter, which is sampled at 30 minutes. To keep the homogeneity in the sampling rate of IRISE with EXPESIGNO, the power consumption data of IRISE appliances are down sampled at 30 minutes by taking the mean value for half hourly timestamps. The statistics of IRISE appliances are calculated and presented in TableV.[START_REF]Un mix électrique 100% renouvelable? Analyses et optimisations[END_REF].By implementing down sampling (by taking average) on the data, it is common to find the maximum value of energy consumption different than the maximum value of original sampling. It is because the maximum value of original sampling is averaged with its adjacent values, therefore it is modified. However, it is expected that the mean consumption should remain same irrespective of the down-sampling. By comparing TableV.2 and TableV.3, it is found out that the mean values vary to a considerable extent for hotplate, dishwasher and microwave. The mean values are highlighted in yellow in the tables. A possible explanation of this variation lies in the consumption pattern of these appliances. These appliances have very short time of peak consumption, while the rest of the time they have low consumption during their cycle of operation. Therefore, the mean value varies for these appliances upon down sampling.To train and test models of energy disaggregation on IRISE dataset, an open-source algorithm known as Transfer-NILM45 is used. It uses sequence to point46 architecture of neural networks. This algorithm deals with the transfer learning of energy disaggregation. The transferability of model is tested for appliance level (i.e. model trained on one appliance is used to do energy disaggregation on another appliance) and cross dataset level (i.e. model of an appliance trained on one dataset and tested on the same appliance from another dataset) (D'Incecco et al., 2020).Before training models on IRISE dataset using transfer-NILM algorithms, the claimed results in the research article should be verified. For this purpose, the results are reproduced for washing machine and microwave oven on the open-source REFIT 47 dataset (already mentioned in TableV.1). To check the cross-dataset results, the training is done on REFIT dataset whereas it is tested on REDD 48 dataset (already mentioned in TableV.1). The length of data subsets is given in TableV.4. The data of REDD is down sampled and the same length is cropped as of REFIT dataset. Previously, we have observed that the IRISE

		.2		Statistics for IRISE appliances at 10 minutes sampling
	Appliance	Count	ON (%)	OFF (%)	Mean (W)	Standard Deviation (W)	Median (W)	Max (W)	Average window size
	Aggregated	5103252	x		x		176		365	76	x	x
	Hotplate	12817	2		98		109		87	88	604	3
	Electric Heating	594695	44	56		151		187	90	10924	23
	Electric Oven	137399	9		91		56		93	21	4246	29
	Electric Cooker	179874	11	89		52		95	3	1259	9
	Clothes drier V.6 Reproducibility 102373	5	of	95	an	137	138 open-source 103	1287 energy 11
	Dishwasher Microwave disaggregation algorithm 85566 3 153982 4	97 96		174 39		161 49	119 23	628 587	5 3
	TV	1703741	24	76		11		16	9	856	13
	Washing Machine	301406	6		94		73		107	22	5866	7
		Table V.3		Statistics for IRISE appliances at 30 minutes sampling
	Appliance	Count	ON (%)	OFF (%)	Mean (W)		Standard Deviation (W)	Median (W)	Max (W)	Average window size
	Aggregated	1716530	x		x	174		293	78	x	x
	Hotplate	6759	4		96	69		61	49	434	3
	Electric Heating dataset has a default resolution of 10 minutes, whereas to bring homogeneity with the 211074 47 53 142 174 77 3641 35
	Electric Oven 48718 resolution of LINKY data, IRISE dataset is resampled at 10 minutes. Similarly, to bring 10 90 52 77 22 1421 11
	Electric Cooker homogeneity in the resolution of REFIT (by default at 8 seconds) and REDD (by default at 72977 14 86 43 73 6 623 5
	Clothes drier	39331	5		95	119		122	87	1082	5
	Dishwasher	39754	5		95	126		105	126	469	3
	Microwave	105928	8		92	19		29	11	355	3
	TV	647996	27	73	9		10	8	856	7
	Washing Machine	129679	8		92	57		74	22	1955	3

Table V

 V

		.4	The length of data subsets in REFIT and REDD dataset
			REFIT and REDD data sampled at 8 seconds
	Sr. No.	Appliance	Training Subset	Validation Subset	Test Subset
			(Million)	(Million)	(Million)
	01	Microwave	18.22	5.43	6.76
	02	Washing Machine	43.47	5	6

Table V

 V The purpose of energy disaggregation (as stated earlier) is to disaggregate the smart meter measured load curve into the consumption of respective appliances. Here, we reproduce the results to test that the disaggregated load curve of the appliance recognizes well the state of the appliance by comparing it to the ground truth value of the appliance. Not much difference is spotted for MAE and NDE, however difference in SAE and EpD is observed.Overall, it can be observed that the results are close. It demonstrates that the results are reproduced as they were claimed in (D'Incecco et al., 2020). Energy disaggregation of washing machine via model trained on REFIT dataset and tested on REFIT house no. 8. Grey curve represents aggregated consumption, red curve represents the actual consumption of washing machine and blue curve represents the predic

			.5	The comparison of model creation with and without GPU
	Sr. No.	Parameter Figure V.6		Without GPU		With GPU
	01	Appliance			Washing Machine		Washing Machine
	02	Length of training dataset used	5 (out of 43) million	43 (out of 43) million
	03	Length of validation dataset used	5 (out of 5) million		5 (out of 5) million
	04	Epochs			5			10 (early stopping at 5 th epoch)
	05	Time taken		Around 13 hours		Around 6 hours
		Here the results of washing machine (WM) and microwave oven (MO) are
	presented. The results of washing machine are graphically presented in Figure V.6 below.
		Table V.6	Trained and tested on REFIT dataset at sample period of 8 seconds
			Results of (D'Incecco et al., 2020)		Reproduced results
	Appliance	MAE	SAE	EpD (Wh)	NDE	MAE	SAE	EpD (Wh)	NDE
	Microwave Oven	12.66	0.17	95.78	0.71	9.5	0.007	105.07	0.71
	Washing Machine	16.85	2.61	319.11	0.54	32.09	0.16	418.16	0.58

V.6.

b Training on REFIT and testing on REDD dataset: transfer learning

  The REDD dataset is sampled at 1 seconds for smart meter, whereas 3 seconds for appliances. To keep homogeneity with REFIT, the REDD dataset is resampled to 8 seconds.It is significant to look at the statistical values of appliances in TableV.7 before reproducing results. Since the same appliance in different datasets does not belongs to same brand and same model, therefore their energy consumption pattern is different. Besides, the appliances are not operated all the time, therefore they have different time of utilization. By looking at Table V.7, it can be observed that the appliances in REDD dataset have low utilization time (i.e. ON time), yet high mean and standard deviation value as compared to the appliances in REFIT dataset. It is illustrated in Figure V.7 as well, in which the peak consumption of washing machine for REDD dataset is higher than that of REFIT dataset.

Table V

 V

	.7	The statistical characteristics of appliances in REFIT and REDD dataset
	Appliance	On (%)	OFF (%)	Mean (W)	Standard Deviation (W)
		REFIT dataset (sampled at 8 seconds)
	Microwave Oven	6.6	93.4	9	92
	Washing Machine	10.94	89.06	147	478
		REDD dataset (sampled at 8 seconds)
	Microwave Oven	2.03	97.97	358	613
	Washing Machine	0.69	99.31	2282	743

Table V .

 V [START_REF] Allcott | Social norms and energy conservation[END_REF] Trained on REFIT and tested on REDD dataset at sample period of 8 seconds

		Results of (D'Incecco et al., 2020)		Reproduced results
	Appliance	MAE	SAE	EpD (Wh)	NDE	MAE	SAE	EpD (Wh)	NDE
	Microwave Oven	23.10	0.36	208	0.71	23.81	0.30	221.03	0.76
	Washing Machine	36.83	0.74	750.85	0.91	35.24	0.87	778.04	0.96

Table V .

 V [START_REF] Allcott | The Short-Run and Long-Run Effects of Behavioral Interventions: Experimental Evidence from Energy Conservation[END_REF] The length of data subsets for training the predictive model of each appliance For the test of predictive models, the performance indicators are tabulated below in Table V.10. It is pertinent to discuss here that these performance indicators are different than the one used in section V.6. The purpose of former performance metrics is to verify the reproduction of the results by comparing it with the claimed results in the scientific publication of (D'Incecco et al., 2020). These performance metrics are given in the scientific publication and served to calculate the error in the prediction of energy consumption.

			Data sampled at 10 minutes	Data sampled at 30 minutes
	Sr. No.	Appliance	Training Subset	Validation Subset	Test Subset	Training Subset	Validation Subset	Test Subset
			(Million)	(Million)	(Million)	(Million)	(Million)	(Million)
	01	Hotplate	0.387	0.054	0.053	0.126	0.018	0.018
	02 Electric Heating	1.083	0.054	0.054	0.361	0.018	0.018
	03 Electric Oven	1.304	0.054	0.053	0.435	0.018	0.018
	04 Electric Cooker	1.458	0.054	0.053	0.488	0.018	0.018
	05 Clothes drier	1.625	0.054	0.054	0.542	0.018	0.018
	06 Dishwasher	2.485	0.054	0.053	0.832	0.018	0.018
	07 Microwave	3.624	0.054	0.053	1.211	0.018	0.018
	08 TV	4.157	0.054	0.053	1.391	0.018	0.018
	09 Washing Machine 4.490	0.054	0.053	1.502	0.018	0.018

Table V

 V

			F1 score is a harmonic mean of precision 49 and recall 50 . In the terms
			of the above 4 performance indicators, the F1 score is mathematically
	05	F1 Score	given as:
			𝐹 1 = 2 ×	𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑟𝑒𝑐𝑎𝑙𝑙 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑟𝑒𝑐𝑎𝑙𝑙	=	𝑇𝑃 + {	1 2	𝑇𝑃 × (𝐹𝑃 + 𝐹𝑁)}
	V.7.
		.10	The performance indicators for testing the predicted models
	Sr. No.	Performance Indicator				Remarks
	01	True Positive (TP)	Actual state : ON Predicted state : ON
	02	True Negative (TN)	Actual state : OFF Predicted state : OFF
	03	False Positive (FP)	Actual state : OFF Predicted state : ON
	04	False Negative (FN)	Actual state : ON Predicted state : OFF

c The results of energy disaggregation in terms of performance indicators

  Initially the median value (of measured consumption) is taken as a threshold for disaggregation results. These median values are given in TableV.2 and Table V.3 for resolution of 10 minutes and 30 minutes respectively. These median values are greater in value than 10W for the appliances except for electric cooker and TV. The threshold value brings sensitivity to results therefore it must be carefully selected by looking at the noise of the prediction. By looking at the results graphically, it is observed that the disaggregated curve does not have same amplitude as the measured curve of the appliance. Besides, it is also observed graphically that the disaggregated curve is found oscillatory within a specific amplitude around OX axis. Therefore, the threshold is set to 10W for all the appliances to suppress the effect of noise in the prediction on the results. The only exceptions in this regard are electric cooker and TV for which the threshold is set to 5W.

Table V .

 V [START_REF] Ayres | Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage[END_REF] The results with respect to performance indicators for data sampled at 10 minutes

		Washing Machine	10		3.62 96.38 1.73	90.88	5.72	1.67	0.32
					True Values		Performance Indicators
	Sr. No.	Appliance	Threshold (W)	ON (%)	OFF (%)	True Positive (TP)	True Negative (TN)	False Positive (FP)	False Negative (FN)	F1 Score
							(%)	(%)	(%)	(%)
	01	Hotplate	10		2	98	1	93	5	1	0.17
	02	Electric Heating	10		13	87	12	75	12	1	0.65
	03	Electric Oven	10		3	97	3	90	7	0	0.45
	04	Electric Cooker	5		4	96	2	83	13	2	0.21
	05	Clothes drier	10		0.44 99.56 0.38	96.49	3.07	0.05	0.2
	06	Dishwasher	10		2	98	2	94	4	0	0.5
	07	Microwave	10		3	97	1	95	2	2	0.33
	08	TV	5		27	73	24	53	20	3	0.68
				𝑇𝑃
				𝑇𝑃+𝐹𝑁

49

Precision is a ratio of true positives and the actual values. In terms of true positive (TP) and false positive (FP), the precision is mathematically given as 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 = 𝑇𝑃 𝑇𝑃+𝐹𝑃

50

Recall is a ratio of true positives and the predicted values. In terms of true positive (TP) and false negative (FN), the recall is mathematically given as 𝑅𝑒𝑐𝑎𝑙𝑙 =

Table V .

 V [START_REF] Bahar | Cross-Border Trade in Electricity and the Development of Renewables-Based Electric Power: Lessons from Europe[END_REF] The results with respect to performance indicators for data sampled at 30 minutes

				True Values		Performance Indicators
	Sr. No.	Appliance	Threshold (W)	ON (%)	OFF (%)	True Positive (TP)	True Negative (TN)	False Positive (FP)	False Negative (FN)	F1 Score
						(%)	(%)	(%)	(%)
	01 Hotplate	10	2.41 97.59 0.88	94.04	3.55	1.53	0.26
	02	Electric Heating	10	18	82	17	58	24	1	0.58
	03	Electric Oven	10	4	96	4	90	6	0	0.57
	04	Electric Cooker	5	4	96	3	78	19	1	0.22
	05	Clothes drier	10	0.54 99.46 0.45	94.36	5.10	0.08	0.15
	06 Dishwasher	10	3	97	3	91	6	0	0.5
	07 Microwave	10	6.68 93.32 1.25	91.63	1.68	5.43	0.26
	08 TV	5	29	71	27	45	26	2	0.66
	09	Washing Machine	10	4	96	2	88	8	2	0.29

It can be observed in Table

V

.11 that the ON (percentage) is very less as compared to OFF (percentage). The percentage value of false positive (FP) predictions is found greater (or equal to) the true positive (TP) predictions. It means that besides accurately predicting

  To keep energy balance in a future energy mix which is predominantly energized by renewable energy sources, it is inevitable to implement the different modes of energy flexibility. Residential sector (being the highest consumer of electricity) remained the focal point of demand response. It still remains a potential sector to implement energy flexibility.

Feedback is another important aspect of demand response. It can be either direct (i.e., in real time) or indirect (after the period of consumption). Direct energy flexibility is an incentive-based approach to switch loads at remote end. It is significant for industrial sector; however, it is not desirable for residential consumers since it is intrusive. Besides, there exist concerns among consumers regarding privacy and balanced use of direct load

  Nudge provides indirect suggestions to influence the decision making, whereas green nudge is an extension of nudge technique which is attributed to environmental cause. A review of nudges used in the field of energy worldwide is also presented. Most of these nudges used either a self-comparative or social comparative norm. In contrast to this, the architecture of a field experiment in residential sector using green nudges is presented. This non-monetary field experiment is performed with 175 French households (within the constraints of GDPR); categorized in control group and treated group. Two types of interventions are conceived i.e. orange alerts for load curtailment and green alerts for load shifting. The nudge signals (assisting with the curious questions) in the form of SMS are sent to the treated group for both types of interventions.

  This variable concerns the status of households regarding their housing (owner or tenant).

	ANNEX B
	B.1 Household Characteristics
	B.1.i Owner/Tenant
	Table B.1:	Distribution of household status according to group
			Control Group	Treated Group
	Status	Number of	Percentage	Number of	Percentage
			subjects	(%)	subjects	(%)
	Owner	62		78.5	74	77.9
	Tenant	17		21.5	21	22.1
	Total	79		100	95	100
	𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙𝑖𝑡𝑦 𝑜𝑓 𝑎𝑝𝑝𝑙𝑖𝑎𝑛𝑐𝑒 =	𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑡𝑖𝑚𝑒𝑠 𝑡ℎ𝑒 𝑐𝑢𝑟𝑡𝑎𝑖𝑙𝑚𝑒𝑛𝑡 (𝑜𝑟 𝑠ℎ𝑖𝑓𝑡𝑖𝑛𝑔) 𝑜𝑓 𝑎𝑝𝑝𝑙𝑖𝑎𝑛𝑐𝑒 𝑖𝑠 𝑖𝑑𝑒𝑛𝑡𝑖𝑓𝑖𝑒𝑑 𝑑𝑢𝑟𝑖𝑛𝑔 𝑝𝑒𝑟𝑖𝑜𝑑 𝑜𝑓 𝑎𝑙𝑒𝑟𝑡 𝑇𝑜𝑡𝑎𝑙 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑠𝑢𝑏𝑗𝑒𝑐𝑡𝑠 𝑤ℎ𝑜 𝑐𝑜𝑚𝑚𝑖𝑡𝑡𝑒𝑑 𝑡ℎ𝑖𝑠 𝑎𝑝𝑝𝑙𝑖𝑎𝑛𝑐𝑒	* 100

Table B . 2 :

 B2 Distribution of the type of housing of households according to the groups

		Control Group	Treated Group
	Status	Number of	Percentage	Number of	Percentage
		subjects	(%)	subjects	(%)
	House	36	54.4	50	52.6
	Apartment	43	45.6	45	47.4
	Total	79	100	95	100

Table B .

 B 3: Distribution of liveable area by group

		Control Group	Treated Group
	Surface Area	Number of	Percentage	Number of	Percentage
		subjects	(%)	subjects	(%)
	[0-50[	5	6.3	9	9.4
	[50-100[	45	57.0	51	53.7
	[100-150[	24	30.4	24	25.3
	[150-400]	5	6.3	11	11.6
	Total	79	100	95	100

Table B .

 B 4:Distribution of the period of construction of the household according to the groups

	Year of Construction		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	Avant 1949	9	11.4	7	7.4
	1949-1961	2	2.5	13	13.7
	1962-1974	31	39.2	23	24.2
	1975-1989	14	17.7	16	16.8
	1990-2012	18	22.8	25	26.3
	Après 2012	5	6.3	11	11.6
	Total	79	100	95	100

Table B . 5 :

 B5 Distribution of the number of people in the household according to the groups

	Number of Persons		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	1	8	10.10	13	13.70
	2	20	25.30	25	26.30
	3	24	30.40	22	23.20
	4	16	20.30	22	23.20
	5 or more	11	14.00	13	13.70
	Total	79	100	95	100

Table B .

 B 6:Distribution of the presence of a person in the dwelling at least 3 days a week

	Presence 3 days per week		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	Yes	36	45.6	49	51.6
	No	43	54.4	46	48.4
	Total	79	100	95	100

Table B .

 B 7: Distribution of different types of heating according to groupThis variable addresses whether or not the household has a water heater.

				Control Group	Treated Group
	Type of Heating	Number of	Percentage	Number of	Percentage
			subjects	(%)	subjects	(%)
	Gas		53	67.1	64	67.4
	Electricity		7	8.9	9	9.5
	Liquid Fuel		4	5.1		5	5.3
	Granular Wood 1	1.3		5	5.3
	Inverter AC	0	0.0	2	2.1
	Other		14	17.7	10	10.5
	Total		79	100	95	100
	B.2 Presence of appliances
	B.2.i	Electric water heater
	Table B.8:	Distribution of water heater ownership by group
	Presence of water heater	Control Group Number of Percentage subjects (%)	Treated Group Number of Percentage subjects (%)
	Yes		22	72.2	21	22.1
	No		57	27.8	74	77.9
	Total		79	100	95	100

Table B .

 B 9: Distribution of presence of air conditioning by group

	Presence of air conditioner		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	Yes	5	6.3	8	8.4
	No	74	93.7	87	91.6
	Total	79	100	95	100

Table B .

 B 10: Distribution of presence of heat pump by group

	Presence of heat pump		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	Yes	4	5.1	6	6.3
	No	75	94.9	89	93.7
	Total	79	100	95	100

Table B .

 B 11: Distribution of presence of PV panel by group

	Presence of PV panels		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	Yes	0	0	3	3.2
	No	79	100	92	96.8
	Total	79	100	95	100
	B.2

.v Double glazing of windows

  It is interesting to know about the presence of double glazed windows in the dwelling of households.

	B.3
	Table B.12:	Distribution of presence of double glazing by group
	Presence of		Control Group	Treated Group
	double glazed	Number of	Percentage	Number of	Percentage
	windows	subjects	(%)	subjects	(%)
	Yes	66	83.5	84	88.4
	No	5	6.3		8	8.4
	Partially	8	10.1	3	3.4
	Total	79	100	95	100

Number of appliances in household B.3.i Electric Oven This

  variable refers to the number of ovens present per household.

	Table B.13:	Distribution of number of ovens according to group
	Number of electric ovens	Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	0	2	2.5	2	2.1
	1	70	88.6	85	89.5
	2	7	8.9	8	8.4
	Total	79	100	95	100

Table B .

 B 14: Distribution of number of dishwashers by group

	Number of dishwashers		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	0	16	20.3	15	15.8
	1	63	79.7	80	84.2
	Total	79	100	95	100

Table B .

 B [START_REF] Bartolome | Which tax rate do people use: Average or marginal[END_REF]: Distribution of number of washing machines according to group

	Number of	Control Group	Treated Group
	washing	Number of	Percentage	Number of	Percentage
	machines	subjects	(%)	subjects	(%)
	0	1	1.3	0	0
	1	77	97.5	95	100
	2	1	1.3	0	0
	Total	79	100	95	100

Table B .

 B 16: Distribution of number of clothes dryers by group

	Number of cloth dryers		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	0	61	77.2	65	68.4
	1	18	22.8	30	31.6
	Total	79	100	95	100

Table B .

 B 17: Distribution of number of electric vehicle by group This variable concerns with the hour of use of the iron during a weekday. Table B.18: Distribution of iron use during a weekday

	Number of cloth dryers		Control Group Number of Percentage subjects (%)	Treated Group Number of Percentage subjects (%)
	0	79	100	93	97.9
	1	0	0	2	2.1
	Total	79	100	95	100
	B.4 Energy Consumption during weekdays

B.4.i Pressing Iron

Time of use of pressing iron Control Group Treated Group Number of subjects Percentage (%) Number of subjects Percentage (%)

	6 AM to 9 AM	10	22.7	10	16.9
	9 AM to Noon	5	11.3	7	11.8
	Noon to 3 PM	1	2.3	3	5.1
	3 PM to 6 PM	3	6.8	7	11.8
	6 PM to 8 PM	7	15.1	14	23.7
	8 PM to 11 PM	15	34.1	17	28.8
	11 PM to 6 AM	1	2.3	1	1.7
	Don't know	2	4.5	0	0
	Total	79	100	95	100

Table B .

 B 19: Distribution of electric oven use during weekday

	Time of use of electric oven		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	4	5.3	4	3.4
	9 AM to Noon	8	10.5	9	7.6
	Noon to 3 PM	6	7.9	11	9.3
	3 PM to 6 PM	3	3.9	7	5.9
	6 PM to 8 PM	49	64.4	65	55.0
	8 PM to 11 PM	6	7.9	21	17.7
	11 PM to 6 AM	0	0	1	0.8
	Don't know	0	0	0	0
	Total	79	100	95	100

Table B .

 B 20: Distribution of dishwasher use on weekdays

	Time of use of dishwasher		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	5	7.9	8	8.6
	9 AM to Noon	5	7.9	9	9.7
	Noon to 3 PM	5	7.9	9	9.7
	3 PM to 6 PM	3	4.8	6	6.5
	6 PM to 8 PM	6	9.5	5	5.4
	8 PM to 11 PM	31	49.2	43	46.2
	11 PM to 6 AM	8	12.7	13	14.0
	Don't know	0	0	0	0
	Total	79	100	95	100

Table B .

 B 21: Distribution of washing machine use on weekdays

	8 PM to 11 PM	14	18.7	16	14.5
	11 PM to 6 AM	3	4.0	9	8.2
	Don't know	2	2.7	2	1.8
	Total	79	100	95	100
	Time of use of	Control Group	Treated Group
	washing	Number of	Percentage	Number of	Percentage
	machine	subjects	(%)	subjects	(%)
	6 AM to 9 AM	5	6.7	6	5.5
	9 AM to Noon	18	24.0	20	18.2
	Noon to 3 PM	1	1.3	13	11.8
	3 PM to 6 PM	15	20.0	16	14.5
	6 PM to 8 PM	17	22.7	28	25.5

Table B .

 B 22: Distribution of cloth dryer use on weekdays

	Time of use of cloth dryer		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	0	0	2	8.0
	9 AM to Noon	3	21.4	6	24.0
	Noon to 3 PM	0	0	3	12.0
	3 PM to 6 PM	1	7.1	0	0
	6 PM to 8 PM	2	14.3	5	20.0
	8 PM to 11 PM	5	35.7	8	32.0
	11 PM to 6 AM	1	7.1	1	4.0
	Don't know	2	14.3	0	0
	Total	79	100	95	100

Table B .

 B 23: Distribution of induction plates use on weekdays

Time of use of hotplate Control Group Treated Group Number of subjects Percentage (%) Number of subjects Percentage (%)

	6 AM to 9 AM	5	8.9	8	9.9
	9 AM to Noon	8	14.3	10	12.3
	Noon to 3 PM	10	17.9	13	16.0
	3 PM to 6 PM	1	1.8	0	0
	6 PM to 8 PM	27	48.2	37	45.7
	8 PM to 11 PM	5	8.9	12	14.8
	11 PM to 6 AM	0	0	1	1.2
	Don't know	0	0	0	0
	Total	79	100	95	100

Table B .

 B 24: Distribution of electric radiator use on weekdays

	Time of use of electric radiator		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	6	28.6	11	50.0
	9 AM to Noon	1	4.8	0	0
	Noon to 3 PM	0	0	0	0
	3 PM to 6 PM	0	0	0	0
	6 PM to 8 PM	12	57.1	9	40.9
	8 PM to 11 PM	2	9.5	1	4.5
	11 PM to 6 AM	0	0	0	0
	Don't know	0	0	1	4.5
	Total	79	100	95	100
	B.5

Energy Consumption during the weekend B.5.i Pressing Iron This

  variable concerns with the hour of use of the iron during a weekend. Table B.25: Distribution of iron use during a weekend

	Time of use of pressing iron		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	1	2.6	2	3.4
	9 AM to Noon	10	26.3	15	25.4
	Noon to 3 PM	2	5.2	1	1.7
	3 PM to 6 PM	5	13.1	15	25.4
	6 PM to 8 PM	9	23.6	10	16.9
	8 PM to 11 PM	6	15.7	13	22.0
	11 PM to 6 AM	0	0.0	1	1.7
	Don't know	5	13.1	2	3.4
	Total	79	100	95	100

Table B .

 B 26: Distribution of electric oven use during weekend

	3 PM to 6 PM	5	13.2	15	25.4
	6 PM to 8 PM	9	23.7	10	16.9
	8 PM to 11 PM	6	15.8	13	22.0
	11 PM to 6 AM	0	0	1	1.7
	Don't know	5	13.2	2	3.4
	Total	79	100	95	100
	Time of use of electric oven	Control Group Number of Percentage subjects (%)	Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	1	2.6	2	3.4
	9 AM to Noon	10	26.3	15	25.4
	Noon to 3 PM	2	5.3	1	1.7

Table B .

 B 27: Distribution of dishwasher use on weekends

	Time of use of dishwasher		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	1	1.3	4	4.2
	9 AM to Noon	10	1.3	14	14.7
	Noon to 3 PM	13	17.3	16	16.8
	3 PM to 6 PM	12	16.0	8	8.4
	6 PM to 8 PM	5	6.7	6	6.3
	8 PM to 11 PM	24	32.0	31	32.6
	11 PM to 6 AM	10	13.0	11	11.6
	Don't know	0	0	5	5.3
	Total	79	100	95	100

Table B .

 B 28: Distribution of washing machine use on weekend

Time of use of washing machine Control Group Treated Group Number of subjects Percentage (%) Number of subjects Percentage (%)

	6 AM to 9 AM	6	6.1	6	5.3
	9 AM to Noon	34	34.7	49	43.0
	Noon to 3 PM	16	16.3	14	12.3
	3 PM to 6 PM	17	17.3	16	14.0
	6 PM to 8 PM	9	9.2	11	9.6
	8 PM to 11 PM	9	9.2	10	8.8
	11 PM to 6 AM	3	3.1	6	5.3
	Don't know	4	4.1	2	1.8
	Total	79	100	95	100

Table B .

 B 29: Distribution of cloth dryer use on weekend

	Time of use of cloth dryer		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	0	0	2	8.0
	9 AM to Noon	3	16.0	6	24.0
	Noon to 3 PM	2	11.0	5	20.0
	3 PM to 6 PM	4	22.0	1	4.0
	6 PM to 8 PM	3	16.0	4	16.0
	8 PM to 11 PM	4	22.0	4	16.0
	11 PM to 6 AM	0	0	2	8.0
	Don't know	2	11.0	1	4.0
	Total	79	100	95	100

Table B .

 B 30: Distribution of induction plates use on weekend

	Time of use of hotplate		Control Group Number of Percentage subjects (%)		Treated Group Number of Percentage subjects (%)
	6 AM to 9 AM	3	4.6	8	7.3
	9 AM to Noon	11	16.9	21	19.1
	Noon to 3 PM	18	27.7	26	23.6
	3 PM to 6 PM	3	4.6	4	3.6
	6 PM to 8 PM	24	36.9	37	33.6
	8 PM to 11 PM	6	9.2	14	12.7
	11 PM to 6 AM	0	0	0	0
	Don't know	0	0	0	0
	Total	79	100	95	100

Table B .

 B 31: Distribution of electric radiator use on weekdays

	ANNEX D
	Time of use of electric radiator D.1	Control Group Number of subjects	Percentage (%)	Treated Group Number of subjects	Percentage (%)
	6 AM to 9 AM		20.0	4	17.4
	9 AM to Noon		10.0	7	30.4
	Noon to 3 PM		0	0	0
	3 PM to 6 PM	1	5.0	1	4.3
	6 PM to 8 PM	11	55.0	8	34.8
	8 PM to 11 PM		10.0	1	4.3
	11 PM to 6 AM		0	0	0
	Don't know		0	2	8.7
	Total	79	100	95	100

The Mann-Whitney U Test for green alert days of both sessions using random forest (sequence to sequence) reference load curve in place of control group Sr.

			Reference Load Curve	Treated Group	Mann-Whitney U Test
	No	Alert Date	Obs	µr	бr	Obs	µT	бT	Stat	p-valu	Null hypo-thesis	µr -µT
										e	retaine
											d
						Green Session 1
	1	27-07-2020	0.74	0.7	71	0.99	1.21	2374	0.28	Yes	-0.25	0.74
	2	25-08-2020	0.79	0.56	34	0.81	0.64	143	0.6	Yes	-0.02	0.79
	3	14-09-2020	0.71	0.82	88	0.79	0.83	3579	0.41	Yes	-0.08	0.71
	4	17-09-2020	0.71	0.6	88	0.77	0.68	3797	0.41	Yes	-0.06	0.71
	5	22-09-2020	0.7	0.57	88	0.79	0.96	3968	0.61	Yes	-0.09	0.7
						Green Session 2
	6	14-06-2021	85	0.73	0.63	85	1.01	1	3299	0.16	Yes	-0.28
	7	06-09-2021	-	-	-	-	-	-	-	-	-	-
						Aggregated Result
	Aggregated	423	0.72	0.6 7	454	0.86	0.93	9304 0	0.21	Yes	-0.14
	D

.2 The Mann-Whitney U Test for orange alert days of both sessions random forest (sequence to sequence) reference load curve in place of control group Sr.

	3 4	20-01-2020 06-02-2020 ANNEX E 92 1.32 91 1.42	1.28 1.29	92 91	1.3 1.11	1.57 1.31	4742 5194	0.92 1	Yes Yes	0.02 0.31
	5	19-02-2020	91	1.27	1.17	91	1.19	1.29	4656	0.93	Yes	0.08
	6	27-02-2020 E.1	91	1.18	1.16	91	1.23	1.32	4356	0.73	Yes	-0.05
	7	02-03-2020	90	1.15	1.06	90	0.93	1.07	4870	0.99	Yes	0.22
	8	04-03-2020	91	1.2	1.21	91	0.98	1.06	4844	0.98	Yes	0.22
	9	09-03-2020	90	0.97	1	90	1.26	1.91	3883	0.32	Yes	-0.29
							Orange Session 2
	10 30-11-2020	88	1.43	1.03	88	1.21	0.9	4427	0.95	Yes	0.22
	11	02-12-2020	89	1.48	1.26	89	1.4	1.06	4092	0.65	Yes	0.08
	12	04-12-2020	89	1.35	1.1	89	1.25	1.15	4298	0.84	Yes	0.1
	13	07-12-2020	89	1.39	1.13	89	1.4	1.43	4242	0.79	Yes	-0.01
	14	09-12-2020	89	1.33	1.03	89	1.48	1.32	3913	0.45	Yes	-0.15
	15	18-12-2020	88	1.47	1.35	88	1.19	1.05	4503	0.97	Yes	0.28
	16	04-01-2021	88	1.49	1.51	88	1.57	1.79	3954	0.6	Yes	-0.08
	17	06-01-2021	88	1.62	1.58	88	1.61	2.11	4251	0.87	Yes	0.01
	18 08-01-2021	88	1.61	1.74	88	1.4	1.97	4563	0.98	Yes	0.21
	19	11-01-2021	88	1.54	1.88	88	1.75	1.92	3653	0.26	Yes	-0.21
	20 26-01-2021	87	1.34	1.19	87	1.37	1.23	3830	0.56	Yes	-0.03
	21	11-02-2021	87	1.35	1.41	87	1.23	1.44	4116	0.84	Yes	0.12
						Aggregated Result
		Aggregated		187 5	1.36	1.34	1875	1.29	1.51	19299 17	1	Yes	0.07
					Reference Load Curve	Treated Group	Mann-Whitney U Test
		No	Alert Date	Ob s	µr	бr	Obs	µT	бT	Stat	p-valu e	Null hypothe sis retaine	µr -µT
													d
								Orange Session 1
		1	14-11-2019	91	1.63	1.99	91	1.32	2.41	5089	1	Yes	0.31
		2	20-12-2019	90	1.09	0.82	90	0.92	0.74	4615	0.95	Yes	0.17

The split of IRISE households data with respect to each appliance

  , 921, 923, 924, 925, 926, 927, 928, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 971, 972, 973, 975,

			919, 920976, 978, 979, 980, 981, 984, 985, 986, 987, 988,
			989, 990, 991, 993, 994, 995
	E.2	Sr. No.	Appliance Name	Households Training subset	subset Validation	subset Test
		01	Hot plate	908, 916, 946, 947, 967, 974, 975, 980	982
				909, 914, 921, 928, 934, 936, 938, 944, 945, 953,	983
		02	Electric Heating	954, 959, 967, 968, 969, 971, 974, 975, 977, 978,
				982
				908, 916, 919, 922, 926, 927, 931, 935, 937, 939,	996
		03	Electric Oven	943, 946, 947, 949, 952, 955, 956, 966, 970, 973,
				974, 978, 979, 980, 982
				993, 904, 906, 909, 910, 911, 912, 914, 915, 917,	987
		04	Electric Cooker	918, 921, 923, 924, 928, 932, 933, 938, 941, 945,
				948, 951, 964, 968, 972, 984, 985, 986
				901, 902, 904, 907, 908, 914, 915, 927, 928, 930,	992
		05	Clothes Drier	931, 934, 936, 938, 939, 940, 943, 944, 946, 947, 952, 959, 960, 962, 965, 966, 970, 972, 978, 987,
				989
				901, 903, 907, 908, 910, 914, 916, 917, 920, 922,	996
				925, 926, 928, 930, 931, 933, 936, 937, 938, 939,
		06	Dishwasher	940, 941, 943, 944, 946, 947, 948, 949, 950, 952,
				953, 961, 964, 966, 969, 970, 972, 974, 979, 980,
				981, 983, 984, 985, 989, 991, 993
				901, 902, 905, 906, 907, 908, 909, 910, 913, 914,	996
				915, 916, 917, 918, 919, 920, 921, 922, 925, 927,
				928, 929, 931, 932, 933, 934, 937, 939, 941, 942,
		07	Microwave Oven	943, 945, 946, 947, 949, 950, 951, 952, 956, 957,
				958, 959, 960, 962, 964, 967, 969, 971, 973, 975,
				976, 977, 979, 980, 981, 982, 983, 984, 985, 986,
				987, 988, 989, 990, 991, 992, 993, 994, 995
				901, 902, 905, 906, 908, 909, 910, 911, 912, 913,	996
				914, 915, 916, 917, 918, 920, 921, 922, 923, 924,
				927, 928, 929, 930, 932, 933, 934, 935, 936, 937,
		08	TV	938, 940, 941, 942, 945, 947, 949, 950, 952, 953, 954, 955, 956, 957, 958, 959, 960, 962, 963, 964,
				966, 967, 968, 969, 970, 971, 972, 973, 974, 975,
				976, 977, 978, 979, 980, 981, 982, 983, 984, 985,
				986, 987, 988, 989, 990, 992, 993, 994, 995
		09	Washing Machine	909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 900, 901, 902, 903, 904, 905, 906, 907, 908,	996

The length of data subsets for training the predictive model of each appliance Sr. No. Appliance Data sampled at 10 minutes Data sampled at 30 minutes

			Training	Validation	Training	Validation
			Loss	Loss	Loss	Loss
	01	Hotplate	0.05	0.05	0.07	0.06
	02	Heating Electric	0.14	0.02	0.09	0.02
	03	Electric Oven	0.08	0.06	0.12	0.11
	04	Electric Cooker 0.11	0.12	0.15	0.16
	05	Clothes drier	0.06	0.02	0.09	0.22
	06	Dishwasher	0.03	0.02	0.07	0.03
	07	Microwave	0.06	0.03	0.09	0.04
	08	TV	0.47	0.37	0.46	0.37
	09	Machine Washing	0.13	0.12	0.10	0.09

COP21 is the

st Conference of Parties (COP21) organized by UNFCCC (United Nations Framework Convention on Climate Change) in Paris in November 2015.

ADEME is the French Environment and Energy management Agency.

éCO2mix power data: https://www.rte-france.com/eco2mix/telecharger-les-indicateurs

éCO2mix power data: https://www.rte-france.com/eco2mix/telecharger-les-indicateurs

Electricity of France : https://www.edf.fr/

French distribution system operator : https://www.enedis.fr/

Grenoble applied economics lab : https://gael.univ-grenoble-alpes.fr/accueil-gael

General data protection regulation (EU) : https://gdpr.eu/what-is-gdpr/

Etude-Elec : https://etudelec.univ-grenoble-alpes.fr/. The website is made for the purpose of study. It may not be available anymore.

PP1/PP2: https://www.services-rte.com/fr/visualisez-les-donnees-publiees-par-rte/signaux-pp1-et-pp2.html

TEMPO : https://particulier.edf.fr/fr/accueil/contrat-et-conso/options/tempo.html#/selection-bp

EJP : https://particulier.edf.fr/fr/accueil/contrat-et-conso/options/tempo.html#/selection-bp

The region of Auvergne-Rhônes-Alpes, where the experiment is performed is included in the south zone by EDF for EJP notifications : https://www.fournisseurs-electricite.com/edf/tarifs/ejp/zone

The analysis was performed in RStudio in the programming language 'R'. A group of students of econometrics assisted in this evaluation. The evaluation served as a course project for the students.

The estimation is made using the visualization function of Kernel density estimation in "pandas" package (of python language). This visualization function uses a class "scipy.stats.gaussian_kde" from "scipy" python package at backend. For the estimation, the default parameters are used i.e. Gaussian kernel is used alongwith "scott" method as bandwidth estimator of density estimation.

This test is also known as Wilcoxon rank sum test. The test was performed using the function "scipy.stats.mannwhitneyu()" of Python's SciPy package.

The test was performed using the function "statsmodels.regression.linear_model.OLS" of Python's StatsModel package. The default parameters of the function are used.

Significant codes: a) *** [0, 0.001] , b) ** (0.001, 0.01] , c) * (0.01 , 0.05]

It is also sometimes referred as non-intrusive appliance load monitoring or NIALM in short.

Information regarding integration of IRISE dataset in REMODECE project: https://remodece.isr.uc.pt/database/Campaign_Irise.html

A.3 Default commitments for green alert A.4 Default Commitment of Orange Alert

Chapter III

Measuring the impact of nudge signal

This chapter deals with the statistics of alerts i.e. interventions made for energy flexibility. It also presents a comparison of statistical and energy consumption characteristics of control group and treated group. The later section of this chapter discusses the quantification of the energy flexibility implemented by treated group as compared to the control group.

Chapter IV

Reference Load Curve: An approach for indirect feedback In this chapter, reference load curve is studied as a tool:

to give an indirect feedback in graphical form to potentially replace control group and to categorize the individual with respect to their effort of energy flexibility according to a defined threshold.

Chapter V

The quest of finding potential appliances for energy flexibility in line with subject commitment -Pressing Iron -Electric vehicle -Electric mobility

V.5.b The statistics of appliances in IRISE dataset

Before going further, it is interesting to look at the statistical variables of each appliance. For each appliance, these variables are calculated for their respective data present in IRISE households.