Christine Esclapez
email: christine.esclapez@prism.cnrs.fr

Caroline Boe
email: boe@prism.cnrs.fr

Josephine Simonnot
email: josephine.simonnot@cnrs.fr

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Axe 1 Archive comme matériau

Travail de recréation base sur la réutilisation d'archives musicales issues du Repertorium: réécritures, détours, réinterpretations.

Recherche-Création

La vie de l'archive musicale et sonore après sa "mise en archive" Trois axes de recherche : protocole hybride et innovant (SATT Sud-Est)

Axe 2

Archive comme document

Expertise archivistique du

Repertorium pour établir une politique de conservation et de valorisation du patrimoine à moyen et long terme.

Sciences archivistiques et historiques

  dans l'atelier • Enregistrements sonores • Discussions sur la musique du patrimoine • Discussions sur la perception du son des gestes artisanaux Composition acousmatique • Sons concrets liés à l'artisanat • Musique ancienne Artisan•es dans leurs ateliers : Toni Casalonga, Marie-Claire Darnéal, Ugo Casalonga, Cyril Quilichini, Hervé Quilichini, Virginie Torrelli. Photos : C. Boë © CC BY. Le Chant du Geste Inauguration of the balade: 23/02/2023 « Je pense que pour les touristes, cela leur donnera une image encore plus culturelle et un lien plus profond avec le village, plutôt qu'un lien commercial. En très peu de temps, cela peut donner une dimension sociale et culturelle, même sans explications » Commentaire collecté après l' inauguration, 23/02/2023. Public écoutant lors de l'inauguration , 23/02/2023. Photo : C. Boë © CC BY.

Valorisation par la recherche : L'écoute active du territoire

	Son, Héritage et Histoire
	Pastilles sonores : valorisation par l'écoute
	Archives musicales	Artisans
	Repertorium	CORSICADA	Strada di l'Artigiani
	CNCM Voce	(1964)	(1993)
	« arrêter l'hémorragie qui vide	« maintenir et développer le
	l'intérieur de la Corse de ses	secteur de l'artisanat dans la
	forces vives. »		région de la Balagne. »

J.-L. Morucci, Les années Corsicada ou l'histoire singulière d'un projet d'économie alternative,

Ajaccio, Albania, 2008, p. 10.

https://www.routedesartisans.fr/lassociation/lesstatuts


Fabien Degalez

Sandrine Lagarrigue

A lncRNA GENE-ENRICHED ATLAS FOR THE GRCg7b CHICKEN GENOME AND ITS FUNCTIONAL ANNOTATION ACROSS 47 TISSUES

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

A lncRNA GENE-ENRICHED ATLAS FOR THE GRCg7b CHICKEN GENOME AND ITS FUNCTIONAL ANNOTATION ACROSS 47 TISSUES CONTEXT While protein-coding genes (PCG) are relatively well known in livestock genomes, long non-coding RNA (lncRNA) gene loci, known to be regulatory genes, are more poorly described and differ greatly between reference genome annotations. Even if new expression data contribute to improve their identification, their low expression and high context-specificity remain a challenge. For the chicken, in 2022, the new GRCg7b chicken genome assembly with its associated genome annotations have been released.

OBJECTIVES

• Provide an annotation of the chicken genome according to the GRCg7b assembly.

• Integrate the two reference annotations including "NCBI-RefSeq" (Ncbi) and "EMBL-EBI Ensembl/GENCODE" (Ensembl).

• Ease the joint use of gene models from Ncbi & Ensembl and the switch between the galgal5, GRCg6a and GRCg7b chicken assemblies.

• Increase the number of lncRNA identified using additional resources from multi-tissue projects or specialist databases.

• Provide a genomic and functional annotation for the community working on gene expression (lncRNAs and/or PCGs) to elucidate, for example, the molecular mechanisms linking non-coding variants and relevant phenotypes.

MATERIALS & METHODS

RESULTS

WEBSITE

To help in performing studies such as QTL, eQTL or differentially expressed genes analyses, all these data are easily accessible via a user-friendly online tool called GEGA (Gallus Enriched Gene Annotation) enabling efficient data analysis thanks to a multitude of modules and an ergonomic design. All results and a multitude of graphical representation can be simply produced and downloaded, ranging from basic gene information to detailed visualization of expression and co-expression. gega.sigenae.org

LOC101748017 ≈ ENSGALG00010028753

Expr. (TPM)

Brain System

Expression information to test consistency between phenotypes and gene expressions related to SV2A gene

• Chicken Epilepsy • Brain-related tissues expression

Genomic and expression information to predict lncRNA function ("guilt by association" principle)

-with respect to the closest PCG -with respect to hosted miRNA The antisense lncRNA, only identified in Ensembl, is co-expressed across tissues and especially in the heart with TBX5 associated to heart development [START_REF] Steimle | TBX5: A Key Regulator of Heart Development[END_REF]. sandrine.lagarrigue@institut-agro.fr fabien.degalez@inrae.fr

Fig. 1 : 2 :

 12 Fig. 1: Origin and aggregation rules used to build the gene-enriched annotation Fig. 2: A diversity of 47 tissues used for gene expression

Fig. 3 :

 3 Fig. 3: Number of genes in the enriched atlas Fig. 4: Functional gene annotation, a resource for a variety of analyses

•

  The lncRNA, only identified in Ncbi, is liver-specific and hosts mir-122 involved in liver function[START_REF] Bandiera | miR-122--a key factor and therapeutic target in liver disease[END_REF]. Conservation of the expression profiles of the two syntenic orthologous lncRNAs.• The miRNA hosted in the lncRNA also shows liver-specific expression in human.

•*

  Conservation of the expression profiles for both syntenic lncRNA and PCG.• In human, TBX5-AS1 regulated TBX5 expression levels by forming an RNA duplex that protects the mRNA from RNase degradation Tissues for which the gene is not/lowly expressed are not shown.

F. Degalez 1 &

 1 S. Lagarrigue 1 1 PEGASE, INRAE, Institut Agro, 35590 Saint Gilles, France GEGA Gene model features Functional annotation (Go terms / MGI) Occurrence of the gene loci in the 6 resources Gene pair configuration (PCG / lncRNA / miRNA) Gene expression Orthology (human / mouse)

Acknowledgments: This project is funded by the European Union's Horizon 2020 research and innovation program under grant agreement N°101000236 and by ANR CE20 under 'EFFICACE' program.


est destinée au dépôt

Mode selection and dispersion engineering in

  Bragg-like slot photonic crystal waveguides for hybrid light-matter interactions Samuel Serna, Weiwei Zhang, Thi Hong Cam Hoang, Carlos Alonso-Ramos, Delphine Marris-Morini, Laurent Vivien, Eric Cassan To cite this version: Samuel Serna, Weiwei Zhang, Thi Hong Cam Hoang, Carlos Alonso-Ramos, Delphine Marris-Morini, et al.. Mode selection and dispersion engineering in Bragg-like slot photonic crystal waveguides for hybrid light-matter interactions. Photonics research, 2017, 6 (1), pp.54. 10.1364/prj.6.000054. hal-


domain: qfin sde.es stat

text:
Pille-Riin Aja

Louis-Gaëtan Giraudet

Sébastien Houde

The (Non-)Disclosure of Energy Efficiency: The Case of Cooling Technologies across Africa

The adoption of air conditioning (AC) could grow exponentially across Africa under the joint effect of acute warming, sustained income growth and rapid urbanization. The implications for greenhouse gas emissions will crucially depend on the energy efficiency of the models adopted. Little is known, however, about how energy efficiency information is conveyed to consumers in these markets. To fill this gap, we gathered data on cooling appliances' characteristics from Africa's largest e-commerce platform, serving 13 countries-Algeria, Cameroon, Côte d'Ivoire, Egypt, Ghana, Kenya, Morocco, Nigeria, Senegal, South Africa, Tanzania, Tunisia, and Uganda. We find that less than 10% of the AC models available on the marketplace (N=1,229) have information disclosed about their energy performance. Information disclosure appears to be highly idiosyncratic with weak strategic motives. This overall lack of information about energy efficiency represents an important challenge for enforcing energy performance standards and steering demand toward energy-efficient cooling appliances.

Introduction

The impacts of climate change and the resources needed to adapt to them are unequally distributed across regions. In particular, Africa, already the hottest continent, is experiencing acute warming-a trend that is only expected to continue [START_REF] Diedhiou | Changes in climate extremes over West and Central Africa at 1.5 °C and 2 °C global warming[END_REF][START_REF] Nikulin | Scinocca, and Samuel Somot. The effects of 1.5 and 2 degrees of global warming on Africa in the CORDEX ensemble[END_REF]. This vulnerability is exacerbated by low income levels across the continent, making it harder to adapt. The widespread diffusion of cooling technologies, such as fans and air conditioners (ACs), could be an effective way to address the adaptation challenge. These technologies are already mature, relatively inexpensive, and have a proven ability to mitigate a range of adverse effects related to heat stress; including mortality, morbidity, educational underachievement and productivity losses [START_REF] Basu | High ambient temperature and mortality: a review of epidemiologic studies from 2001 to 2008[END_REF][START_REF] Barreca | Adapting to Climate Change: The Remarkable Decline in the US Temperature-Mortality Relationship over the Twentieth Century[END_REF].

The ownership rate of cooling devices is currently very low across Africa-less than 5%, compared to 88% among households in the United States, the country with the highest penetration rate [START_REF] Eia | Energy Information Administration -EIA -Independent Statistics and Analysis[END_REF]. However, this could change rapidly thanks to fast income growth among African middle-and high-income households, known to be a first-order driver of AC adoption [START_REF] Davis | Air conditioning and global inequality[END_REF]. Accordingly, the IEA (2022) projects that AC and fan ownership will more than double across Africa in the current decade.

A crucial question raised by this rapid market expansion is whether the implied energy demand will grow at the same rate or instead remain flat due to the adoption of highly energy-efficient technologies. One important condition for the latter is that energy efficiency information is conveyed transparently to consumers. This, in turn, depends on firms' information disclosure behavior, which we know very little about in this context.

In this paper, we intend to fill this gap and elicit the patterns of energy efficiency disclosure in several important markets for ACs in Africa. To do so, we use a novel database of product characteristics scraped over a 3-year period from Africa's leading e-commerce platform, serving 13 countries across the whole continent. We find that the information made available to consumers is very limited, with less than 10% of all products offered (N=1,229) having their energy consumption characteristics disclosed on the platform. We additionally find evidence that information disclosure 1 is correlated with the existence of a mandatory energy performance standard in a country and with product characteristics, such as brand and price category. The correlation is, however, weak. Altogether, these findings make it very unlikely that consumers will knowingly and deliberately adopt energy-efficient products-price constraints notwithstanding. More generally, we see this lack of systematic disclosure as an impediment to effectively enacting and enforcing energy efficiency regulations and incentives-minimum performance standards and subsidy programs in particular.

The remainder of the paper is organized as follows. In the next section, we describe the institutional setting of African markets for cooling appliances. In Section 3, we discuss our data collection and compute basic statistics depicting the market structure of cooling appliances. In Section 4, we analyze the decision to disclose energy information. We then conclude.

African Markets for Cooling Appliances

Between 300 million and 2 billion African residents are projected to experience 15-day extreme heat waves (>42°C) by the end of the century [START_REF] Peter | The future urban heat-wave challenge in Africa: Exploratory analysis[END_REF]. With growing income and expanding energy access, mechanical cooling appliances-fans and air conditioning-are becoming increasingly accessible to households [START_REF] Gertler | The Demand for Energy-Using Assets among the World's Rising Middle Classes[END_REF]. The current AC ownership rate on the continent is 0.08 units per household (compared to 0.7 units globally or 2.2 units in the U.S.) (IEA, 2022). The biggest markets are located in North and South Africa (CLASP, 2018). In Ghana, the African country with one of the highest rates of electricity access (World Bank, 2021), only 0.7% of households have ACs [START_REF] Kwabena | Efficiency of household electricity consumption in Ghana[END_REF].

Despite low ownership rates, the expansion of air-conditioning is putting African power grids under pressure [START_REF] Falchetta | The role of residential air circulation and cooling demand for electrification planning: Implications of climate change in sub-Saharan Africa[END_REF]. In sub-Saharan Africa, half of electricity production capacity relies on polluting, low-efficiency backup generators (IFC, 2019). Unless significant changes occur in the grid and off-grid networks, increased energy demand from AC operation will be met by these polluting generators. It is therefore crucial to increase the energy efficiency of ACs.

Little is known about the energy efficiency of the ACs sold in African markets. In an international comparison featuring South Africa as the only African country, the IEA (2018) reports a narrow energy efficiency range there, centered around a relatively low average value. In addition, CLASP (2019CLASP ( , 2020)), an active organization in the cooling field, reports evidence of active secondary markets where the dumping of used and low-efficiency appliances imported from other regions is common practice across the continent. Furthermore, comprehensive information about energy efficiency is scarce even in countries that have introduced energy regulations, 1 such as energy labels, minimum energy performance standards (MEPS), and/or bans on the import of used appliances [START_REF] Kofi | Removing barriers and promoting demand-side energy efficiency in households in Sub-Saharan Africa: A case study in Ghana[END_REF]. Several of these countries also participate in an IEA co-led initiative targeting the energy efficiency of different appliances including ACs (IEA, 2022). 2 The degree of enforcement of these measures however remains unclear, and consistent reporting of energy efficiency information is still lacking in most African countries.

The organization of the downstream market also makes it difficult to track energy efficiency information for the set of products for sale. As of today, African retail cooling markets consist of brick-and-mortar stores supplemented by informal markets. E-commerce still has a marginal market share. It is however growing rapidly, spurred by the increase in internet users-+33% between 2017 and 2021, now reaching 590 million 3 -and the spread of mobile phones offering broadband connection-+100% in five years, up to 28% of the population in 2020 (AFDB, 2022). This expansion is so fast that e-commerce could "leapfrog" bricks-and-mortar retail in many African countries, where the average number of stores is still below 15 per one million people [START_REF] Tempest | The Digital Economy and Ecommerce in Africa -Drivers of the African Free Trade Area?[END_REF]. This could be further accelerated by newly implemented policies. For instance, the African Union has committed to creating a single digital market by 2030 (African Union, 2020). The African 1 This includes Egypt, Ghana, Kenya, Morocco, South Africa and Tunisia [START_REF] Shaffie | Cooling Egypt: Cost and benefits of room air conditioner efficiency improvement in Egypt[END_REF]The Energy Commission, 2020;CLASP, 2019;IEA, 2020a,b;CLASP, 2020). These countries all feature in our database.

2 Ghana and Nigeria joined the COP26 Product Efficiency Call to Action, Ghana and South Africa are members of the Super-Efficient Equipment and Appliance Deployment Initiative.

3 Internet World Stats website of Miniwatts Marketing Group. Source: https://www.internetworldstats.com/stats1.htm Development Bank Group's 4IR Fund highlights e-commerce as an important element in improving digitalization in Africa (AFDB, 2022). ICA (2019) reports that $7.8 billion-worth of investment was made in information and communication technology in 2018, a trend that continues to rise.

As e-commerce platforms are bound to play an increasingly important role in African households' shopping decisions, it is crucial that they transparently disclose the technical characteristics of the products offered -energy-related information in particular. In turn, e-commerce platforms offer an ideal setting to track product characteristics, thus creating interesting opportunities for research at the intersection of economics and energy.

Data Collection

Very few African authorities maintain a public database of cold products (e.g., ACs, fans, or refrigerators), 4 To overcome this limitation, we set out to build our own database. In the spirit of Cavallo and Rigobon's Billion Prices Project [START_REF] Cavallo | The Billion Prices Project: Using Online Prices for Measurement and Research[END_REF] and Gerke and Tu (2015)'s International Database of Efficient Appliances (IDEA), we developed scraping algorithms to collect data on African e-commerce. Importantly, our data collection effort complements IDEA by documenting a yet uncovered region and surveying markets at a higher frequency (daily, instead of once every month or so) and over a longer period (four years and counting, instead of 2014). We focus on Jumia, Africa's first and most visited e-commerce platform.5

Methodology

Our goal is to assemble a panel of daily prices and product characteristics for all major cold appliances offered on the Jumia platform. Our data collection effort started on June 28, 2019, and was carried out with a scheduled algorithm that extracted product information displayed to website visitors. The database spans 13 African countries where Jumia has a substantial presence -Algeria, Cameroon, Côte d'Ivoire, Egypt, Ghana, Kenya, Morocco, Nigeria, Senegal, South Africa, Tanzania, Tunisia, and Uganda. The information collected is in French or English.

Our targeted product categories are so-called cold appliances, which include ACs, comfort fans, refrigerators, refrigerator-freezers and freezers. We focus on several types of ACs, defined as appliances driven by an electric compressor that provide space cooling or heating (EU, 2017), such as double duct ACs, single duct ACs and ACs with additional functionalities like dehumidification and air-purification. For those appliances that provide both cooling and heating and have separate technical specifications for each functionality, we consider only those relevant to cooling. As for fans, defined as appliances that are designed to create air movement in the proximity of the human body for the purpose of personal cooling comfort (EU, 2017), we collect all device categories, including-but not limited to-floor standing, ceiling, and USB-powered fans. In the refrigerators and freezers section, defined as appliances with one or more compartments for preserving fresh or frozen foodstuffs that are cooled by an energy-consuming process (EU, 2019), we collect all models available on the platform.

Our scraping algorithm retrieves the following information from a product's page: title, category, brand, average rating of the product submitted by the users of Jumia, number of ratings submitted, estimated delivery time to the customer, price, original price and discount percentage (if any), URL, and two description fields. Most importantly, the algorithm is specifically designed to identify and extract all energy-related information.

The panel is unbalanced due to various events that caused interruptions in data collection. The most frequent issues were changes in the website source code, which typically took us a few days to incorporate in our algorithm. Moreover, data collection had to be discontinued altogether in Cameroon, Tanzania and South Africa, for different reasons. 6

Recovering Energy-Related Information

Our primary goal is to retrieve energy efficiency information for all models of cooling appliances scraped on the Jumia platform. The most commonly used metrics for measuring AC energy efficiency are the energy efficiency ratio (EER) and the seasonal energy efficiency ratio (SEER). The former is the ratio between the cooling capacity of the appliance and its effective power input (Energy.gov). The latter factors in the effective annual energy consumption at the numerator (EESI). The EER is a convenient indicator to compare AC energy efficiency across countries. The SEER is, however, becoming more prevalent, for it more accurately captures climate-related differences in AC operation [START_REF] Won | Lost in translation: Overcoming divergent seasonal performance metrics to strengthen air conditioner energy-efficiency policies[END_REF].

Calculating these indicators requires detailed information about local seasonal conditions, appliance design, and energy consumption for all possible consumption modes (EU, 2017; [START_REF] Huang | Air conditioners and comfort fans[END_REF]EU, 2019). In this paper, we use the power input and annual energy consumption figures reported in the product description. We do not observe which local seasonal conditions or appliance design parameters were considered in their calculation.

Our algorithm is designed to identify EER and SEER ratings alongside other related attributes, usually in the product description field. We used regular expression patterns to locate and retrieve the relevant technical characteristics from those fields, which are presented on the website in raw text format. This task was complicated by inconsistent naming conventions, an ambiguous use of technical terms, 7 mistakes and inconsistencies in text spacing, and illogical sentence construction. Since there is no straightforward way to bypass these obstacles, our identification process is not exempt from omissions. We nevertheless tried to minimize them by adding a step of manual browsing to identify undetected information.

For the purpose of our analysis, we refer to EER and SEER as stated if the ratios were directly communicated in the product description. Otherwise, if at least the cooling capacity and power consumption were reported separately, we could compute the ratios ourselves. We refer to these as implicit. 8 We consider the stated ratios to be the most accurate proxy of energy efficiency. The computation of their implicit counterparts relies on rounded-off input values, resulting in measurement errors. Moreover, cooling capacity is documented in three different units: watt, BTU/h, and ton. The latter unit has a coarser measurement resolution, thus amplifying rounding errors. 9 To account for these limitations, we construct two separate implicit ratios: (i) Implicit EER (or SEER) computed using watt or BTU/h, and (ii) Implicit EER (or SEER) computed using ton. Finally, when available, we use information about the maximum energy consumption to compute yet another ratio, which we refer to as Implicit minimum EER. 10 4 The (Non-)Disclosure of Energy Information

In this section, we focus on the AC data collected in the first three years (i.e., through June 30, 2022) and analyze the EER/SEER metrics displayed on Jumia. Despite important efforts to systematically track and recover energy-related information, we find that neither the stated EERs 6 Jumia ceased to serve Cameroon on November 18, 2019, and changed its business model from an e-commerce website to a marketplace platform on November 27, 2019, in Tanzania. The platform started serving South African in 2021 but stopped selling our products of interest by the end of that year. Our South African data thus only span the April 21-November 14, 2021 period.

7 For example, the labeling of the power input and cooling capacity indicators are often unclear. Moreover, a significant number of ACs report horsepower as the unit of measurement for capacity. This is an indicator of compressor capacity only, which does not allow us to recover the cooling capacity or consumption of the AC.

8 For the calculation of Implicit SEER, we used total seasonal power consumption instead of regular power consumption.

9 To fix ideas, the measurement error is 32% if a 1.52 ton cooling capacity is rounded to 2 tons but only 0.6% if a 17,900 BTU/h capacity is rounded to 18,000 BTU/h.

10 Conventional energy efficiency indicators are computed using nominal energy consumption values. A metric using the maximum energy consumption values as input can thus be considered a minimal EER.

nor the information needed to compute the SEERs are frequently disclosed. This motivates us to examine in a second stage whether the scarce information available follows a particular disclosure pattern.

Descriptive statistics

The following analysis will concentrate on the features of AC models, independent of the time component, so we transform the panel data into cross-sectional data. The modified database contains 14,134 AC models from 166 brands. Brand names could not be identified for only 87 observations (0.6% of the data set) in Côte d'Ivoire, Nigeria, Morocco, Kenya, Senegal, Ghana and Tanzania. The broadest product sets are found in Nigeria, Egypt, and Côte d'Ivoire and the smallest in Algeria, Kenya and Uganda.

Taken together, the five EER metrics (i.e., Stated EER, Stated SEER, Implicit EER, Implicit SEER or Implicit minimum EER) are available for 8.5% only of all AC models in our database, with substantial heterogeneity across countries. Stated EERs-both the most accurate and prevalent metric-are available 6% to 27% of the time in Senegal, Egypt, Uganda, Tanzania, Tunisia and Morocco, but less than 5% of the time in Côte d'Ivoire, Ghana and Nigeria. In Algeria and Cameroon, where the choice sets are the smallest, information is richer, with stated EERs available for about half of the products. There are no stated EER information available for ACs in Kenya and South Africa, so we exclude these two countries from the rest of the analysis and focus on the other 11 countries. Table 1 provides a detailed overview of these indicators. There is a strong correlation in the disclosure of various indicators: in countries where stated EERs is frequently available, so are the inputs needed to compute implicit EERs. In contrast, information is in most cases missing about the SEER and minimum EER.

The average AC energy efficiency level in the 11 African countries, where stated EERs were presented, is 3.2 W/W (table 2). To put these figures into perspective, EER values similarly fall within 2.9 and 3.6 W/W in Europe, East Asia and North America according to the IEA ( 2018). The African figures, however, follow a different distribution, both within and across countries. (see Min./Max. columns in table 2). In particular, the upper end of the distribution in Egypt and Senegal matches the performance of the world's best technology (EER>6.0 W/W). Still, the maximum value remains well below this mark in most countries (between 3.0 and 4.0 W/W). Manufacturers, therefore, appear to refrain from offering the most efficient AC models in African markets. The technology frontier makes it unlikely that African households could leapfrog other countries by adopting the most efficient models at the onset of their electrification. Note: MEPS usually vary with compressor type and other attributes. As for EER, we make no distinction between AC types and attributes, as we do not systematically observe such characteristics in our data.

During our sample period, six countries had mandatory minimum energy performance standards (MEPS) for ACs -Egypt, Ghana, Kenya, Morocco, South Africa and Tunisia [START_REF] Shaffie | Cooling Egypt: Cost and benefits of room air conditioner efficiency improvement in Egypt[END_REF]The Energy Commission, 2020;CLASP, 2019;IEA, 2020a,b;CLASP, 2020) -and two had voluntary MEPS -Nigeria and Uganda (CLASP, 2020). Figure 1 allows us to visualize the stated EER distribution for each country (except Kenya and South Africa, where we have no ACs with stated EER), together with MEPS when relevant. The mass of AC models that fall below the lowest MEPS in a given country provides a proxy for the degree of compliance with the regulation. Egypt stands out with more than half of the models (271, hence 60%) that are not compliant with the regulation. Non-compliance is much less pervasive in other countries. In Morocco and Tunisia, only one model (0.1% in both countries) is not compliant and in Ghana, all available models meet the minimum standard.

Disclosure of Energy Information

We now examine the decision to disclose energy efficiency information (namely EERs or SEERs) by manufacturers. In our context, manufacturers are responsible for this decision because the ecommerce platform discloses whatever information it receives from the manufacturers. As discussed above, we found that EER or SEER information is only available for 5.8% of the models offered (Table 1). Our goal is to investigate if the disclosure decision is strategic or correlated with specific product characteristics. For instance, if we find that EERs are only disclosed for products that systematically fit certain criteria, this could suggest strategic disclosure. We hypothesize that product quality, which we proxy by price levels, is correlated with the disclosure decision-expensive and high-quality products, yielding high markups, could be more likely to include energy efficiency information. The regulatory environment, particularly the presence of minimum performance standard regulations, could also be a driver of disclosure.

We consider the following linear probability model:

ST AT ED i = βM EP S i +γ j •P RICECT ij +σ b •BRAN D ib +λ c •COU N T RY ic +µ m •M ON T H im +ϵ i , (1)
where the dependent variable is the disclosure dummy ST AT ED i , taking the value one if the stated EER/SEER of the AC model i is disclosed and zero otherwise. We regress this dummy against a parsimonious set of observable variables. The dummy M EP S i takes the value one when the country, on whose platform model i is displayed, has implemented a mandatory MEPS. The coefficient β estimates the effect of the existence of a MEPS on the probability of the AC having a dislosed EER/SEER. We use P RICECT ij , a dummy for each price level j, to proxy for product quality. The coefficients γ j estimate the effect of price level j on the probability of disclosure. The dummies BRAN D ib refer to each brand b. The coefficients σ b estimate the brand b's effect on the probability of stated energy efficiency disclosure. The dummies COU N T RY ic and M ON T H im refer to country and the AC's month of first appearance on a given country's Jumia website effects, respectively. Their coefficients λ c and µ m capture these variables' effect on the probably of energy efficiency disclosure.

Note that we exclude ACs with a maximum price above USD 2,000 and countries where no ACs have stated EER information (Kenya and South Africa).

As shown in Table 3, we find that the disclosure decision is negatively correlated with the descending price terciles, i.e., AC models in the top two highest price terciles are more likely to have their EER information disclosed relative to AC models in the lowest price tercile. This correlation holds across the different specifications. To account for time trends, we control for the month-of-sample the product was first offered on the platform. We also control for country fixed and brand fixed effects.

The coefficient of the regulation dummy is positive in the model with only the price terciles present. But the impact of regulation is not identified once we account for country fixed effect. This variable is likely to be correlated with other country-specific variables, which we do not account for. The specification with brand fixed effects generates several insights. First, the relatively high adjusted R-squared value suggests brands explain a significant portion of the variance in the disclosure decision. 11 In Figure 2, we rank brand fixed effects. We observe substantial heterogeneity, as well as some patterns. Smaller brands (i.e., the ones with less models offered) are more likely to disclose, for instance, relative to larger brands. To illustrate this, we have indicated the number of AC models offered by each brand on the figure labels. The five brands that most frequently display energy efficiency information tend to be those with the smallest catalogues available on the retail platform.

Overall, our analysis of the disclosure decision points toward some strategic elements. Quality, energy regulations, and brand size are correlated with the disclosure decision. These elements, however, explain only part of the variation in the disclosure decision, which remains mostly idiosyncratic.

Conclusion

In an effort to document AC product lines in the African continent, we have assembled a database of product characteristics from online markets in 13 countries: Algeria, Cameroon, Côte d'Ivoire, Egypt, Ghana, Kenya, Morocco, Nigeria, Senegal, South Africa, Tanzania, Tunisia, and Uganda.

Our analysis shows that less than 10% of ACs (N=1,229) available for sale on Jumia-Africa's leading e-commerce platform-have their energy performance disclosed. While price levels, energy regulations, and brand size seem to explain some of the variation in energy efficiency disclosure, information disclosure is highly idiosyncratic.

11 We also considered a model where brand fixed effects were replaced by brand size and we found that the former regressor significantly improves the model's ability to explain the variation in the efficiency disclosure decision than the latter. The brand fixed effects capture several dimensions related to a brand's strategy, e.g. marketing strategy, in addition to the number of models offered.

The disclosure of energy information in these markets is unlikely to occur without government intervention. Given the rapid expansion of the demand for cooling technologies across Africa in the upcoming years, it is crucial to implement policies quickly and enforce them effectively. Specifically, a combination of information provision and incentives could be instrumental in encouraging the adoption of highly energy-efficient appliances, thus helping African households adapt to climate change.

An important avenue for further research would be to enhance the database with product data gathered directly from manufacturers, thus allowing us to get a more complete overview of the energy efficiency of ACs in African online markets. The data collection could also be extended to further regions where AC markets are more established -India, Brazil, the United States and a key European countries such as France, Italy, Spain and Greece. This would generate insights into how energy efficiency standards could be coordinated at the international level.

  Data cover the period of 28 June 2019 to 30 June 2022. Stated EER: EER explicitly documented in the product description.Stated SEER: SEER explicitly documented in the product description. Implicit EER: EER computed from AC cooling capacity and energy consumption. Implicit min. EER: EER indicator computed from AC cooling capacity and maximum energy consumption.Implicit SEER: SEER indicator computed from AC cooling capacity and seasonal energy consumption.

Figure 1 :

 1 Figure 1: Distribution of Stated EER in W/W.

Table 1 :

 1 Overview of available energy efficiency information for ACs on Jumia e-commerce platform.

Table 2 :

 2 Descriptive statistics: Stated EER in W/W

		Count Mean Std dev. Minimum Maximum
	Algeria	5	3.6	0.0	3.6	3.6
	Cameroon	20	2.8	0.3	2.4	3.3
	Côte d'Ivoire	15	3.2	0.8	2.0	5.9
	Egypt	177	3.3	0.7	2.3	5.9
	Ghana	9	3.2	0.3	2.9	3.9
	Morocco	51	3.1	0.3	2.5	3.7
	Nigeria	271	2.9	0.5	1.5	3.7
	Senegal	25	3.2	0.8	2.0	6.5
	Tanzania	8	3.2	0.3	2.8	3.6
	Tunisia	47	3.2	0.1	2.8	3.4
	Uganda	8	3.4	0.3	2.8	3.6

Table 3 :

 3 Disclosure decision: linear probability model

		Decision to disclose EER or SEER
		(1)	(2)	(3)
	Country with mandatory MEPS	0.0284 * * *
		(0.0053)
	Price category 400-1000 USD	-0.0302 * * * -0.0333 * * * -0.0335 * * *
		(0.0098)	(0.0094)	(0.0091)
	Price category <400 USD	-0.0708 * * * -0.0458 * * * -0.0353 * * *
		(0.0096)	(0.0097)	(0.0095)
	Constant	0.0957 * * *	0.0867 * * *	0.0825 * *
		(0.0095)	(0.0315)	(0.0329)
	Country	No	No	Yes
	Brand	No	Yes	Yes
	Month of first appearance	No	Yes	Yes
	R squared	0.0169	0.1734	0.2154
	Adj R squared	0.0167	0.1615	0.2035
	Observations	14,101	14,101	14,101

*** , ** , * denote significance at the 1%, 5% and 10% level, respectively. All regressions have robust standard errors.

Kenya is set to launch a public database of registered appliances (CLASP, 2019).

According to Statista, Jumia was the most visited online marketplace in Africa in 2021 (https://www.statista.com/chart/27527/africas-popular-e-commerce-platforms/). Jumia, on its part, reported over 1.1 billion visits in 2021 (https://group.jumia.com/). The breadth of Jumia's product coverage is similar to Amazon's, who did not operate on the continent until very recently. Amazon plans to launch their platform in Nigeria and South Africa in 2023 (https://www.theafricareport.com/234135/amazon-accelerates-in-africa-much-tothe-chagrin-of-local-players/).

Acknowledgements

This work benefited from funding from Agence Nationale de la Recherche under the AFRICOOLING project (ANR-21-CE03-0006). We thank Philippe Quirion and two anonymous referees for helpful comments that significantly improved the quality of the paper.

 1) Clivatex ( 7) Bluegate ( 9) Hoomex ( 4) Lakes ( 4) Daikin ( 170) Polystar ( 1238) Era ( 25) Elbee ( 2) Midea ( 967 78) Xper ( 8) Ilux ( 55) Elactron ( 6) Eastpoint ( 10) Firman ( 39) Signature ( 2) Comfee ( 1) York ( 55) Starsat ( 8) Skyline ( 9) Unionaire ( 631) Chiq ( 14) Americool ( 20


Benoit Couraud
email: benoit.couraud@glasgow.ac.uk

Pierre-Jean Barre
email: pierre-jean.barre@univ-cotedazur.fr

Roberta Pennucci
email: roberta.pennucci@univ-cotedazur.fr

Yann Rozier
email: yann.rozier@univ-cotedazur.fr

Merlinda Andoni
email: merlinda.andoni@glasgow.ac.uk

Sonam Norbu
email: sonam.norbu@glasgow.ac.uk

David Flynn
email: david.flynn@glasgow.ac.uk

Collecting French Smart Meter Data for Residential Flexibility

Keywords: Load Analysis, load management, residential flexibility, smart grids, smart meter

Most of western countries have successfully deployed Smart Meters and are now in a phase of exploration to regulate and leverage the use of smart meter data. In France, the Linky smart meter provides data locally to the end-users in close to realtime, whereas it also makes 30 minutes data available to third party servers on the following day, which is inappropriate for operational energy services applications. In this work we detail the design of a full plug and play solution named Linky TIC Reader (LTR) to locally collect data from the French smart meter and make it available to end-users and to third party energy services providers in close to real time. Although many products to read Linky TIC output are already commercialised, they are usually externally supplied, unlike the LTR described in this work. This work aims to democratise access to an interoperable solution to control and automatically limit households' overall consumption at a low cost and for all distribution board configurations. Furthermore, this article describes several potential applications of the data collected by the LTR, such as load management, non-intrusive load monitoring and load forecasting.

I. INTRODUCTION

The energy transition requires the shift of several residential usages for electric technologies, such as electric vehicles and heat pumps [START_REF] Veyrenc | Energy Pathways to 2050[END_REF]. In parallel, the increase of gas prices has led many buildings, shared properties and homes to shift from gas based heating systems to electric heaters. However, electric distribution network operators (DNO) cannot always reinforce the grid locally to enable this power increase. In this case, endusers are often not allowed to increase their maximum power subscription, and the transition to electric heating requires them to manually switch off some other electric appliances when the heaters are turned on. Similarly, residential flexibility might also be required by the grid operators to prevent grid congestion. Such flexibility can be triggered either directly by the DNO, who can leverage the smart metering infrastructure to disconnect households that are in a highly constrained location, or by aggregators and energy services providers who can use smart devices at the households premises to provide energy services [START_REF]Voltalis, a solution for energy savings[END_REF], [START_REF]Charge angels: open source e-mobility and smart charging solution[END_REF]. Finally, residential flexibility can also be controlled directly by the end-users without any third party's intervention, in which case it can be done manually or automatically through smart devices. All these levels of flexibility control require the use of a device such as a smart meter to monitor and communicate the house's overall power.

In this paper, we focus on the case of France, that has already deployed more than 35 millions of its smart meter Linky. As France is now working on regulations of smart meters' data usage [START_REF] Cre | définition du nouveau cadre de regulation du projet linky[END_REF], this paper first aims to describe a lowtech solution to collect smart meter's real time data. Second, the paper aims to discuss the potential benefits and usage of Linky's real-time data, especially in the context of residential flexibility procurement as required by the energy transition.

Section II describes the current context and need for flexibility at households level. Then, Section III presents a solution to collect and make available the French smart meter real-time data. Finally, Section IV discusses the experimental results and potential applications.

II. SMART METER ENABLED FLEXIBILITY A. Needs for Residential Flexibility

The electrification of transport and heat will strongly impact the distribution network in terms of voltage excursion and excessive network cables heat at specific times of the day. To visualise these impacts, we ran power flow studys on the standard European Low Voltage network feeder [START_REF]IEEE PES Test Feeder, European Low Voltage Test Feeder[END_REF] for different deployment rates of EVs and HP. Households and EVs load profiles used are minutely data that come from ReFLEX, the UK largest smart local energy system demonstrator in the Orkney Islands, that monitored over 140 EVs; whereas HP load profiles came from the large scale trial "Renewable Heat Premium Payment Scheme" [START_REF] Lowe | Renewable Heat Premium Payment Scheme: Heat Pump Monitoring: Raw Data 2013-2015[END_REF] that monitored hundreds of heat pumps. The average daily number of minutes with voltage excursions (below the lower voltage limit) was recorded and is displayed in Fig. 1 as a function of the deployment rate of EV and HP. It shows that some neighbourhoods might experience more than one hour per day of local voltage constraints. These constraints could either be solved by expensive grid reinforcement, or by residential flexibility coordinated by a third parties (Distribution System Operators and aggregators).

Along with the energy transition, the gas prices' increase has led several households with gas heating to change for electric heating without being given the possibility to increase their maximum power subscription to cover the load increase in Winter. Therefore, several households have experienced instantaneous power cut due to excessive power consumption. This can be seen in Fig. 2 where an apartment with a 6kVA subscription was disconnected from the grid at 18:44 by the smart meter circuit breaker due to a power consumption excess from electric loads including water storage heater, space heaters and cooking plates. A similar issue could also be faced by households acquiring an EV, as shown by the purple curve in Fig. 2). Such situations could be avoided thanks to households flexibility enabled by real-time smart meter data.

B. French Smart Meter Characteristics

The French smart meter Linky provides two types of data that are shown in Fig. 2. One (in grey) with a 30minutes time interval, that is automatically sent at a remote concentrator of the French DSO through power line communication [START_REF] Gougeon | Modeling the end-to-end energy consumption of a nation-wide smart metering infrastructure[END_REF], and that is made available to the end-users on the following day, making it inappropriate for use in operational decisions for electric flexibility. The other type of data is real-time data available locally at a time granularity below 5 seconds through connectors of the smart meter named the TIC (Tele-Information Client) and visible in Fig. 4. However, collecting this data requires an additional electronic device to decode the TIC data and send it to a back-end that would be able to inform the end-users of load power excess or to control smart appliances. Although such device can theoretically be supplied directly by the smart meter itself, it is made complicated by the fact that Linky only provides an AC signal of 6V rms at 50kHz with a minimum power of 130mW [START_REF] Enedis | Sorties de tele information client des appareils de comptage Linky[END_REF], which is very low to supply an electronic device that aims to send data in real-time to a web service through an internet or a LoRa connection.

This section demonstrated the need for real-time monitoring of households' load consumption in order to provide residential flexibility. In the case of the French infrastructure, it was shown that such monitoring can be done by the smart meter Linky, but it requires the integration of an additional electronic device. The next section will describe the design of such a device aiming to automatically read the household's power consumption from the smart meter, send data to third party services, and provide real-time control of local assets such as heating devices or electric vehicles.

III. SOLUTION FOR FRENCH SMART METER DATA COLLECTION

This section describes the details of a Linky TIC Reader (LTR). The main constraints are the low power supply available at the Linky connection, and the space limitation to integrate such a device into Linky's casing. The power supplied by Linky for a LTR is slightly greater than 130mW, whereas the minimal power requirement to send any message is between 400 and 560mW over a few seconds for WIFI protocol, and between 60 and 400mW for LoRa communication. Therefore, the main approach for a LTR is to store energy in a large supercapacitor, and to power up the LTR when there is enough energy to read Linky's data. Then, if the energy is large enough for data transmission, the LTR can send data to a third party web service through an API. It is therefore necessary to consider four main operations: first, the charging phase of a supercapacitor, that occurs every time the other operations have emptied the capacitor, second the recording phase, that aims to read data from the smart meter. Third, the action phase, that consists in switching on or off flexible assets directly connected to the LTR in case of high household power consumption, and finally the communication phase that sends data to a remote third party for energy or non-energy services.

A. Architecture of the LTR

The LTR is composed of 4 main elements. The power supply, the demodulation, the communication and control, and the actuator modules. The links between all of these elements and their schematic is proposed in Fig. 3. Because WIFI constraints are higher than for other protocols, the rest of the paper focusses on a Wifi version that is fully replicable in a LoRa or GSM configuration. Each one of the four elements presented in Fig. 3 are described in the next subsections.

B. Demodulation Module

The demodulation module aims to demodulate the signal that is made available by the Tele-information Client (TIC) connector of Linky smart meter. It adopts a well-adopted optocoupler based architecture that is already available to purchase on the market, and will therefore not be described here. We highlight however that there are two versions of the French smart meter, that communicate either at 1200bps or at 9600bps, which require a demodulator resistor value of 22kΩ or 12kΩ respectively, as shown in Fig. 3.

C. Actuator

The actuator aims to connect or disconnect a flexible load depending on the household's power consumption. It consists of a latching relay similar to the one in [START_REF] Adafruit | Adafruit Latching Mini Relay FeatherWing[END_REF] with two coils in order to limit the power consumption from the control module. Small loads can directly be connected to the relay, whereas large loads require the use of an intermediary circuit breaker as it is done for peak/off-peak hours circuit breakers [START_REF] Schneider-Electric | Contacteur heures creuses[END_REF] and shown in Fig. 3.

D. Power Supply Module

Although it is possible to directly supply the demodulation and communication modules using a USB power adaptor with an electric plug from the house distribution board, this paper focusses on the design of an integrated solution that directly powers from the smart meter and that would fit in any household configuration. The power supply aims to rectify the AC power available, to charge a supercapacitor that acts as an energy tank and to connect this supercapacitor to the rest of the circuit (i.e. the demodulation, communication and actuator module) through the MOSFET Q 1 (right side of Fig. 3) when C 1 is enough charged. The indication that the supercapacitor is charged enough is given by its voltage (V 1 = 2E C1 ), which is monitored by the power supply module and used to decide to supply or cut off the rest of the circuit. Although there are not many other options, using V 1 as the main indicator to supply or not the rest of the circuit brings a great challenge: indeed, although V 1 increases when the rest of the circuit is not connected, it will then directly decrease rapidly once the circuit is connected, which will lead to a disconnection of the rest of the circuit before it was able to finish its operation. Therefore, there is the need for a hysteresis control loop such that the connection of the rest of the circuit through Q 1 is triggered at a high voltage threshold for V 1 , and it should be kept switched on until V 1 decreases below a much lower voltage threshold.

Although a Schmitt trigger could provide such hysteresis control in theory, it can not work correctly in the case of Linky as the trigger's input voltage would be directly proportional to its supply voltage (i.e. V 1 , the voltage at C 1 output). Therefore, we chose to use a low power microcontroller U 1 to control the voltage of the hysteresis and to control the supply of the rest of the circuit through the switching of Q 1 . The microcontroller implements the following logic: if the capacitor voltage has been above 4.5V for at least 20 consecutive cycles (with a low clock frequency of 1MHz), the switch Q 1 is switched on. This connects the ground voltage level of the communication module with the ground level of V 1 , which will supply the communication and control module with the 3.3V voltage regulator (MCP1825). The switch Q 1 is kept switched on until the voltage V 1 drops below a threshold of 4.1V for at least 200 cycles. As the microcontroller U 1 is directly supplied by V 1 , which is also the voltage that it aims to monitor, this voltage measurement has to be done by an analog input of U 1 that measures the voltage at the end of a 3.3V Zener diode D 1 . The total cost for a single power supply module is below 13 C.

E. Communication and Control Module

In normal operation, the communication module of the LTR achieves three main purposes. First, it wakes up from sleep mode and reads Linky data made available at the output of the demodulation module and stores it in an internal EEPROM memory before going back to sleep mode to save energy. Second, before going back to sleep: 1. If a load is connected to its actuator, the LTR takes the decision to switch on or off the actuator (which connects/disconnects the flexible electric appliance such as water storage heater or EV) based on the instant power consumption measured by the smart meter: if the house power consumption is greater than a predetermined threshold based on the household's power subscription, it is urgent to switch off flexible appliances. 2. Similarly, in the case where some flexible appliances are remotely controlled by a third party service, the communication module will send a switch off request through WIFI or LoRa to the third party web service regardless of the energy quantity stored into the supercapacitor C 1 . This will allow the third party service to put back the household to safe electric consumption by remotely switching off their smart appliances such as EV chargers. Finally, regardless of the household power consumption value, if the voltage V 1 is high enough, the communication module connects to a registered WIFI network and sends all the previously recorded data to a remote server (e.g. third party web service).

Before achieving this normal operation mode, the communication module must be configured in order to connect to the right WIFI SSID and to store the web addresses to which data should be sent. This is done at the initialisation phase of the LTR by having the communication module set up in Access Point mode with an internal web server stored to get the user's inputs to successfully connect to an existing WIFI.

In order to propose an open source design that anyone could reproduce and install at their household, we worked with the compact, fully democratised and easy to use ESP8266 based WEMO D1 mini Lite boards, that can be purchased for less than 10C. One of the main advantage of such boards is that many libraries have been developed to help the implementation. More specifically, such boards have all the Transport Layer Security protocols available in dedicated libraries. Therefore, this choice allows an easy implementation of security layers to safely connect to https servers. For what concerns security aspects, we have configured a Wificlient secure instance to set trust anchors (third party servers) using their real https certificates delivered by a root SSL certification authority. The drawbacks of using secure connection to remote servers are discussed in Section IV. The final result of an LTR implementation is displayed without packaging in Fig. 4, with a single unit price below 30 C. All the schematics and codes to reproduce this Linky TIC Reader (LTR) are available as open source documentation from the github repository referenced in [START_REF] Couraud | Github Linky TIC Reader repository[END_REF].

IV. USE OF LTR FOR ENERGY SERVICES

In this section, we describe the experimental results of the LTR testing phase, and discuss several usages of this device.

A. Experimental results

The LTR was implemented, tested and deployed in several households for a period of over 6 months. Fig. 2 displays the data captured by one of the LTR in green, and in grey is displayed the associated smart meter data. The main learning from this implementation mostly concerns the time interval between two consecutive data transmission, as it is the only characteristics that depends on the external environment (power available at the smart meter's end). Table I shows the time interval between two consecutive transmission events to a remote server. We can see that because of the low power supply and the high energy requirements for the communication module, the time between two consecutive transmission of data by the LTR is always above 19 seconds. However, we also note that for custom applications, it is possible to send data as a bunch, instead of one data per transmission, which can be a workaround of this limitation. However, for third party services, the data might have to be sent individually and can be the average of all recorded values since the last transmission. Also, we can compare the case of a secure connection to the remote server(through https), and the case without any security considerations. Because of the required security handshake and encryption, the energy consumed by the LTR to send data with a secure connection is much higher than for a simple http connection. As a result, it takes more time for the LTR supercapacitor to retrieve an acceptable voltage level allowing the LTR to send data again (see Table I). Therefore, a lower frequency in data collection is the price to pay for a secure connection with encrypted data and effective authentication of the device and the server. In the rest of the paper, we discuss the potential use of LTR collected data for energy and nonenergy services.

B. Use cases for LTR data

LTR data consist in Linky smart meter data that is made available at a much higher frequency than the DNO does (less than 1min data for the LTR against 30 min data for the DNO services). However, it is not full real-time data as remote servers will receive data only once every 30-55 seconds. We discuss here the potential use of such data, following the list of applications proposed in [START_REF] Wang | Review of smart meter data analytics: Applications, methodologies, and challenges[END_REF].

1) Load Analysis : Load analysis mostly consists in the identification of issues in the load profile of a households (bad data, non-technical loss, profiling) which can be achieved with LTR data. Moreover, using LTR data, it is also possible to analyse further households' consumption and possibly contribute in the identification of residential flexibility procurement in case of participation in a flexibility event. Residential flexibility settlement require an aggregator to compute the flexibility effort from households that participated in such event (i.e. the flexibility delivered by the household compared to a baseline). It currently consists in the comparison of the actual consumption with an average consumption (based on 30min data) over several days prior to the time of delivery, called the baseline [START_REF]P376 Baseline Methodology[END_REF]. Adding capabilities such as the ones described in Section IV-B3 could help to identify actual flexibility efforts based on assets switching and not only load reduction.

2) Load Management: LTR can be used for participation in demand response schemes, but also to identify itself the need to reduce an household's power consumption, and then to switch off one of the flexible assets (heating appliances, EV chargers, ...) either by direct control, as described in Fig. 3, or through remote control by sending the control request to a remote server able to control flexible assets. 3) Non-Intrusive Load Monitoring: Non-intrusive Load Monitoring (NILM) consists in the identification of appliances' switching (on or off) based only on the mains' measurement, which can give information about the households' members activities and could be used by families for home based elderly monitoring. Although it usually requires high frequency resolution monitoring data (seconds to sub-second), we implemented state of the art NILM algorithms with LTR data to compare the capability to identify appliances with and without security layers. Fig. 5 shows the Root Mean Square Error (RMSE) for identification of a water storage heater in an apartment based on 6 weeks of data with a short period of calibration during which only the water heater storage appliance was connected to the mains. We can see that for each algorithm, the identification error is similar for all data transmission time except in the case of the Sequence to Point algorithm for which 55 seconds between two data transmission provided even better results in our case than any other algorithm used with 15 seconds data granularity. This demonstrates that if the TLR was to be used for NILM purposes, the addition of security layers would a priori not reduce the accuracy of the disaggregation. However, the calibration process might not be compatible with residential use cases.

4) Load Forecasting: Finally, LTR can be used for load forecasting purposes. It was reminded in [START_REF] Wang | Review of smart meter data analytics: Applications, methodologies, and challenges[END_REF] that load forecasting algorithms for individual households provide very low accuracy, and thus high Mean Absolute Percentage Error, especially compared to the case of aggregation with many other households. In this paper, we used LTR data gathered from experimentations held by the French Chair UX for Smart Life coordinated by the Universite Cote d'Azur to determine the impact of data aggregation in time on load forecasting accuracy, i.e. comparing the accuracy of a forecast of an household energy consumption for each hour of the day to the accuracy of forecasting the average energy consumption per day using k-Nearest Neighbours algorithm. V. CONCLUSION This paper first described the relevance of smart meter data and residential flexibility to enable the energy transition. It then described an open-source plug-and-play solution to read and send French smart meter data to third party energy services to control flexible assets and provide flexibility services. The main aim of this paper was to provide a full access to such solution and to democratise access to residential flexibility, which could certainly help to support the necessary energy transition. Finally, several usages of LTR data were described, showing that it can seamlessly be used for energy and nonenergy services, including load management, but also load analysis such as activity monitoring. Further work will include a more detailed study on the use of LTR data for elderly activity monitoring in the context of an ageing population.

Fig. 1 .

 1 Fig. 1. Simulated average number of minutes in a day with Low Voltage network voltage excursions in a European neighbourhood.

Fig. 2 .

 2 Fig. 2. Example of an EV charger load curve and of a winter's household electric load with a power subscription of 6kVA, and disconnection at 18:44.

Fig. 3 .

 3 Fig. 3. Global schematic of the LTR that controls a water storage heater.

Fig. 4 .

 4 Fig. 4. Integration of the WIFI based Linky TIC Reader (LTR) device for Linky local data collection and transmission to third party energy services.

Fig. 5 .

 5 Fig. 5. Comparison of RMSE for 4 state of the art NILM algorithms used for different frequency of data transmission (15, 30 and 55 seconds).

Fig. 6 .

 6 Fig. 6. MAPE accuracy for prediction of a single house load profile for different levels of data aggregation.

Fig. 6

 6 Fig.6shows that the accuracy increases considerably with the aggregation of data in time. It is indeed much more accurate to forecast energy consumption over a day than over an hour, mainly because of a reduction of data variability. This outcome could inform the design of local energy markets, as it shows that an individual household's load forecast with aggregation time below 4hours might lead to high load forecast errors, which makes it unsuited for local energy markets with clearing time intervals below 4H.

TABLE I IMPACTS

 I OF SECURITY LAYERS ON LTR DATA TRANSMISSION.

	Operation mode	time interval between 2 transmissions
		mean (s) max (s)	min (s)
	with security layers	54.6	140	20
	without security	33	68	19

ACKNOWLEDGMENT

This paper was funded by the French partnership Chair UX for Smart Life: Home and Mobility coordinated by IMREDD institute at the Universite Cote d'Azur, and by the UK EPSRC DISPATCH project (EP/V042955/1).


Julie Keisler
email: julie.keisler@edf.fr

Etienne Le Naour
email: etienne.le-naour@edf.fr

WINDDRAGON: ENHANCING WIND POWER FORE-CASTING WITH AUTOMATED DEEP LEARNING

Achieving net zero carbon emissions by 2050 requires the integration of increasing amounts of wind power into power grids. This energy source poses a challenge to system operators due to its variability and uncertainty. Therefore, accurate forecasting of wind power is critical for grid operation and system balancing. This paper presents an innovative approach to short-term (1 to 6 hour horizon) wind power forecasting at a national level. The method leverages Automated Deep Learning combined with Numerical Weather Predictions wind speed maps to accurately forecast wind power.

INTRODUCTION

To meet the 2050 net zero scenario envisaged by the Paris Agreement (United Nations Convention on Climate Change, 2015), wind power stands out as a critical energy source for the future. Remarkable progress has been made since 2010, when global electricity generation from wind power was 342 TWh, rising to 2,100 TWh in 2022 (International Energy Agency (IEA), 2023). The IEA targets approximately 7,400 TWh of wind-generated electricity by 2030 to meet the zero-emissions scenario. However, to realize the full potential of this intermittent energy source, accurate forecasts of wind power generation are needed to efficiently integrate it into the power grid.

Research in wind power forecasting has developed a wide range of methods [START_REF] Giebel | Wind power forecasting-a review of the state of the art[END_REF][START_REF] Tawn | A review of very short-term wind and solar power forecasting[END_REF], including statistical [START_REF] Riahy | Short term wind speed forecasting for wind turbine applications using linear prediction method[END_REF], physical [START_REF] Lange | Physical approach to short-term wind power prediction[END_REF], hybrid [START_REF] Shi | Evaluation of hybrid forecasting approaches for wind speed and power generation time series[END_REF], and deep learning (DL) [START_REF] Wang | A review of wind speed and wind power forecasting with deep neural networks[END_REF] approaches. These methods use a variety of data sources, including historical wind power records, geospatial satellite data, on-site camera imagery, and numerical weather prediction (NWP) forecasts. Among these, typical NWP-based methods primarily focus on using local time series of wind speed forecasts for local wind power prediction [START_REF] Piotrowski | Advanced ensemble methods using machine learning and deep learning for one-day-ahead forecasts of electric energy production in wind farms[END_REF]. However, NWP forecasts produce richer outputs, notably spatial predictions of physical quantities such as wind speed and direction over large scale grids (e.g. national or regional). Predicting aggregated (e.g national or regional) wind power from such fine-grained spatial information appears promising and is largely unexplored in the literature [START_REF] Higashiyama | Feature extraction of nwp data for wind power forecasting using 3d-convolutional neural networks[END_REF]. Thus, we propose to explore how wind speed maps combined with suitable machine learning models can capture complex patterns, improving large scale wind power predictions.

In this work, we propose to leverage the spatial information in NWP wind speed maps for national wind power forecasting by exploiting the capabilities of DL models. The overall methodology is illustrated in Figure 1. To fully exploit DL mechanisms potential, we introduce WindDragon, an adaptation of the DRAGON 1 [START_REF] Keisler | An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters[END_REF] framework. WindDragon is an Automated Deep Learning (AutoDL) framework for short-term wind power forecasting using NWP wind speed maps. WindDragon's performances are benchmarked against conventional computer vision models, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), as well as standard baselines in wind power forecasting. The experimental results highlight two findings:

• The use of full NWP wind speed maps coupled with DL regressors significantly outperforms other baselines.

• WindDragon demonstrates superior performance compared to traditional computer vision DL models.

Predicted wind speed map

Numerical Weather Predictions (NWP) 6-hours ahead

Step 2: Pass the wind speed maps through the proposed regressor

Prediction of the regional wind power gener ation For a given hor izon h :

Step 1: Get wind speed maps predictions from the NWP model Deep L ear ning regressor

Figure 1: Global scheme for wind power forecasting. Every 6 hours, the NWP model produces hourly forecasts. Each map is processed independently by the regressor which maps the grid to the wind power corresponding to the same timestamp.

WINDDRAGON: A FRAMEWORK FOR REGRESSION ON WIND SPEED MAPS

Deep Learning models have the ability to capture complex spatial patterns, which makes them well suited for modeling non-linear relationships between meteorological features and wind energy production. These models are especially useful when wind farms are scattered across the map (see Figure 3) and wind speed has significant variance across locations. [START_REF] Zhang | Making convolutional networks shift-invariant again[END_REF] can hinder wind power forecasting because identical wind speeds at different map locations do not equate to the same power generation due to the uneven distribution of wind farms. Conversely, ViTs excel at image classification by segmenting images into patches and applying self-attention mechanisms, but the size of the considered datasets (less than 20000 points for the training dataset) might limit their effectiveness. Given these concerns, the use of AutoDL frameworks to automatically identify the most appropriate DL architecture is a promising solution.

The DRAGON framework. DRAGON [START_REF] Keisler | An algorithmic framework for the optimization of deep neural networks architectures and hyperparameters[END_REF] is an AutoDL framework which automatically generates well-performing deep learning models for a given task. Compare to other AutoDL frameworks [START_REF] Liu | Darts: Differentiable architecture search[END_REF][START_REF] Hutter | Automated machine learning: methods, systems, challenges[END_REF][START_REF] Zimmer | Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl[END_REF][START_REF] Deng | Efficient automated deep learning for time series forecasting[END_REF], DRAGON provides a flexible search space, which can be used on any task. It allows the extension of the possibilities in terms of architectures and is adapted when the type of architecture to use is unclear or when high performance is sought by tuning hyperparameters. We used several tools from the generic framework to adapt it for wind power forecasting from wind speed maps.

WindDragon: adapting the DRAGON framework for wind power forecasting. The neural networks in DRAGON are represented as directed acyclic graphs, with nodes representing the layers and edges representing the connections between them. In our case, a value ŷt ∈ R is predicted from a 2D map X t ∈ R m×n . The search space is then restricted to a specific family of constrained architectures, as represented in Figure 2. A first graph processes 2D data and can be composed by convolutions, pooling, normalization, dropout, and attention layers. Then, a flatten layer and a second graph follow. This one is composed by MLPs, self-attention, convolutions and pooling layers. A final MLP layer is added at the end of the model to convert the latent vector to the desired output format. We optimized the solutions from our search space using an evolutionary algorithm, as detailed Appendix A.

EXPERIMENTS

Datasets. The wind speed maps used are 100-meter high forecasts at a 9 km resolution provided by the HRES2 model from the European Centre for Medium-Range Weather Forecasts (ECMWF).

The maps are provided at an hourly time step and there are 4 forecast runs per day (every 6 hours).

Only the six more recent forecasts are used here as the forecasting horizon of interest is six hours. The hourly french regional and national wind power generation data came from the french TSO3 .

Data preparation. The national forecast of wind power generation is obtained by summing the forecasts of the 12 administrative regions of Metropolitan France. According to our first experiments, this bottom-up technique produced better results than predicting national production directly.

The division of a national map into regions is a challenge, as shown in Figure 3 as wind turbines are not evenly distributed across the regions. Therefore, we selected areas around each wind farm in the region and took the convex hull of all the considered points. The result is a seamless map that includes local wind turbines with no gaps to disrupt the models. Installed capacity data for each region -corresponding to the maximum wind power a region can produce -is available and updated every three months. It was collected and used to scale the wind power target. Years from 2018 to 2019 are used to train the models, and data from 2020 is used to evaluate how the models perform. We use the following baselines to compare hourly forecasts for an horizon h (h ∈ {1, ..., 6}):

• Persistence: Predicts wind power generation at future time t + h as equal to the observed generation at current time t.

• XGB on Wind Speed Mean: Forecasts wind power at t + h using a two-step approach: (i) Compute the mean wind speed for the considered region at t + h using NWP forecasts.

(ii) Apply an XGBoost regressor [START_REF] Chen | Xgboost: A scalable tree boosting system[END_REF] to predict power generation based on the computed mean wind speed.

• Convolutional Neural Networks (CNNs). Forecasts wind power at t + h using the NWP predicted wind speed map. CNNs can efficiently regress a structured map on a numerical value by learning local and spatial patterns [START_REF] Lecun | Convolutional networks for images, speech, and time series[END_REF].

• Vision Transformers (ViTs) Forecasts wind power at t + h using the NWP predicted wind speed map. The map is segmented into patches and a self-attention mechanism is used to capture the dependencies between these patches [START_REF] Dosovitskiy | An image is worth 16x16 words: Transformers for image recognition at scale[END_REF].

The implementation details of the baselines are described in Appendix B.

We compute two scores: Mean Absolute Error (MAE) in Megawatts (MW), showing the absolute difference between ground truth and forecast, and Normalized Mean Absolute Error (NMAE), a percentage obtained by dividing the MAE by the average wind power generation for the test year.

Results. We run experiments for each of the 12 French metropolitan regions and then aggregate the predictions to derive national results. The national prediction results are presented in Table 1, while detailed regional results can be found in The results in Table 1 highlight three key findings:

(i) Improved performance with aggregated NWP statistics. Using the average of NWPpredicted wind speed maps coupled with an XGB regressor significantly outperforms the naive persistence baseline. (ii) Gains from full NWP map utilization. More complex patterns can be captured by using the full predicted wind speed map, as opposed to just the average, thereby improving forecast accuracy. In this context, both the ViT and CNN regressors applied to full maps yielded gains of 31 MW (7.4%) and 47 MW (11.5%), respectively, over the mean-based XGB. (iii) WindDragon's superior performances. WindDragon outperforms all baselines, showing an improvement of 22 MW (6%) over the CNN. On an annual basis, this corresponds to approximately 193 GWh, which is equivalent to the annual consumption of a French town of 32,000 inhabitants4 . Refer to Appendix A for WindDragon's architecture example.

In Figure 4, we present the aggregated national wind power forecasts using both WindDragon and the CNN baseline during a given week. While both models deliver highly accurate forecasts, it's important to highlight that DRAGON demonstrates superior accuracy, particularly in predicting high peak values. See Appendix C.2 for visual comparisons of all baselines performances.

CONCLUSION AND IMPACT STATEMENT

In this paper, we have presented two key findings that show great promise. First, using NWP wind speed forecasts as a map significantly improves forecast accuracy compared to using only aggregated values. Second, our framework, WindDragon, shows superior performance to all other baseline models. The significant improvement provided by WindDragon is particularly critical in light of the increasing reliance on wind energy, driven by the pursuit of the net-zero scenario.

Future work could adapt our methodology for photovoltaic (PV) systems, applying it to solar radiation maps generated by NWP models. While current deep learning research in PV primarily focuses on short-term nowcasting (Le Guen, 2022), our method holds promise for extending the forecasting horizon, potentially improving the efficiency and reliability of solar power predictions.

A WINDDRAGON

Search algorithm. The DRAGON framework contains operators, namely mutation and crossover, which are commonly used in meta-heuristics such as the evolutionary algorithm and the simulated annealing, to optimize graphs. The mutation operators are used to add, remove, or modify nodes and connections in the graph, as well as to modify the operations and their hyperparameters within the nodes. Crossover involves exchanging parts of two graphs. The mutation and crossover operators were utilised to construct a steady-state (asynchronous) evolutionary algorithm. Compare to the original algorithm, this version enhances efficiency on HPC by producing two offsprings from the population as soon as a free process is available, rather than waiting for the entire population to be evaluated [START_REF] Liu | Hierarchical representations for efficient architecture search[END_REF].

With the division by region, we slightly modified the generic evolutionary algorithm in WindDragon to avoid having to run an optimisation by region, which would be very costly. In this context, a deep neural network f from our search space Ω is parametrized by its architecture α and its hyperparemeters λ. Once α and λ have been settled, the model is trained on the data to optimize the weights θ. We assumed that the architecture α and the hyperparameters λ would be broadly similar across regions. Therefore, we modified our evolutionary algorithm to process all regions at the same time. We create and evolve α and λ independently of the region, and, to optimize the weights θ, we randomly select the region on which the model would be train and evaluate. In order not to penalize models that have been evaluated on regions that are difficult to predict, we use a global loss function, which consists in dividing the loss obtained on the region ℓ region by the loss of our baseline CNN model on that region, L region . During the optimisation, for each region, we progressively save the best model evaluated on it. The pseudo code for our steady-state evolutionary algorithm can be found Algorithm 1.

Algorithm 1: Steady-state evolutionary algorithm for multi-regions wind power forecasting Inputs: Ω search space [L region 1 , . . . , L region R ] CNN losses for each region K population size T number of iteration Initialization Sample K untrained models f α1,λ1 , . . . , f α K ,λ K from Ω For k = 1, 2, . . . , K Select a region r to train the model Train f α k ,λ k to get the model weights θ r k on the region r Get the loss ℓ k r on this region, and set the model loss to

ℓ k = ℓ k r /L r If ℓ k r is the best loss so far on r, save f α k ,λ k θ r k For t = K + 1, K + 3, K + 5, . . . , T
Select two parents f α k1 ,λ k1 and f α k2 ,λ k2 from the population based on their loss ℓ k Mutate and evolve f α k1 ,λ k1 and f α k2 to f α K+t ,λ K+t and f α K+t+1 ,λ K+t+1 Select two regions r A and r B Train respectively f α K+t ,λ K+t and f α K+t+1 ,λ K+t+1 on r A and r B to optimize θ r A K+t and θ The best saved model by region

XGboost on the mean of the NWP wind speed map. Figure 7 shows the two-steps procedure of the XGboost baseline.

Step 1 : Compute the wind speed mean inside the convex hull

Step 2 : Applied an XGboost regressor

Figure 3 :

 3 Figure 3: Data preparation for the region Auvergne-Rhône-Alpes. The wind farms are represented in red. The first image shows the distribution of wind farms across the administrative region.

Figure 4 :

 4 Figure 4: Wind power forecasts for a week in January 2020. The figure displays the ground truth as dotted lines, and the forecasts from the two top-performing models, WindDragon and the CNN.

  the models losses to ℓ K+t = ℓ K+t rA /L rA and ℓ K+t+1 = ℓ K+t+1 rB /L rB If ℓ K+t rA or ℓ K+t+1 rB are respectively the best losses so far on r A or r B save fα T +h ,λ T +h θ r A or f α T +h+1 ,λ T +h+1 θ r BIf ℓ K+t or ℓ K+t+1 are lower than the maximum population loss, we replace the worst model with the new one Output:

Figure 7 :

 7 Figure 7: Visual illustration of the XGB two-steps approach on the Auvergne-Rhône-Alpes region.

Table 2 (

 2 Appendix C).

Table 1 :

 1 National results: sum of the regional forecasts for each models. The best results are highlighted in bold and the best second results are underlined.

		WindDragon	CNN		ViT		XGB on mean	Persistence
		MAE (MW) NMAE MAE (MW) NMAE MAE (MW) NMAE MAE (MW) NMAE MAE (MW) NMAE
	France	346.7	7.7 %	369.0	8.1 %	385.7	8.5 %	416.7	9.2 %	779.7	17.3 %

Table 2 :

 2 Regional results. The best results are highlighted in bold and the best second results are underlined.

		WindDragon	CNN		ViT		XGB on mean	Persistence
	Region	MAE (MW) NMAE MAE (MW) NMAE MAE (MW) NMAE MAE (MW) NMAE MAE (MW) NMAE
	Auvergne-Rhône-Alpes	19.5	14.9 %	19.6	15.0 %	21.6	16.5 %	29.2	22.4 %	28.7	22.0 %
	Bourgogne-Franche-Comté	32.9	14.8 %	34.1	15.4 %	37.2	16.8 %	42.3	19.1 %	58.7	26.6 %
	Bretagne	36.1	14.1 %	38.0	14.9 %	39.9	15.6 %	47.1	18.4 %	67.2	26.3 %
	Centre-Val de Loire	53.3	15.0 %	57.3	16.1 %	59.0	16.6 %	61.9	17.5 %	96.7	27.3 %
	Grand Est	125.6	12.5 %	130.5	13.1 %	161.0	16.1 %	148.8	14.9 %	251.2	25.1 %
	Hauts-de-France	159.7	12.1 %	167.6	12.7 %	177.0	13.4 %	178.8	13.5 %	320.1	24.2 %
	Île-de-France	6.8	22.6 %	7.17	23.7 %	7.4	24.3 %	7.5	24.9 %	9.5	31.5 %
	Normandie	29.6	12.7 %	30.8	13.2 %	31.2	13.4 %	36.8	15.8 %	55.9	24.0 %
	Nouvelle-Aquitaine	43.1	15.7 %	44.0	16.4 %	48.4	17.6 %	53.7	19.6 %	77.9	28.4 %
	Occitanie	51.2	12.3 %	55.8	13.5 %	64.1	15.5 %	91.6	22.1 %	96.3	23.2 %
	PACA	3.5	32.4 %	3.5	32.4 %	4.0	37.2 %	4.5	41.4 %	4.3	39.5 %
	Pays de la Loire	37.1	13.6 %	39.0	14.3 %	39.9	14.7 %	41.9	15.4 %	74.9	27.5 %

https://www.ecmwf.int/en/forecasts/datasets/set-i

https://www.rte-france.com/eco2mix

based on the average European per capita consumption(Statista Research Department, 2022)

https://github.com/lucidrains/vit-pytorch

://dragon-tutorial.readthedocs.io/en/latest/index.html

Grand Est Convex Hull

Identity,Swish Self-Attention,28,Swish Self-Attention,6,Swish AvgPool2d,4,Swish Flatten Identity,LeakyReLU Self-Attention,28,Sigmoid LayerNorm1d,Sigmoid MLP,1,Linear Results. The outputs of WindDragon would be by region the best model found during the optimisation and the prediction of this model. The found architectures vary a bit from a region to another. An example of the best model for the region Grand Est can be found Figure 5. This architecture uses self-attention just like in the Transformer [START_REF] Vaswani | Attention is all you need[END_REF], but without the patches that can be found in the ViT architecture. The model is also a lot smaller than a Transformer, which can explain why it outperforms the other baselines on this region

B BASELINES DETAILS

The baselines used in Section 3 are explained in more detail below.

Convolutional Neural Network (CNN). Figure 6 shows the architecture of the CNN baseline that we implemented. We used a simple grid search to optimize the hyperparameters (e.g. the number of layers, the kernel sizes, the activation functions) Vision Transformer (ViT). The Vision Transformer used in this paper is based on SimpleViT's [START_REF] Beyer | Better plain vit baselines for imagenet-1k[END_REF] architecture. We reused the implementation from lucidrains package 5 .