Alaa Krayem

Prof Nesreen Ghaddar

Prof Jean

Mariam Samah

Ahmad Al Bitar

Ghaleb Faour

Sara Najem

Revealing the spatio-temporal energy consumption of a Mediterranean city: the case of Beirut

To reduce greenhouse gas emissions and energy consumption in urban areas, understanding buildings energy performance and consumption patterns is essential for implanting effective energy management and efficiency strategies at a city scale.

Such plans' implementation at large scale requires information on how the energy demands may change under specific interventions. Urban Building Energy Models (UBEM) are proposed tools to estimate current and future building's energy demand.

These models rely on a bottom-up approach, combining both statistical techniques and physics-based methods.

First of all, I would like to express my gratitude and appreciation for my thesis

This study aims at providing an enhanced modeling approach that simulates buildings' energy demand at high spatial and temporal resolution, which can help in evaluating energy management strategies and decision-making energy policies. The methodology is applied for the city of Beirut, representative of the Mediterranean region where the similarity of buildings technologies and climatic concerns among its cities is pronounced. The main objectives of the thesis are to develop, investigate and adjust a bottom-up energy modeling tool at urban scale; to provide evidence of the tool's suitability to support guidelines for future interventions; and finally to investigate the impact of the city's compactness on daylight availability and thus citizens' well-being.

In this case study based on two different districts within the city, a near-city-scale building energy model, BEirut Energy Model BEEM, is generated to estimate the building's stock electricity consumption. To reduce the modeling and computation time, an archetypal classification of the buildings based on their types and periods of construction is adopted. The additional information required to generate the 3D i model of the buildings are the number of floors, buildings' areas and a topographic map of the study areas. By coupling the models to the hourly weather conditions, the thermodynamic model of 3,630 buildings is simulated in EnergyPlus.

Adapting the model to Beirut's occupancy and users' behaviors is crucial to enhance the reliability of BEEM. The availability of metered electricity data allows the model calibration, which is based on buildings' clustering and finding the clusters' coefficients representative of specific energy patterns. After the training phase, the model's accuracy in predicting electricity consumption is improved. Comparing the actual consumption and the calibrated results, the averaged absolute percentage error of the electricity consumption was reduced from 310% to 41% in district A and from 326% to 39% in district B.

The calibrated model is combined with Geographic Information System (GIS) for a spatiotemporal distribution of energy demand patterns, which can help in assessing the most suitable intervention technologies. An analysis of the spatial distribution of electricity use demonstrates a spatial clustering that underlies urban energy demand which can be used for smart grid zoning.

The urban morphology affects the solar potential in an urban setting, which is a major driver in building's energy demand. Particularly, the daylight availability is examined by investigating its link with urban metrics such the buildings' orientation and heights. Revealing how sensitive such links are helps in optimizing urban design and structure, and informs retrofitting intervention strategies.

Keywords-Urban energy modeling, energy model calibration, decision support system, building energy performance, archetype classification, daylight accessibility ii

Résumé

Pour réduire les émissions de gaz à effet de serre et la consommation d'énergie dans les zones urbaines, il est essentiel de comprendre les performances énergétiques et les modes de consommation des bâtiments pour pouvoir mettre en oeuvre des stratégies efficaces de gestion de l'énergie et d'efficacité énergétique à l'échelle de la ville. La mise en oeuvre à grande échelle de tels plans nécessite des informations sur la manière dont les demandes en énergie peuvent changer dans le cadre d'interventions spécifiques. Les modèles énergétiques de bâtiments à l'échelle urbaine (UBEM) sont des outils proposés pour estimer la demande énergétique actuelle et future des bâtiments. Ces modèles reposent sur une approche ascendante (bottomup approach) combinant à la fois des techniques statistiques et des méthodes basées sur la physique thermodynamique.

Cette étude vise à fournir une approche de modélisation améliorée simulant la demande énergétique des bâtiments à haute résolution spatiale et temporelle, ce qui peut aider à évaluer les stratégies de gestion de l'énergie et les politiques énergétiques décisionnelles. La méthodologie est appliquée pour la ville de Beyrouth, représentative de la région méditerranéenne, où la similarité des technologies de construction et des préoccupations climatiques de ses villes est prononcée. Les objectifs principaux de la thèse sont de développer, étudier et calibrer un outil de modélisation énergétique ascendante à l'échelle urbaine ; fournir des preuves de la pertinence de l'outil pour soutenir les directives pour les interventions futures ; et enfin, étudier l'impact de la compacité de la ville sur la disponibilité de la lumière du jour et donc sur le bien-être des citoyens.

Dans cette étude de cas basée sur deux quartiers différents de la ville, un modèle énergétique de bâtiment à échelle urbaine approximativement, applé BEirut Energy Model [2]. Urban area are encountering unprecedented growth because of rural exodus due to socioeconomic factors such as seeking better job opportunities or higher education levels. Additionally, the development of the economy along with the concentrated industrialization in some areas at the expense of others, together with migration from poor countries suffering from economic problems, or asylum of citizens escaping from wars and political crisis zones are all contributing factors to the increase in urban population. Urbanization has the potential to make cities more prosperous and countries more developed, by creating wealth, generating employment and driving human progress. However, many cities around the world are suffering from persisting issues: increased residency in slums and informal settlements, challenges in providing urban services, climate change, exclusion and rising inequality, insecurity upsurge in international migration [3]. Among the climate challenges in urban environments is the urban warming measured in numerous cities across the world in addition to the Green House Gas (GHG) emissions [4]. One factor of the increased urban temperatures is the dominance of the artificial character of the cities at the expense of the natural green fractions. Another aspect of the urbanization is the economical structural change of the country. The concentration of the economic activities in the cities drives the labor force to shift from the agriculture sector to the industrial or services sectors in the city, phenomenon well-known as tertiarization. This transfer that accompanies the rural-urban migration, contributes indirectly in the increase in the energy consumption. First, due to lack in labor force, agriculture products have to be mechanized and transported from areas of production to cities. Secondly, the needed transport services increase, leading to an increase in fossil fuels demand and hence, in its impacts on the urban climate. Last but not least, the economic growth in cities affects the behaviors and lifestyles of residents who tend to purchase commercial products and services, for example electrical appliances and accessories, increasing the energy consumption [5]. The city being a major energy consumer and GHG emissions contributor, a relevant understanding of its metabolism is pivotal in developing energy efficient strategies [6]. In order to ensure the optimization and the prioritizing of the energy conservation measures (ECM) to be applied, programmatic decision making or energy management is required. It consists of planning, implementing and monitoring energy supply, distribution and use in effective and efficient manners to reduce its waste and consumption. Furthermore, it studies the alternative and environment friendly resources, technical organization, cost effectiveness, and behavioral shifting to improve energy quality, availability and impacts on environment and nature [7]. However, understanding how a city's energy system changes over space and time under these interventions is crucial to support the decision making process. The building sector has been identified as responsible of 30%-70% of primary energy consumption in cities [8] and 30% of the gas emissions [9]. Therefore, developing Building Energy Models (BEM) at city scale are essential for energy supply management. These models have been developed and served as design enabler tools at individual building level [10]. They are typically used at the early design stage and throughout the design process to evaluate various design options and optimize the overall performance of building systems. Extending the energy modeling scope to the urban scale allows the assessment of building to building interactions (shading, heat exchange,etc.), and of building to other urban components interactions such as the urban heat island and traffic. City scale energy balance models [11,12,13] based on top-down approaches are used for climatic modeling but they do not provide the needed details to test innovative scenarios at building scale. On the other hand, bottom-up physical simulation models were introduced as effective simulation tools to model the impact of the urban context on buildings energy demand [14,15,16,17]. In this context, Reinhart and Davila [18] introduced the Urban Building Energy Models (UBEM), bottom-up physical simulation models as effective simulation tools to simulate the impact of urban context on buildings energy demand.

List of Nomenclature

BEM and UBEM need to be reliable and adaptable in that they should provide accurate estimates of the buildings energy performance. However, discrepancies often occur between the predicted model and the actual metered building energy use, mainly due to inputs' inaccuracy/uncertainty in model parametrisation and structure [19]. Hence, the calibration of energy models is critical to achieve a confidence level in model predictions and encourage their adoption. They range from deterministic manual iterative tuning [19,20] to automatic calibration process using optimization techniques, machine learning algorithms or Bayesian calibration [21]. Once calibrated, UBEM can be useful to estimate impacts of new technologies and policies where no measured data is available and to reveal unknown occupant patterns.

Among the sources of energy consumption in buildings, solar irradiance and illuminance are considered renewable resources to increase buildings' energy efficiency through active and passive techniques and use of daylighting. The latter, combined with artificial lighting, has been investigated in numerous studies as part of a sustainable development strategy to insure the visual comfort, energy optimization and architecture aspect of the buildings [22,23,24].

Daylight and outdoor comfort are indicators for resident comfort and well-being [25]. The amount of solar energy and daylight reaching an urban environment is highly linked to the urban compactness, among its indicators the geometry of the urban canyon and the urban blocks, the streets and buildings' orientation. Evaluating the effects of these indicators on solar energy potential and daylight provide guidelines for urban form optimization in relation to retrofitting interventions on building envelopes and solar-energy applications in dense urban areas [26].

Of particular interest in this study is Beirut administrative district of Greater Beirut, Lebanon's capital. The city witnessed a horizontal and vertical expansion over the years (figure 1.1). This densification brought enormous challenges related mainly to the provision of urban services such as energy and to alleviating the impacts of climate change. The city held 50.7% of the urban population of the country and 44.6% of the total population in 2016 [2], and consumes 12% of the total national energy produced while it only covers 0.2% of the country's total area. Its tertiary sector (commercial sector, public offices, hotels and hospitals) and residential sector account for 73% and 26% of its electricity consumption respectively. These represent 39% and 14% of the electrification GHG emissions respectively [27]. While the energy demand is inflating, Lebanon has been suffering since many years, from a crisis in the energy sector. The country relies on oil products importation to meet its energy needs, putting the country in a state of high vulnerability. [1]. The available capacity for electricity generation is 2670 MW [28], of which only 1500 MW to a maximum of 2000 MW are insured by EDL (Electricity of Lebanon) [29], the only public institution responsible of the generation, transmission, and distribution of electrical energy in Lebanon [30]. In other words, a maximum of 65% of the power generated is issued by the State and the rest by back-up private generators. Even though Beirut is subjected to the least rationing period (only 3 hours while it is up to 12 hours in some parts of the country), reducing its energy demand can alleviate the daily blackouts in the other regions.

This situation challenges Lebanon's pledge to reduce its GHG by 30% by 2030 with 2015 being the base year. The country is then facing a significant challenge to manage its energy sector and integrate renewable energy. Therefore, developing an urban scale energy model is essential to Beirut's resources and energy supply management. The model serves as a decision support system by estimating energy consumption patterns and identifying grid peak demands with a spatiotemporal distribution. The latter, integrated with the potential solar production findings [31], helps in estimating the savings and recommending targeted energy-use policies to alleviate peaks and ensure an efficient resources distribution. Another feature of the energy model for Beirut is its capability to project the energy consumption under normal conditions. Currently, estimates of demands do not account for the suppressed amount of electricity, since during outages, occupants modify their behaviors and alter their energy consumption patterns and preferences.

Thesis Outline

The thesis starts with an overview of the scope of this study and its motivation. Chapter 2 introduces the traditional and recent urban building energy modeling approaches and identifies their limitations and strengths.

Chapter 3 discusses the data requirements, sources and quality. Methods for data management and preprocessing such as remote sensing and machine learning techniques are described. The archetype approach for urban massing models is applied and the algorithm that automatically creates the building multi-zone 3D model is introduced.

Chapter 4 presents the urban daylight potential and its relation to urban morphology.

The selected simulation engine DART and its characteristics are presented. DART is used to approximate results of radiative budget in a more efficient manner. Further, results are processed so they represent the daylight potential within the city. Urban metrics are computed and their impact on the daylight access is studied.

Chapter 5 represents the energy implementation of the model. The theoretical background of the energy simulation engine EnergyPlus is included. The calibration methodology is discussed and results of the simulation are reported at high temporal and spatial resolution. The policy implications of the study are presented and were published as an energy policy brief available in the appendix B.

Chapter 6 summarizes the results of this dissertation and discusses the possible future work.

State of the art 2.1 Overview of urban building energy models

The building sector is responsible of a great part of the global energy consumption. Hence, it has a great potential in reducing GHG emissions and improving energy efficiency, by the mean of retrofitting and the use of high-efficient energy technologies in the demand side [32].

Moreover, it can contribute in the energy supply management by the integration of renewable energy production in buildings such as solar PVs. Hence, building energy modeling at city scale plays an important role as decision-making tools to plan strategies for the both demand and supply management of the energy sector. The main purposes of building energy models can be summarized as follows [33,34]:

predict present and future energy consumption, disaggregated by the factor of interest (e.g., building type, income, etc.) by quantifying the energy use as function of different input parameters.

predict the technical and economic effects of different policy measures and energy consumption reduction strategies.

However, urban energy modeling is facing challenges among which the rarely accessible high level of detail (LoD) data, the systematic uncertainties, the heavy required computational resources, its sensitivity to urban microclimate and human behaviors [35]. The review section below starts with a brief introduction of urban building energy modeling approaches, then dive into the subcategories of each one to end with the chosen technique for this research, its benefits and challenges.

Modeling approaches: Top-down and Bottomup models

Based on the literature review, there is no unique classification of the urban energy models.

Even though, many approaches can be considered the same with different terminologies. For example, the forward approach proposed by 2009 ASHRAE Handbook relies on detailed physical description of the buildings and their systems as the engineering method discussed by Swan et al. [36] and the physical models suggested by Foucquier et al. [37] . In the following, a summary classification is discussed. Two main approaches have been used to model energy use patterns in urban context: top-down and bottom-up models [33].

Top-down models

Top-down models are mainly applied to explore the inter-relations between the energy sector and the socio-economic factors [33]. The main stream of top-down models is going from national or regional sector to individual buildings sector. They simply use the total energy consumption estimate and disaggregate it by attributing the energy consumption to buildings components without technical details. This workflow is easy since the required data (aggregated energy consumption data) is widely available. By using historical data, top-down models extrapolate from the status quo to predict near future energy patterns.

However, if paradigm shifts in the energy sector are encountered, top-down models fail, which make them unsuitable for interventions and technological studies. Moreover, the identification of possible improvements such retrofitting or demolition is inhibited due to lack of details. The top-down models can be divided into two categories: econometric and technological. Econometric models study the energy consumption as function of economic variables such as gross domestic product (GDP), income, energy price and may include climatic conditions. As they rely on the past energy-economy interactions to predict current and future consumption, they lack technical details and are not suitable for climatic change impact evaluation as the latter may dramatically affect the pillars of a sustainable development: society, environment and economy. On the other hand, the technological models include other factors that affect the energy use such saturation effects, technological progress, structural change and so on [33]. An example of top-down models was developed for the residential stock in Jordan [38]. A multivariate regression model with a time series analysis was adopted to predict future energy consumption and potential energy savings by correlating macroeconomic indicators such as income level, electricity and fuel unit prices, social indicators such as population and the weather conditions. Authors claimed that their approach might not be accurate but can inform about the future.

Bottom-up models

The first intuition of bottom-up models was to identify future energy efficiency measures capable to reclaim wasted energy resources. The impacts of these measures implementation, costs and doubts can be evaluated by developing different scenarios making bottom-up analysis a more realistic approach to specify energy reduction potentials and thereby GHG emissions [39]. Bottom-up models estimate individual end-uses, aggregate results according to their impacts on energy use, then extrapolate to regional or national level. This detailed approach allows for improvements and technological studies. By relating end-use energy consumption to macro-economic indicators, bottom-up models can gain some of top-down models strengths. Behavioral factors such as occupancy behavior, heating and cooling systems uses and energy gains may be incorporated in energy assumption increasing the accuracy of the model. However, due to the complexity of these occupant dependent variables, they are often assumed. This assumption level is a major drawback of some bottom-up models. In addition, large amount of data is required to efficiently describe each component contribution, which is limited in many countries. In addition, the sensitivity of input parameters is inappropriately described. Calculation and simulation techniques are in many cases time consuming, high costly and seek high level of expertise. According to Harish et al. [40], bottom-up models are based on two approaches:

Forward approach: it involves the input of detailed parameters of the buildings to predict the outputs. Models based on this approach are highly accurate as thermodynamics and heat transfer equations are applied. In addition, building energy simulation software tools are widely developed incorporating complex equations for better prediction.

Data driven approach: the inputs and the outputs of the model are known or have been measured. The data can be intrusive in case the experiments to gather information are performed under normal system operation.

When controlled experiments are limited by the building operation, nonintrusive data is collected. The energy consumption is estimated using regression analysis relating it to various parameters. Artificial algorithms such as Neural Networks and Support Vector Machines are applied when long-term energy estimation is requested to reduce the amount of performance data to be collected and the number of parameters to be identified when repeated operations such occupancy and set points schedules are encountered.

A more sophisticated and branched classification is proposed by Swan et al. [36], where the bottom-up models are classified into statistical and engineering models. Though, as the use of statistical learning algorithms is spreading widely, Zhao et al. [41] presented them in a separate group named Artificial Intelligence methods. Each of the aforementioned groups can be further sub-divided as studies and modeling techniques are in the process of development and growth.

Statistical models

Statistical models, also called inverse models [42], are primary used to identify building parameters by using existing data such as billing data and surveys information. They are particularly adopted to:

Detect energy consumption abnormalities or malfunctioned systems

Analyze impacts of retrofitting measures

Statistical models embed the strengths of top-down models as they use macroeconomic and socioeconomic variables such as energy price and income. They incorporate occupant behavior by attributing energy consumption to end-uses. In general, they are easy to develop and use. Regression and conditional demand analysis (CDA) are well-documented techniques and widely used [36]. As examples, linear regression models have been used to assess the electricity and fuel consumption of New York City [43] and correlate the energy performance for heating with the Surface area to Voume ratio S/V in the city of Carugate, Italy [44]. However, as they dont provide a detailed description of energy end-uses, they lack of flexibility and are limited when assessing new energy measures [33]. Large amount of data is required to correlate energy consumption with end-uses and to achieve an acceptable accuracy level. In addition, when based on annual metered data, they are unable to predict energy use in monthly or hourly time steps or to simulate the combined impact of several energy efficiency measures in buildings [14].

Engineering models

Engineering models (EM) complex equations are widely incorporated in building energy simulation programs to overcome their complexity [40,45], like EnergyPlus [46] and TRNSYS [47]. However, a detailed physical description of building properties and systems and precise weather characteristics are essential for these programs [48].

By applying engineering models, end-uses energy consumption are determined based on their physical functions and thermodynamics relations. Accordingly, a detailed description of their impacts on the aggregated energy consumption is provided. The model does not rely on previous data so it is more suitable to test new technologies and effectively estimate the low-cost energy efficiency opportunities and their appropriate combinations. Although EM are considered high accurate, occupant behavior and preferences are difficult to be included and are rather assumed. In consequence, the socio-economic factors are excluded [START_REF] Aydinalp | Comparison of neural network, conditional demand analysis, and engineering approaches for modeling end-use energy consumption in the residential sector[END_REF]. In addition, large amount of inputs and high level of expertise are required to develop the models and solve the equations. To reduce EM complexity, some modelers proposed some alternatives to simplify the analytical approach, either by applying steady-state methods such as degree-day method and its optimization techniques [START_REF] Saad | Computer-aided building energy analysis techniques[END_REF], or by simplifying the building characteristics inputs by applying easy equations or using average values from statistical data.

Dynamic simulation

Dynamic simulation consists of using energy performance software tools, like EnergyPlus [46] and TRNSYS [47], to overcome the complexity of EM. It is suitable for large buildings with complex systems simulation and are capable to involve control strategies [42]. Energy simulation programs are often based on two modeling techniques [45]: the analytical method and the numerical method. The first one solves linear differential equations with time independent parameters, while the second one uses a nodal network representation of the building and applies for each node a system of nonlinear and time dependent equations.

The nodal network is then simulated simultaneously (e+). As the numerical method handles more complexity in the nodes interactions, it is more preferred. In general, a detailed physical description of building properties and systems and precise weather characteristics are essential for the software. Additionally, simulation relies on simplified inputs and assumed values related to behavioral parameters reducing the accuracy of the model [48]. In addition, it is still expensive in terms of expertise, time and costs. Harish et al. [40] provided a recuperative overview of energy simulation programs, their applications and limitations.

Archetypes

The classification of "reference building"is a commonly used concept to represent certain categories within a stock identified based on specified criteria. Then, the energy consumption of each category is calculated using EM methods. Results are aggregated to estimate the regional or national housing stock energy consumption. Three approaches are proposed to define building typologies:

Real example building approach that selects, by experts, a real building as the most representative of specific parameters when statistical data is not available;

Real average building approach that finds a real building which characteristics are similar to the mean features of a statistical sample;

Synthetical average building or Archetype approach that defines a virtual building attributed the mean features statistically detected from a sample.

These approaches were applied in the TABULA project (Typology Approach for Building Stock Energy Assessment). The aim of this project is to create a harmonized structure for European Building Typologies by the identification of reference buildings, to assess the actual energy performance and to study the impact of retrofits applied to existent buildings in terms of energy savings and CO2 emissions reductions [START_REF] Ballarini | Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project[END_REF]. The reference building selection is performed based on a categorization process where a building sample is split into categories according to climatic area, age and geometry. Then buildings were selected as references with geometrical and thermal characteristics. In another study, this procedure is compared to a cluster analysis, a technique to partition a set of houses into clusters with similar profiles [START_REF] Ballarini | Improving energy modeling of large building stock through the development of archetype buildings[END_REF]. However, the generation of archetypes seems to be a more reliable and applied technique. According to Swan et al. [36], archetypes are classified as engineering models. Their generation is achieved through two steps [START_REF] Davila | Modeling Boston: A workflow for the generation of complete urban building energy demand models from existing urban geospatial datasets[END_REF]:

Segmentation: Key parameters that will be used to distinguish the archetypes can be identified from previous work, surveys, statistical algorithms or measured data. For example, Famuyibo et al. [START_REF] Albert Famuyibo | Developing archetypes for domestic dwellings An Irish case study[END_REF] ranked nine key independent variables (wall, roof, floor and window U-values, air change rate, heating system efficiency, dwelling type, floor area, DHW tank insulation) depending on their influence on energy use based on international literature and the available data sample. Then, to identify the most important variables, multi-linear regression analysis (MLRA) followed by clustering were performed. Attia et al. [START_REF] Attia | Development of benchmark models for the Egyptian residential buildings sector[END_REF] determine parameters average values through surveys and literature review. A walk-through survey was conducted to identify schedules and users patterns. The archetypal data-tree is another approach to identify the representativeness of a parameter [START_REF] Sousa Monteiro | The Use of Multi-detail Building Archetypes in Urban Energy Modelling[END_REF]. Each tier of detail represents the level of disaggregation of the selected parameter and each node represents the corresponding archetype. When comparing the results at each level, urban modelers can quantify the impact of the parameter and hence proceed to further subdivision or not. In general, it was found that building use (e.g., residence, office, etc.), age of construction, floor area and shape are the most used parameters for segmentation.

Characterization: Each archetype is identified by its non-geometrical properties such as construction materials, occupancy rate, DHW, cooling and heating set points.

Deterministic parameters can be assigned to archetypes gathered from buildings audits or existing database. Parekh [START_REF] Parekh | Development of Archetypes of Building Characteristics Libraries for Simplified Energy Use Evaluation of Houses[END_REF] generated 30 archetypes for residential and commercial stocks in Houston city based on surveys and previous data. Building prototypes were then simulated using eQuest and results were visualized in GIS and spatially distributed for a better analysis of energy consumption spatial and temporal variation. Still, with the finer parameters, the model is not able to address the variety in energy consumption among the buildings of the same archetype. Furthermore, the available information determines the degree of confidence of the parameters. Among the high-uncertainty parameters are the occupant dependent variables, infiltration rates, thermal losses from distribution systems [START_REF] Sokol | Validation of a Bayesianbased method for defining residential archetypes in urban building energy models[END_REF]. However, many approaches have been developed for a more realistic and robust representation of the occupant behavior.

Grey models or Hybrid models

Grey models are defined as models used when the information about a system are not very well known. For example, if the variables influencing a system are known but how exactly they affect the system is not clear, the system is considered a grey system [7,41]. According to Fumo [START_REF] Fumo | A review on the basics of building energy estimation[END_REF], they are hybrid models that first physically determine the building characteristics and systems contributing in the energy consumption, then involve a statistical analysis to identify the key parameters for a satisfactory energy prediction. Fonseca et al. [START_REF] Jimeno | Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts[END_REF] combined statistical data (archetypes databases) with analytical methods to develop a model that provides detailed qualitative and quantitative description of the energy supply. The model generates a spatiotemporal energy distribution for scenarios investigations and visualizes results via GIS platform. Potential wasted energy resources and building retrofits as well as urban zoning analysis and distributed schemes integration were all examined and studied. Assembling, managing, and automating the workflow is essential. For this purpose, the building stock is divided into archetypes to reduce complexity and computation requirements [14].

Urban building energy models

An illustrative case of UBEM is the Boston model developed by the MIT Sustainable Lab [START_REF] Davila | Modeling Boston: A workflow for the generation of complete urban building energy demand models from existing urban geospatial datasets[END_REF].The model was accomplished using a set of tools comprising GIS [START_REF]ArcGIS V10.3[END_REF] for buildings' footprints importation, Rhinoceros 3D [START_REF] Mcneel | Rhinoceros 3D V5[END_REF] as the CAD environment, and EnergyPlus as the thermal simulation engine. The workflow consists of generating the archetypes based on the year of construction and buildings' types, extruding the building's footprint to create the three-dimensional form, dividing it into floors, generating windows and assigning the specific thermal properties based on the building's archetype. Shading surfaces were determined and each building was then represented by a thermal model and its energy performance was simulated in EnergyPlus. A following study, where the same workflow was applied for a neighborhood of Boston, explored different ECM that can be applied to reduce the energy consumption [15]. Another example is the CityBES in the US, an open interactive web-based platform to automatically generate UBEMs based on city GIS dataset [16]. It provides results of energy end-uses on annual, monthly and hourly timescales with a 3D visualization of the city and its urban modules.

3DStock is another 3D model for the British building stock, which breaks buildings to floors with different activities, and floors to zones with different sub-activities. Geometrical data, electricity and gas consumption are attached to each Self-Contained Unit. 3DStock is capable of making projections of future consumption, or testing the impact of possible abatement measures and new technologies [17].

In the previous paragraphs, we discussed the recent trends of cities, the urbanization issues and challenges, and the climate change threats. Urgent interventions and feasible actions are required. In this context, energy management for urban policies rises with a particular importance, to reduce the energy consumption, improve its quality, increase its availability and reduce the GHG emissions resulting from its production. Urban models have been introduced to assist these objectives. An overview of traditional and recent modeling techniques was presented to explore the limitations and the strengths of each technique.

When managing the integration of renewable energy or application of retrofit measures at city level, scaling down to hourly energy consumption patterns is crucial. This high resolution temporal energy demand is determined by occupant activities, lifestyles and economic status. Hence, UBEM calibration concentrates on integrating the significant weight of occupant behaviors into the urban model.

To encompass these aspects, probabilistic approaches are applied. In this context, Cerezo et al. [START_REF] Cerezo | Three Methods for Characterizing Building Archetypes in Urban Energy Simulation A Case Study in Kuwait City[END_REF] proposed a Monte Carlo simulation with probabilistic distributions method to characterize uncertain parameters related to building occupancy. The method was then validated by comparing it to two others deterministic methods for a district in Kuwait City. The probabilistic method showed less error in terms of average Energy Use Intensity (EUI) and standard deviation. For the building sector, the calculations were based on the constructed area per building type, the climatic zone, the occupancy rates and the energy demands per end-uses per building type defined by a previous study A roadmap for developing energy indicators for buildings in Lebanon [START_REF] Schimschar | A Roadmap for developing Energy Indicators for Buildings in Lebanon[END_REF]. The latter study results were obtained after simplified calculations conducted for a business as usual case (BAU) under coastal climate and with assumptions of the boundary conditions based on expertise. However, both studies did not account for the different properties of buildings envelope. All the above studies can be complemented by an archetypal classification of the buildings and more detailed BEM for more accuracy and applicability.

Conclusion

This chapter provides an overview of the urban energy modeling techniques applied for energy assessment at urban scale.

Remote sensing for data preprocessing

Remote sensing techniques are widely used for many applications such as mapping crops practice [START_REF] Bégué | Remote sensing and cropping practices: A review[END_REF], snow monitoring [START_REF] Amin Shaban | Remote sensing application to estimate the volume of water in the form of snow on Mount Lebanon[END_REF], determination of clouds and precipitation properties [START_REF] Stephens | The Remote Sensing of Clouds and Precipitation from Space: A Review[END_REF],

and environmental policy support [START_REF] De Leeuw | The function of remote sensing in support of environmental policy[END_REF]. In urban environment, they can inform about the optical and thermal properties of the materials in the urban canopy [START_REF] Landier | Modélisation 3D du Bilan Radiatif des Milieux Urbains par Inversion d ' Images Satellites en Cartes de Réflectance et de Température des Matériaux Urbains[END_REF]. In this study, remote sensing was applied in urban context for data preparation and cleaning, as will be discussed in the next sections.

Buildings detection and segmentation

Identifying the geometrical properties of buildings in the city is a crucial step to create the 3D Bi-monthly electricity consumption from 2015 of a set of buildings was provided by EDL and added to the updated dataset. Buildings near the shore (up to 1Km inland) have in general their facades face west and/or north with large windows overlooking the sea. To take into consideration this feature, we labeled these buildings. Moreover, a comprehensive survey was used to label glazed buildings as "glazed". These buildings were represented by 3D thermal models as will be discussed in chapter 5.

The previous intervention led to a discontinuity in the urban layout. However, for the daylight and solar access analysis, all buildings in the zones of interest were retained.

Buildings with missing function were considered residential. When no year of construction was available, it was assumed that the buildings were built after 1991.

Construction of the DSM and buildings' heights identification

In 2018, the Lebanese Army identifies the buildings heights in Beirut by attributing each one an elevation point. The "Army heights" shapefile was joined to Beirut buildings shapefile. 17, 632 points were identified, with an increase of 60 buildings compared to Beirut buildings shapefile. The analysis of the data shows that this difference is due to the following:

2,920 polygons had no corresponding elevation points 13,270 polygons had one elevation point 1,380 polygons had more than one elevation point Three reasons were identified responsible of buildings with no elevation points:

1. Beirut buildings shapefile account for all buildings constructed until 2016 while the Army field work was achieved in 2018. Many buildings were built between these two years. Note that in some cases. the elevation point resided outside the polygon, so manual intervention was applied to link each point to its nearest building. Otherwise, the buildings were not attributed any height.

2. Some buildings were attributed more than one elevation point, for example on the roof and the rooftop. In this case, the lowest point was kept. However, sometimes the multiple points were considered and the building was split into many buildings with the same properties. This assumption is accepted especially for the radiative budget study where the height is critical.

3. Some points had unreliable values, for example negative values, and were therefore excluded.

The army heights were compared to the Digital Surface Model (DSM) obtained from the processing of Tristereo Pleiades images (figure 3.2 ).

Machine learning for oultier detection

The problem of outliers is that they have the effect of misclassification of data and affect the outputs attributes [START_REF] Aggelos | Data preprocessing in predictive data mining[END_REF]. An "outlier is an observation, which deviates so much from other observations as to arouse suspicions that it was generated by a different mechanism" [START_REF] Hawkins | Identification of Outliers[END_REF].

Outlier detection is applied in many fields such as intrusion and fraud detection, medical diagnosis, sensor networks, image processing and many other domains. Outlier detection techniques can operate under supervised mode (when training data for both normal and outliers classes is available), semi-supervised mode (when training data is only available for normal or abnormal class instances), and unsupervised mode [START_REF] Bettencourt | Outlier Detection: Applications And Techniques[END_REF][START_REF] Kriegel | Outlier Detection Techniques[END_REF]. Some prominent categories of outlier detection techniques in the unsupervised outlier detection setting are proximity-based methods [START_REF] Chen | Outlier Detection with Autoencoder Ensembles[END_REF], which treat outliers as points which are isolated from the remaining data [START_REF] Bhattacharya | Outlier detection using neighborhood rank difference[END_REF]. They rely on notions of distance (how far the instance is from the majority of other instances in the dataset) or density (how similar is the density around the instance to the density around its neighbors).

Density-Based Spatial Clustering of

Applications with Noise DBSCAN is a fairly used unsupervised clustering method. It has the ability to find non-linear clusters based on density. The DBSCAN algorithm works by going through all data points, and find neighbors that are closer to each other than a certain parameter and therefore considered to be in a same cluster.

Autoencoders are another method used for outliers detection. They are unsupervised multi-layer neural networks, with symmetric architecture. The number of nodes in the input layer is the same as in the output, and the number of nodes in the middle layers is small. The objective of an autoencoder is to train the output to reconstruct the input as close as possible, with low dimension representation. This reduced representation of the data is a natural approach for discovering outliers, which are harder to be represented in this form, and therefore their error score will be large [START_REF] Chen | Outlier Detection with Autoencoder Ensembles[END_REF]. The use of DBSCAN and autoencoders before the calibration process will be discussed further in section 5.4.2.

Generation of the 3D model 3.4.1 Archetypes generation

To overcome the complexity of dealing with a large dataset of distinct buildings, archetypes were generated. An archetype is a set of geometrical and non-geometrical properties that characterize a building's thermal performance. Non-geometrical properties include the thermal resistance of the construction materials, occupancy schedules and appliances densities [15]. As previously mentioned, archetypes generation consists of two steps [14]:

the segmentation of buildings with similar properties based on key parameters such as the type, age, shape [START_REF] Albert Famuyibo | Developing archetypes for domestic dwellings An Irish case study[END_REF][START_REF] Theodoridou | A typological classification of the Greek residential building stock[END_REF][START_REF] Ballarini | Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project[END_REF][START_REF] Sokol | Validation of a Bayesianbased method for defining residential archetypes in urban building energy models[END_REF], and the characterization of the thermal properties for each archetype. This information was gathered from buildings audits [START_REF] Attia | Development of benchmark models for the Egyptian residential buildings sector[END_REF], existing database [START_REF] Filogamo | On the classification of large residential buildings stocks by sample typologies for energy planning purposes[END_REF][START_REF] Parekh | Development of Archetypes of Building Characteristics Libraries for Simplified Energy Use Evaluation of Houses[END_REF][START_REF] Cerezo | Three Methods for Characterizing Building Archetypes in Urban Energy Simulation A Case Study in Kuwait City[END_REF] or local expertise and previous literature [START_REF] Albert Famuyibo | Developing archetypes for domestic dwellings An Irish case study[END_REF]. In this study, the buildings were grouped based on two parameters: the building's type and the year of construction based on a historical architectural analysis of the buildings. The building type helps in setting a building's occupancy patterns and determining its internal heat loads, while its year of construction informs about construction material and methods. For the specific case of Beirut, five distinguished construction periods were identified in the literature: 1900-1923, 1924-1940, 1941-1960, 1961-1990, and 1991 and on [START_REF] Joseph | Practicing modernism in Beirut architecture in Lebanon 1946-1970[END_REF]. Regarding the type, the buildings were grouped in five classes: residential, mixed, hospitals, schools and governmental buildings. In total, 5 × 5 = 25 archetypes were generated. Thermal properties were acquired from the Technical Guide for the application of the Thermal Standard for Buildings in Lebanon [START_REF] Undp | Groundwork for a Technical Guide for the application of the Thermal Standard for Buildings in Lebanon[END_REF], published in 2005. Missing information was obtained from ArchSim default library [START_REF] Dogan | Archsim -energy modeling tools for grasshopper[END_REF] and online libraries [START_REF] Plag | Ubakus -U-value calculator[END_REF]. A priori, non-geometrical properties, including light and equipment loads intensities and usage schedules were set by referring to ASHRAE standards [START_REF]Standard 90.1-2010-Energy Standard for Buildings Except Low-Rise Residential Buildings[END_REF]. HVAC systems performance coefficients were obtained from reference [START_REF] Schimschar | A Roadmap for developing Energy Indicators for Buildings in Lebanon[END_REF].

Finally, ArchSim library was updated with the missing properties as inputs for the simulations. Figure 3.5 summarizes the adopted workflow.

Buildings Data

Rhinoceros 3D

Footprints Area Number of floors

Year of construction Function

Architectural history

Archetypes generation

Geometrical properties

WWR Floor heights Construction methods

Statistical model

Hourly climatic data

Engineering model Calibration model

EDL metered data

Bimonthly electricity consumption

DEM

Topographic map

Non Geometrical properties

Envelope properties Setup points Occupancy, light, plug loads densities and schedules

3D model

Energy model

3D model

After preparing the data, buildings were divided into floors and each was assigned an archetype. The geometry modeling process is shown in figure 3.6. The buildings footprints and the topographic map contours were imported to Grasshopper, a free plugin of Rhinoceros 3D (which is not a free software tool but with a 3 months trial) [START_REF] Mcneel | Rhinoceros 3D V5[END_REF]. After generating the Digital Terrain Model (DTM) using Delaunay triangulation, buildings footprints are projected on the DTM surface. The polygons were then extruded to the buildings heights. Then, using the ArchSim tool, each building was divided into its number of floors, each represented by a single thermal zone. No further zoning was applied in this study. Next, windows were generated on free facades, which resulted from excluding adjacent walls and those whose neighboring wall is less than 3m apart. To have a more realistic representation of the buildings, the number of windows per facade was restricted by its width and the Window to Wall Ratio WWR given by orientation and year of construction. In this respect, windows were generated with a spacing of 5m for residential buildings and 4m for non-residential buildings. The WWR was primarily identified according to the year of construction. Values ranged between 0.15 in 1900-1923 and up to 0.7 after the 1990s. However, if the building overlooks the sea, the WWR was increased to 0.7 to take into account the north and west highly glazed facades. In addition, fully glazed buildings and first floors of mixed buildings, considered as retail shops, were assigned a 0.95 and 0.8 WWR respectively. Another feature of BEEM is the possibility to include balconies, which are added to free walls of residential floors having an area greater than 15m2 .

Potential shading surfaces from neighboring buildings were determined in the 3D model based on a two-step process. In general, a building will shade another one at a certain time of the day if the length of its shadow is greater than the distance between the two buildings at this time. However, the shadow length varies over the day in function of the sun angle.

The longest shadow is provided with the smallest angle, but this will lead to a great number of shadows on one building. To overcome this issue, each building (of height H) that is 3.78H away from the target building within a radius of 300m is considered as a building that casts a shadow. The multiplicative coefficient 3.78 was calculated as in [8], based on a sun angle of 14.8 • , corresponding to 80% of the time between 5:30 am to 8 pm in Beirut.

We chose this interval to include all the time when the sun is shining, in winter and summer. To accomplish this task, we used the equations provided by the NOAA solar calculator 2 . To further reduce the number of shading surfaces, a ray tracing algorithm was used to detect only the neighborhood buildings' surfaces that obscure the solar radiation.

Once the 3D model was completed, each thermal zone was assigned the simulation parameters from the buildings database.

Conclusion

This chapter shows how remote sensing is applied in urban environment for urban components detection, such as buildings. It also stresses its effectiveness for data preprocessing. However, combining it with machine learning algorithms would enhance its applicability and feasibility.

In this context, machine learning application is becoming an inevitable requirement in data management and in results processing as will be shown in next chapters.

A statistical approach to create archetypes for buildings characterization is introduced. The results of these processes are fed to the automated generator of 3D massive models that will be used for the daylight assessment and energy modeling.

Urban daylight model 4.1 Introduction

The radiation budget is an important component of the energy budget of buildings. It contributes directly by the solar gains by exterior surfaces, and indirectly by the energy savings achieved by replacing artificial lighting with daylight. This chapter focuses on daylight accessibility in the urban context by simulating first the luminous energy in two different zones, using DART. Then, the importance of a high LoD 3D model in daylight assessment is investigated, as well as the variation of the daylight along the buildings' elevations. A neural-network approach linking urban morphology to daylight accessibility is presented.

Presentation of DART model

To estimate the intercepted solar radiation, we simulate the radiative budget of the different urban geometries (walls, roofs, glazed surfaces, ground) using DART. DART, a free software tool, is a 3D model that computes the radiation propagation through the earth-atmosphere system, over the entire optical domain, ranging from visible to thermal infrared wavelengths [START_REF] Jean | Discrete anisotropic radiative transfer (DART 5) for modeling airborne and satellite spectroradiometer and LIDAR acquisitions of natural and urban landscapes[END_REF]. It works with any experimental and instrumental configuration (altitude and spatial/temporal/spectral sensor resolution, sun and view directions, atmosphere, ...). It provides two major types of products for natural and urban landscapes:

• Satellite/plane/in-situ spectrometric and LiDAR acquisitions: These are useful for sensitivity studies, for procedures that invert satellite images. • Radiative budget RB: It corresponds to the budget of the incident, intercepted, absorbed, scattered and thermally emitted radiation. It can be computed per scene element and per type of scene element such as "roofs", "walls", "trees", etc.

Vegetation fluorescent RB can be also simulated. The radiative budget is computed based on a ray tracing approach detailed in section 4.2.3. Different scattering orders are simulated:

1. Direct illumination (Illudir): radiative budget of direct sun illumination. At this step, scattering and absorption are null.

2. Direct and diffuse illumination (Illudif).

3. Multiple iteration steps (Iter1, Iter2,...). DART is composed of four executable modules to parametrize the simulation:

Direction: it calculates the directions of propagation of the radiation in the space 4π.

Phase : it computes the optical properties of the surfaces (i.e. opaque surfaces and earth scene), the atmosphere and the vegetation.

Maket : it simulates the 3D architecture of the scene.

Dart : it simulates the radiative budget.

Earth-Atmosphere scene

General introduction

The scene to model is contained in a rectangular parallelepiped divided into parallelepiped cells (voxels) (figure 4.3). The cells size, defined by the user and used to simulate the earth scene are in general smaller than those used to simulate the atmosphere. In DART, a scene element is either a volume or plane surface. The former is a 3D juxtaposition of cells filled with turbid material used to simulate fluids (air, water...) and vegetation. The latter is a juxtaposition of triangles or parallelograms used generally to simulate urban elements and topography [START_REF] Gastellu-Etchegorry | 3D modeling of satellite spectral images, radiation budget and energy budget of urban landscapes[END_REF]. In addition, DART scene can contain empty cells (i.e. without interaction with the radiation) or mixed cells (i.e. contains turbid matter and surface elements).

Boundary conditions

Even though the scene in DART is represented as a finite parallepiped, infinite landscapes can be also simulated as the juxtaposition of a given pattern.

Isolated scene: a ray that exists the scene is eliminated.

Repetitive scene: the scene and topography (DEM: Digital Elevation Model) are duplicated for simulating an infinite landscape. To get a spatially continuous scene and avoid that a ray is reintroduced under the ground, the DEM is regularized so that altitudes of the opposite faces of the scene are equal. A ray that exits the scene through a vertical scene side enters the scene through the symmetric vertical scene side, with the same direction and at the same altitude.

Infinite slope: the scene is repeated by ensuring continuous slopes. For that, the altitudes of the edges of the scene are shifted by a constant value at each edge. a ray that exists the scene through a vertical scene side enters the scene through the symmetric vertical scene side, with the same direction and the constant vertical shift.

3D objects

Objects in DART can be generated having simple geometrical forms. In this study, the CAD environment of DART is used to create plots or quadrilaterals containing turbid matter to simulate vegetation. It is also used to create schematic trees. Each tree specie is defined by many characteristics [START_REF] Landier | Modélisation 3D du Bilan Radiatif des Milieux Urbains par Inversion d ' Images Satellites en Cartes de Réflectance et de Température des Matériaux Urbains[END_REF]:

1. The height of the trunk under and in the crown.

2. The diameter of the trunk under the crown.

3. The shape of the crown (trapezoid, truncated cone, compound cone, ellipsoid, compound ellipsoid), and associated parameters (e.g. diameter, height).

4. The azimuth rotation around the vertical axis of the tree.

5. The presence or absence of branches.

6. The crown levels, and for each defined level, the horizontal and vertical distributions of H, the diameter of the trunk, the optical properties of the leaves, trunk and branches.

The position and the size factors of the trees can be defined by three ways:

Exact position and dimensions of the trees.

Exact position and semi-random size of the trees.

Semi-random positions and sized of the trees.

The trees trunks are represented by triangles while the crown can be represented by turbid cells or a cloud of triangles the density and distribution of which are defined by the user.

DART can also generate buildings of four walls and complex roof forms, roads and aquatic surfaces, and landforms derived from a terrain model imported as a raster image.

DART has also the possibility to import 3D objects generated by 3D CAD software tools such as Rhinoceros, Blender, etc. These objects are represented by triangles (or parallelograms), classified into groups based on their corresponding materials. Therefore, a building is composed of a minimum of three groups of triangles: "walls", "windows" and "roof". Each 3D object can be treated as a set of triangles or mixed or totally transformed into turbid cells. This can be used for the vegetation. The transformation from "triangles"

to "turbid cells" reduces significantly the computational time. During this transformation, the properties of the turbid matter is either calculated by DART given the triangles properties, or set by the user himself.

Atmosphere

The atmosphere has a major role in the radiative budget, via the absorption, emission and diffusion of the radiation. Its impact depends on its state (pressure, temperature), spatial distribution of its components (gases and aerosols) and the wavelength considered. DART simulates the effect of the atmosphere by meshing it into cells the size of which is inversely proportional to the density of the component particles of the atmosphere. The atmosphere is simulated as the superposition of three matrices of cells "air":

Bottom atmosphere (BA): cells "air" at Earth landscape level, i.e. below landscape top altitude.

Mid atmosphere (MA): from BA up to HA. It is made of cells that are usually larger than BA cells.

Higher atmosphere (HA): above MA. It is made of superimposed layers.

The atmospheric cells are characterized by the spectral properties of the gases and aerosols they contain. The profiles of these particles are stocked in the database of DART, but they can be modified or imported by the user.

Elements optical properties

The optical properties define how the surfaces and volumes of the scene elements intercept, absorb and scatter the radiation. They depend on the chemical composition and structure of these elements.

Properties of surfaces

The transfer functions of surfaces are characterized by a reflectance, direct transmittance and diffuse transmittance. These spectral properties depend on the spectral band considered. They are either read in DART's internal databases, entered by the user, or generated by DART built-in models. These properties allow the calculation of the absorptance and the directional emissivity of the surfaces.

A surface's reflectance can be isotropic, constant independently of the direction (i.e lambertian) or anisotropic (analytic model: Hapke, RPV,) with possible specular term.

Lambertian reflectance:

ρ(Ω S , Ω V ) = ρ lamb + ρ spec (Ω S , Ω V ) (4.1)
The specular reflectance depends on the refractive indices of the incident medium and of the medium radiation heads to, a multiplicative factor and the angular widths of the cones where specular radiation propagates.

Reflectance Hapke and specular:

ρ(Ω S , Ω V ) = ρ Hapke (Ω S , Ω V ) + ρ spec (Ω S , Ω V ) (4.2)
In the Hapke model, the ground is simulated as a plane medium containing particles, randomly distributed and large compared to the wavelengths. It is associated to a phase function that simulated backscattering and forward scattering.

Rahman-Pinty-Verstraete reflectance (RPV) This model easier to use than that of Hapke, reproduces well the standard bidirectional reflectance distribution function BRDFs of terrestrial surfaces, even asymmetrical, and the hotspot effect.

ρ(Ω S , Ω V ) = ρ 0 .M I (K).F HG (Θ).H(ρ c ) (4.3)
M I is the Minnaert function, F HG is based on Henyey-Greenstein functions and H is a function that simulates the hotspot.

Properties of turbid cells of vegetation

The optical properties of vegetation cells filled with turbid matter are characterized by phase functions defined by:

The spectral optical properties of the leaf: transmittance and reflectance, and

descriptive

General information on radiative transfer

Radiative transfer corresponds to the propagation of radiation in a medium. Using the exact kernel and discrete ordinate methods, radiation is restricted to propagate in a finite number of directions (Ω i ) with an angular sector width (∆Ω i ) (sr), in such way to always have

∆Ω i = 4π ([92]
). Any radiation that propagates along direction (Ω i ) at a position r is called source vector W (r, Ω i ). The radiative transfer equation gives the rate of variation of the radiance L(r, Ω) at position r along path dr along direction Ω of a stationary monochromatic wave. It can be written in the Cartesian coordinate system as:

[ζd/dx + ηd/dy + µd/dz].L(r, Ω n ) = -α e (r, Ω n ).L(r, Ω n ) + α a (r, Ω n ).L B (r, Ω n ) + N m=1 α d (r, Ω n ).P (r, Ω m → Ω n )/4π.L(r, Ω n ).∆Ω n (4.4)
where:

ζ, η and µ = cosine angles of the direction of propagation (Ω) wave along x, y and z.

α e (r, Ω n ), α a (r, Ω n ) and α d (r, Ω n ) =

Ray tracing approach

Ray tracing method solves numerically the above equation: N rays along N directions are traced from each source. During its propagation, a ray has its intensity that decreases by interception (absorption/scattering) and increases by thermal emission. Scattering generates, along Ndir directions, new rays W 1 (r, Ω n ), the further scattering of which gives new rays W k (r, Ω n ), and so on. With an iterative and convergent approach, the radiation intercepted by scene elements at iteration i is scattered during the following iteration i + 1.

A radiation is processed until it reaches a specified threshold or is totally absorbed and scattered. The iteration process stops when the difference in scene exitance between two consecutive iterations is less than a certain value. The iteration process is a powerful feature of DART, especially when simulating urban areas, where reflections from surroundings are a major player in the total intercepted radiation by the urban forms. The ray tracing approach has three simulation modes: reflectance (R), temperature (T) and combined (R + T). In the R mode, the sun is the primary source of radiation and the atmosphere the second source. Only the shortwave optical domain is considered. In the T mode, the sun radiation is neglected and the atmosphere brightness temperature is computed. The R + T mode combines both approaches. In our case, we are only interested in computing the visible shortwave radiation as will be discussed later. Therefore, we will use the mode R in our simulations. Cell size is 5cm. 3 remote sensing images are shown for 3 viewing directions (θ v , φ v ): nadir irradiance" (W/m2/µm) by triangles. The RB is simulated at 425nm, on June 21 at 17h.

Simplified examples of DART radiative budget

(θ v = 0 • ), oblique (30
The illumination grid is 1cm. At 17h, sun zenith angle is 81.1 • , which explains the presence of many shadows in the radiative budget for direct sun illumination only (Figure 4.6a).

Indeed, the DART simulation is conducted with the "repetitive scene" option. In that case, the shadows are due to the neighboring districts of the simulated Beirut district. This effect due to neighboring districts can be removed if needed, using either the "isolated scene" option, or by setting the irradiance on one or several faces of the 3D cell array of the simulated urban scene. This latter option was introduced in DART in order to simulate the districts on the sea side. It is not used here. The large sun zenith angle (81.1 • ) explains that the SKYL is very large, which in turn explains that maximal direct sun irradiance is much smaller than sky irradiance. We have: E directsun = 3.7W/m 2 /µm. Hence, the irradiance of the roofs is 3.7W/m 2 /µm. The irradiance of the walls on the west side of the buildings is much larger; it is around 23.7W/m 2 /µm. The rectangular shapes of the ground are due to the discretization scheme that is used to simulate the digital elevation model.

Impact of urban development on energy budget

In the last decades, Beirut has experienced a random urban growth and the rise of high buildings above 10 floors. This wave of urbanism led to the reduction of the distances between the buildings and to their adhesion. This chaos in construction has resulted in the decrease of the incoming daylight into the apartments. The purpose of applying DART in this section is to estimate the impact of urbanization on the daylight accessibility of buildings. For this purpose, the radiative budget of windows for a small number of mixed buildings in the last Another exercise with DART is to assess the influence of buildings features (windows, balconies...) on the radiative budget simulation. Quantifying the influence of integrating different aspects in the 3D model is important since most of urban daylight model rely on simplification of the buildings models, without assessing the consequences of such assumption.

55

The intercepted energy at 3 bands is simulated each time a new feature is added to the 3D model. The increasing complexity of the model is presented in figure 4.9. Figure 4.10 represents the mean incoming radiation on the buildings facades (including walls and windows when exist) for the different models. The results of the models 0 and 1 are almost the same as shown in the figure. However, when the balconies are added in model 2, a significant decrease of the intercepted energy is noticed. The inclusion of the DEM reduces even more the intercepted energy by the facades. It is important to point that the topography of the district we are studying is not too rough, reducing its impact in our case.

In most studies, when no windows are added, the results presented are those of the facades as in model 0. To be more accurate, the intercepted energy by the windows in models 1,

Urban morphology metrics

Compact city as a sustainable urban form is a famous trend among urban planners.

Burgess defined the compact city approach as "to increase built area and residential population densities; to intensify urban economic, social and cultural activities and to manipulate urban size, form and structure and settlement systems in pursuit of the environmental, social and global sustainability benefits derived from the concentration of urban functions" [START_REF] Burgess | The compact city debate: A Global Perspective[END_REF]. Hence, planners have recognized the economic and environmental benefits of dense urban environments, such the optimal use of infrastructure and land, easy access to services and social interactions. However, the increased urban density leads to a conflict between space-use efficiency on one hand, and daylight access and walkability on the other [START_REF] Ewing | Identifying and Measuring Urban Design Qualities Related to Walkability[END_REF][START_REF] Dogan | CitySeek: Towards Urban Daylight Models Based on GIS Data and Semi-Automated Image Processing[END_REF]. Many researchers argued about an experienced sense of compactness or compression influenced by the configuration of the urban zone. The qualification of an urban space, especially at the street level, based on this experienced compactness sense can be achieved through a range of urban design qualities [START_REF] Ewing | Identifying and Measuring Urban Design Qualities Related to Walkability[END_REF] such as the imageability, legibility, human scale, and many others. To quantify these perceptual qualities, indicators are implemented such as the sky view factor SVF, the aspect ratio, maximum view length, solid angle of view to list a few [START_REF] Jiao | Evaluation of Four Sky View Factor Algorithms using Digital Surface and Elevation Model Data[END_REF][START_REF] Middel | Sky View Factor footprints for urban climate modeling[END_REF][START_REF] Kokalj | Application of sky-view factor for the visualisation of historic landscape features in lidar-derived relief models[END_REF][START_REF] Bernard | Sky View Factor Calculation in Urban Context: Computational Performance and Accuracy Analysis of Two Open and Free GIS Tools[END_REF][START_REF] Belgacem | Mesure et représentation cartographique de l'impression de confinement d'un piéton immergé dans la ville[END_REF]. On the other hand, daylight is linked to buildings energy use and citizens' health. Many studies have been carried out to measure the solar access and the daylight availability in urban context [START_REF] Morganti | Effects of urban compactness on the building energy performance in Mediterranean climate[END_REF][START_REF] Dogan | CitySeek: Towards Urban Daylight Models Based on GIS Data and Semi-Automated Image Processing[END_REF][START_REF] De Luca | Facade-Floor-Cluster . Methodology for Determining Optimal Building Clusters for Solar Access and Floor Plan Layout in Urban Environments[END_REF] and assess its impact on the urban energy use [START_REF] Strømann-Andersen | The urban canyon and building energy use : Urban density versus daylight and passive solar gains[END_REF]. Urban canyon geometries impact the solar access and were the subject of many studies to assess these relations [START_REF] Najem | Streets and buildings' orientation entropies and the city's rooftops' solar potentials[END_REF]26]. It was found that the location, the sky components, the width and orientation of the streets, the architecture and roof shapes of the buildings, the urban density and the optical properties of the materials of the ground and the surrounding facades are key parameters that affect the solar access [START_REF] Najem | Streets and buildings' orientation entropies and the city's rooftops' solar potentials[END_REF]26,22,[START_REF] Sanaieian | Review of the impact of urban block form on thermal performance, solar access and ventilation[END_REF][START_REF] Lobaccaro | Boosting solar accessibility and potential of urban districts in the Nordic climate : A case study in Trondheim[END_REF].

In this study, focus is given to buildings' heights, orientations and the scene complexity. We also calculated the entropy of buildings and streets' orientations and the roads network's circuitry to compare the two zones.

Entropy of buildings and streets' orientations

The orientation entropies quantify the dispersal in the buildings and streets angles [START_REF] Najem | Streets and buildings' orientation entropies and the city's rooftops' solar potentials[END_REF].

ρ streetorientation = - N i p i logp i (4.5) ρ buildingorientation = - N i p i logp i (4.6)
where p i is the probability that a street or a building is oriented along a direction i with respect to the North with i going from 0 to pi in steps of pi/15 and i from 1 to N with N = 25 is the number of bins.

Circuitry

The circuitry is defined as the ratio of the sum of all network's pairwise distances D to the Euclidean or straight-line distances D e [START_REF] Najem | Streets and buildings' orientation entropies and the city's rooftops' solar potentials[END_REF][START_REF] Levinson | Network structure and city size[END_REF]. It is also possible to use the great-circle distance instead of the Euclidean distance as suggested by [START_REF] Boeing | The Morphology and Circuity of Walkable and Drivable Street Networks[END_REF]. The circuitry measures the tortuousity or the deviation from straightness of a road joining two points [START_REF] Najem | Streets and buildings' orientation entropies and the city's rooftops' solar potentials[END_REF]. The average circuitry of the nework in each study area is given as below:

C = D D e (4.7)

Vertical daylight profile

The vertical daylight profile corresponds to the profile of the intercepted energy by an urban component (in our case the windows) along the buildings' height. The analysis of this metric helps assessing the impact of urbanization on the daylight access on buildings' floors.

Radiative Budget computation -DART Daylight Radiative Budget

For every band in the simulation, DART generates a RadiativeBudgetFigures file, that gives the radiative budget per triangle. We retrieved the information in the form of a list, where each element has the following format:

[surf ace type , [x 1 , y 1 , z 1 , x 2 , y 2 , z 2 , x 3 , y 3 , z 3 ], [energy budgetvalues , area]] (4.8)
Surface type: we have many types of triangles: Ground, Walls, Windows, Roofs, Balconies and Slabs. A number that is stored in the dart.typ file represents each type.

The coordinates are those of the triangle vertices.

Energy budget values: three types of energy: intercepted energy, absorbed energy and scattered energy.

Area of each single triangle.

φ V = ∞ 0 ȳ(λ)φ e,λ (λ)dλ (4.9)
Where φ V is the luminous flux, in watts.

φ e,λ is the spectral radiant flux, in watts per nanometer.

ȳ(λ), also known as V (λ), is the luminosity function, dimensionless.

λ is the wavelength, in nanometers.

Formally, the integral is the inner product of the luminosity function with the power spectral density. In practice, the integral is replaced by a sum over discrete wavelengths for which tabulated values of the luminosity function are available. However, in our case, we divided the interval between 400nm and 700nm into 6 adjacent spectral bands as shown in

Description of the case study

Two zones (shown in figure 4.12) were arbitrary chosen for this study, having different urban complexity. The radiative budget on the external surfaces of 433 buildings in zone 1 and 414 buildings in B was simulated. Due to limited computational resources, the zones were subdivided into zones (6 subzones in zone1 and 3 subzones in zone 2) . The zones' boundaries were overlapping to account for the effect of shadows from neighboring buildings. Buildings with missing function were considered residential. When no year of construction is available, it was assumed that the buildings were built after 1991.

3D model generation

3D models of the buildings in the areas of interest were generated. Buildings were represented as flat roof models, divided into floors with windows and balconies. The window to wall ratio WWR and the optical properties of the windows, walls and roofs were identified based on the buildings'periods of construction. The latter gives insights on the construction materials and methods. A historical study of Beirut's buildings architectural aspects was achieved for this purpose. Five distinguished construction periods were identified. Spaces between buildings were assumed to be all roads, so the ground was attributed asphalt properties.

Materials' optical properties

Different types of materials characterize the buildings' components in Beirut. In the same period of construction, different materials can be encountered. For example, the roofs in 1900-1923 may be constructed of roof tiles or earth roofing recently covered by asphalt, or walls are either painted with white or beige color. Since no data regarding the materials' distribution, buildings were randomly selected and assigned the materials corresponding to their period of construction. Once the materials of the buildings components were identified, their optical properties were set. DART has an embedded library 'Lambertian database' with a large number of available materials and it was used in our case. However, the material's color is an important factor that should be considered. Therefore, the properties should be updated. A simple proportionality rule was applied based on the color's RGB for each of the six spectral bands of our simulations. Note that two dominating colors (beige and white) characterize the buildings' walls in Beirut. A python script was written to automatically assign properties to the large number of groups of the obj files.

Results and discussion

Zones' Urban morphology

The urban morphology of the zones is described by a set of urban metrics defined in section 4.4.1. Values of these metrics are reported in table 4.2. The two zones display similar morphology in terms of buildings and streets orientations entropies and road network circuitry. The buildings' heights distribution in Figures 4.13 and 4.14 shows that 50.5% of buildings in zone 1 have their heights between 20m and 30m while 40% of the buildings in zone 2 have their heights between 10m and 20m. The rose diagrams in figure 4. 15 show that the buildings in both districts are mainly oriented north-south or east-west.

Daylight availability and variability spatially and temporally

The mapping of the solar radiation in the subzones of zone 1 is presented in figures 4.16 at 9 am on March 21, for the spectral band 400nm -450nm. The 3D surface radiative budget results reveal the impact of the complex urban morphology. Walls without balconies receive higher energy compared to walls with balconies. Lower floors are less exposed to solar radiation than higher floors. These effects are more pronounced in figure 4.17. Non-linear multivariate regression was performed to predict the daylight availability by correlating it to urban related variables. Neural Network NN was employed to achieve this task. It was trained an tested using Python. The first step to create a NN is to identify its input variables. In this study, quantitative variables were only used, and presented as follows:

HS : mean height of surrounding buildings ŌS : mean orientation of surrounding buildings σ H S : standard deviation of surrounding buildings' heights σ O S : standard deviation of surrounding buildings' orientations H: building height X: building x coordinate Y : building y coordinate Surrounding buildings were identified as were the shading surfaces in section 3.4.2. Briefly, a surrounding building (of height h) is a building that is 3.78h away from the target building within a radius of 300m.

Due to different ranges of values of the NN features, scaling of the features was essential to bring values to the same ranges. The values of the features were standardized by removing the mean and scaling to unit variance using StandardScaler() from sklearn library, that calculates the standard score of a sample x by applying the following equation:

z = x -µ s (4.11)
where µ is the mean of the training samples, and s is the standard deviation of the training samples. In this study, the multilayer perceptron (MLP) regressor neural network was adopted. The number of hidden layers was identified through an optimization process to reduce the mean absolute percentage error (MAPE).

Input layer

Hidden layer Output layer

a (1) a (2) a (3)
Θ (1) =                  0.
                 Θ (2) =            30
          
a (1) =                  HS ŌS σ H S σ O S H X Y                

Relation between daylight and electricity consumption

Daylight contributes in the energy loads of a building by its capability to reduce electricity consumption by acting as a complement to artificial lighting [23]. In this section, we try to find the relation between daylight accessibility and electricity consumption in the two zones of interest. For this purpose, we simulated the electricity consumption of buildings in both zone as described in chapter 5. Buildings with metered data were included in the calibration process of the energy model results and in the rest of the analysis, while non-metered buildings were excluded. This reduced the number of buildings from 433 to 237 in zone 1 and from 413 to 242 in zone 2. Each value of the electricity consumption of the remaining building was multiplied by its cluster average multiplicative factor to adjust it (more details in section First, the calibration process was applied at the bimonthly level, so caution should be taken when using the calibration coefficients at the hourly level. Second, as the day progresses and therefore the daylight increases, the human activities and their cooling and heating needs increase. Anyhow, further analysis is required to understand the results and conclude the relation between daylight and electricity consumption.

Conclusion

In this chapter, the radiative budget module in DART is presented, revealing its accuracy and advanced algorithms to simulate solar radiation in urban areas. Simulations of the radiative budget then its conversion to present daylight is introduced. The results of the 3D radiative budget shows the impact of the urban context and the buildings features on the assessed daylight. The vertical elevation profiles of the windows intercepted energy illustrated the expected reduction in daylight accessibility at lower floors. Moreover, neural networks are developed to link daylight to urban morphology such as buildings heights, orientations and location. The algorithm have an MAPE ranging between 17% and 40% depending on the zone and the day of study.

BEirut Energy Model BEEM

Introduction

This chapter introduces BEirut Energy Model BEEM, an UBEM developed for the city of Beirut. It is an automated model applied in two districts within the city due to limited input data. The chapter starts with the equations of the heat transfer and thermodynamics embedded in EnergyPlus and applied to compute the energy budget of the buildings. It is then followed by the methodology overview, which includes data preparation, parameters setup, model execution and calibration. Results of loads profiles and the electricity consumption spatiotemporal distribution are shown to demonstrate the capabilities of the model.

Mathematical formulation

EnergyPlus, a free software tool, is a collection of modules that work together to calculate the heating and cooling loads of a building under different environmental and operational conditions. Through an integrated solution manager, EnergyPlus assures a physically realistic simulation by linking and solving the building, system, and plant simultaneously and by substitution iteration based on Gauss-Seidell numerical method [START_REF]EnergyPlus Engineering Reference: The Reference to EnergyPlus Calculations[END_REF]. The program is presented as a series of integrated elements connected by fluid loops. In our project, we tend to calculate the cooling and heating loads of our buildings, plus the electricity consumption from equipment and appliances. For this reason, the zone-system connection is only of interest and will be presented in the following section.

Zone and Air system integration

The heat balance equation on the zone air is the following:

C z dT z dt = N si i=1 Qi + N surf aces i=1 h i A i (T si -T z )+ Nzones i=1 ṁi C p (T zi -T z )+ ṁinf C p (T ∞ -T z )+ ṁsys C p (T sup -T z ) (5.1)
where:

C z dTz dt = energy stored in the air

N si
i=1 Qi = sum of the convective internal loads Energy Plus applies a Predictor-Corrector scheme to adjust the zone temperature at each time step. The scheme is presented below:

N surf aces i=1 h i A i (T si -T z ) =
The load of the zone is used as a starting point since it drives the entire process. By neglecting the capacitance of the air, and by assuming that the air system has sufficient capacity to meet the zone loads requirements (i.e. Qsys = Qload ), the equation becomes:

Qload = N sl i=1 Qi + N surf aces i=1 h i A i (T si -T z )+ Nzones i=1 ṁi C p (T zi -T z )+ ṁinf C p (T ∞ -T z ) (5.2)
In this case, T z is the desired temperature of the zone defined by the control system setpoints.

The air system is simulated to determine its actual capability. In EnergyPlus, the air system is a variable air system.

By applying a third order backward difference algorithm to equation 3.1 , EnergyPlus then updates the actual zone temperature based on the air system calculated capabilities:

T t z =( N sl i=1 Qi + N surf aces i=1 h i A i T si + Nzones i=1 ṁi C p T zi + ṁinf C p T ∞ + ṁsys C p T supply -( C z t )(-3T t-t z + 3 2 T t-2t z - 1 3 T t-3t z )) /( 11 6
C z t + N surf aces i=1 h i A + Nzones i=1 ṁi C p + ṁinf C p + ṁsys C) (5.3)
By this method, the zone temperature depends on the three previous time steps. The next paragraphs present all modules and equations used by EnergyPlus to identify the different parameters of the previous equation, such the surfaces temperatures Tsi and convective coefficients hi, the infiltration and ventilation flow rates ṁinf and ṁvent in order to balance the equation with the zone air temperature equal to the setpoint temperature.

However, it is important to present a brief overview of the air system model used in our study that allows to calculate the supply conditions (air mass flow rate, temperature and humidity).

Ideal Loads Air System

An ideal HVAC system model is applied to simulate the energy loads of the zones. It corresponds to an ideal VAV terminal unit with variable supply temperature and humidity, and it is not connected to a centralized source. The unit supplies cooling or heating air in sufficient quantity to meet the zone load and other constraints such the humidity if specified. The calculation procedure is presented below:

Calculate the minimum outdoor air mass flow rate based on the specifications in the DesignSpecification: OutdoorAir Object, if specified.

Calculate the sensible and latent impact of the outdoor air flow relative to the zone conditions.

Determine if the unit needs to heat or cool by comparing the outdoor air sensible impact and the zone load.

Calculate the supply air mass flow rate based on the supply temperature limit and humidification setpoint.

Set the entering air conditions equal to the outside air conditions in case there is no economizer either heat recovery.

Outdoor/Exterior Convection

The heat transfer from surface convection is calculated by applying the following equation:

Q c = h c,ext A(T surf -T air ) (5.5)
where:

Q c =

Adaptive Convection Algorithm

The algorithms are arranged by complexity and they differ by the depending parameters they use to estimate the convection coefficient. In our model, we used the DOE-2 algorithm, which is a combination of MoWiTT and BLAST Detailed models. For very smooth surfaces, the following equation is applied:

h c,glass = h 2 n + [aV b 2 ] 2 (5.6)
where:

h c,glass = convective heat transfer coefficient for very smooth surfaces h n = natural convective heat transfer coefficient a and b = constants T surf and T air = surface temperature and outdoor air temperature temperatures. For less smooth surfaces, the coefficient is calculated as follow:

h c = h n + R f (h c,glass -h n ) (5.7)
where R f is the roughness coefficient. All the constants can be found in tables in the Engineering Reference, in addition to the details of the remaining algorithms. Note that when the outside environment indicates that it is raining, the exterior surfaces (exposed to wind) are assumed to be wet. The convection coefficient is set to a very high number

(1,000) and the outside temperature used for the surface will be the wet-bulb temperature.

(If you choose to report this variable, you will see 1,000 as its value.)

Longwave radiation heat flux

It is a flux exchange between the surface, the sky, and the ground. The following assumptions are adopted: each surface emits or reflects diffusely and is gray and opaque (α = ε, τ = 0 and

ρ = 1 -ε)
each surface is at a uniform temperature energy flux leaving a surface is evenly distributed across the surface, the medium within the enclosure is non-participating

The enclosure consists of building exterior surface, surrounding ground surface, and sky.

The longwave radiation heat flux is given by the following equation:

q LW R = q sky + q ground + q air (5.8)

By applying the Stephan-Boltzmann law, the equation becomes: The ground surface temperature is assumed to be the same as the air temperature. The sky temperature is calculated as follows:

q LW R =
T sky = ( Horizontal Infrared radiation Intensity σ ) 0.25 -273.5

(5.10)

The horizontal infrared radiation intensity is available in the weather file. Notes: EnergyPlus takes into consideration the variation of the temperature and the wind speed with respect to the height above ground (in the case of high buildings). For this purpose, it calculates for each floor the local air temperature and wind speed by identifying its centroid.

Shortwave radiation heat flux through surfaces

The shortwave radiation includes both direct and diffuse incident solar radiation absorbed by a surface.

Sky Radiance Model

EnergyPlus calculates the diffuse solar radiation incident on an exterior surface, taking into account, the anisotropic radiance distribution of the sky, the surface orientation and the effects of shading of sky diffuse radiation by shadowing surfaces such as overhangs. It does not account for reflection of sky diffuse radiation from shadowing surfaces or from the ground [START_REF]Solar Radiation Distribution[END_REF]. The sky diffuse irradiance on a surface is given by:

I sky = AnisoSkyMulti × DiffuseSkyRadiation on the ground (5.11)
The sky radiance distribution is determined by three distributions:

1. An isotropic distribution that covers the entire sky dome 2. A circumsolar brightening centered at the position of the sun;

A horizon brightening

The proportion of each component depends on the sky condition, characterized by the clearness factor and the brightness factor.

I sky = R horizon I horizon + R dome I dome + R circumsolar I circumsolar (5.12)

Where:

I horizon = I h F 2 sinS = irradiance on surface from the sky horizon R horizon = R dome = R circumsolar = 1 in case there is no shadowing; otherwise they are the ratio of the irradiance with obstructions over the irradiance without obstructions (the horizon is divided into intervals and the sum of the irradiance is calculated on each interval as function of the angle and incidence angle).

I dome = I h (1 -F 2 )(1 + cosS)/2 =
F 1 = f (ε, ∆, Z) = circumsolar brightening coefficient
I 0 = 1353 W/m2 =

Reflected Sky Long-Wave Radiation

The sky long-wave radiance distribution is assumed isotropic and in case of obstructions, it is multiplied by a shading factor, R dome . The long-wave radiation from these obstructions is added to the long-wave radiation from the ground; in this calculation, both obstructions and ground are assumed to be at the outside air temperature and to have an emissivity of 0.9.

Shading module

The aim of the shading module is to determine the sunlit area of a surface. The user can set the shadowing calculations frequency. The default frequency value is 20 days, which means that the solar position is averaged over twenty days to reduce runtime. Then, after determining the sun position, surfaces that sun is behind are identified as shadowing surfaces.

The surfaces are then projected along the suns rays direction to the receiving surfaces. The overlap between the shadows and the receiving surfaces are determined so the sunlit area can be deduced.

Solar Gains

The combination of direct and diffuse solar radiation gives the total solar gain of an exterior surface:

Q sol =

Reflections

Ground Reflectance

The diffuse radiation accounts for ground reflection even if the reflection option (WithReflection) is not used. However, if this option is turned off, shadowing of the ground by the building surfaces and neighboring buildings are ignored.

GroundRef lectedSolar = (BeamSolarcosθ + I d ) × ρ g round where I d = diffuse solar radiation (available in the weather file)

Solar radiation reflected from exterior surfaces

In case the reflections from exterior surfaces are taken into consideration, the WithReflection option is used. Surfaces are categorized as follow:

Shadowing surfaces: such as surrounding buildings or overhangs They are attributed diffuse and/or specular reflectance values.

Exterior building surfaces: when a section of the building reflects solar radiation onto another section. The program considers opaque surfaces (such walls) as diffusely reflecting and calculates the reflectance values from the solar absorptance and visible absorptance values. In contrast, glazed surfaces are assumed to be specularly reflecting. Their reflectance values are calculated from the reflectance properties of the individual glass layers that make up surfaces construction assuming no shading device is present and taking into account interreflections among the layers.

The ground surface (See Ground Reflectance Section): Beam solar and sky solar reflection from the ground is calculated even if withReflections is not used (the default). In this case, the ground plane is considered unobstructed, i.e., the shadowing of the ground by the building itself or by obstructions such as neighboring buildings is ignored. This shadowing is taken into account only if WithReflections is used in the Solar Distribution field (in Building input object). In this case, the user-input value of ground view factor is not used.

Note that the exterior solar reflection is done once and no inter-reflection is counted [START_REF]Solar Radiation Distribution[END_REF]. To calculate the beam solar and sky solar radiation reflected on the buildings exterior surfaces, a backward ray tracing method is applied.

Ray tracing method

An n-sided surface is assigned n receiving points with well-determined coordinates. The radiation received by these points presents that of the whole surface. The ray tracing method consists of sending out 90 rays into the exterior hemisphere surrounding each receiving point.

The beam solar and the sky solar radiance of the surfaces hit by the rays is calculated by multiplying the total radiation by a reflection factor. The results at each receiving points are averaged and the reflected radiation onto the surface is obtained. The radiance of each hit surface is computed depending on its category.

Sky solar radiation diffusely reflected from obstructions

Sky solar radiation diffusely reflected from ground Beam solar radiation diffusely reflected from obstructions Beam solar radiation diffusely reflected from ground Beam solar radiation specularly reflected from windows

Interior Longwave Radiation

It includes absorption and emittance of radiation sources, such as other zone surfaces, equipment, and people.

Internal Shortwave Radiation

Solar distribution of beam solar radiation through exterior windows

There are five modules:

Minimal shadowing FullExterior FullInteriorAndExterior FullExteriorWithReflections FullInteriorAndExteriorWithReflections

In our case, we will use the FullExterior or the FullExteriorWithReflections modules. In addition to beam solar radiation entering the zone, shadowing by exterior surfaces like overhangs and exterior surfaces, and by windows and doors reveals are computed.

Assuming that the entering solar radiation falls into the floor, it is then absorbed according to the floors solar absorptance. In case there is no floor, the absorption is on all interior surfaces level. The reflected radiation is added to the transmitted diffuse radiation, which is assumed to be uniformly distributed and the heat balance is applied to each surface. In addition to the aforementioned computed terms, reflections from exterior surfaces are considered in the case of FullExteriorWithReflections. However, the simulation is much slower (See Solar Radiation Reflected from Exterior Surfaces section).

Interior Solar Distribution Calculation

The interior solar radiation consists of the beam solar radiation, diffuse solar radiation, and short-wave radiation from electric lights. This radiation is apportioned as follows:

absorbed on the inside face of opaque surfaces absorbed in the glass and shading device layers of the zones exterior and interior windows transmitted through the zones interior windows to adjacent zones transmitted back out of the exterior windows.

Interior convection

Many models are available within EnergyPlus to compute the inside convection coefficients and there is no way to declare one is better than another. In our case, we applied the TARP algorithm, which applies different equations for different situations depending on the surface orientation and the difference of surface and zone air temperature.

For no temperature difference or vertical surface:

h = 1.31|∆T | 1/3
For ∆T < 0 and upward facing surface or ∆T > 0 and downward facing surface:

h = (9.482|∆T | 1/3 )/(7.283 -|cosΣ|)
For ∆T > 0 and upward facing surface or ∆T < 0 and downward facing surface:

h = (1.81|∆T | 1/3 )/(1.382 + |cosΣ|)
where Σ is the surface tilt angle.

Interior conduction

It represents the heat transfer to the inside face of the building element and it is calculated using the CTF formulation presented in section (Conduction through the walls).

Infiltration and ventilation Infiltration Design flow rate

Infiltration is difficult to accurately model. It is the flow rate entering a zone mainly through opening and closing of exterior doors and cracks around windows. The basic equation used in EnergyPlus to calculate the infiltration rate:

Q Inf iltration = I design F schedule [A + B|T zone -T odb | + C × windspeed + D × windspeed 2 ] (5.16)
where:

I design =
Q w = C w A opening F schedule W indspeed (5.17)
Where: The max relative humidity in ArchSim is not used by EnergyPlus.

Q w =

Mechanical ventilation (Design Specification Outdoor air)

The mechanical ventilation is used to quantify the controlled air to enter a zone. In ArchSim, the outdoor airflow per person and the outdoor air flow per zone floor area are summed up after multiplying them by the occupancy density and floor area respectively. The result is then multiplied by a fraction schedule. If an economizer is used, the outdoor airflow will be augmented or reduced depending on limits that the user specifies. Heat recovery option is also available but not applied in our case.

Methodology

Data preparation

As discussed in chapter 3, the number of modeled buildings is 3,630 distributed over two districts in Beirut (figure 3.1). A topographic map was used to create the digital elevation model. Hourly weather data is obtained from the Beirut International Airport weather station, which is located along the Mediterranean coast in the southern suburbs of the city of Beirut. However, when the model had to be calibrated with actual data, many of the buildings were excluded for the following reasons:

Null or missing electricity data as obtained from EDL.

Unreasonable low or high electricity consumption by floor area.

Unreliable data: the total yearly consumption is not convenient with the sum of the bimonthly consumptions. In this case, manual intervention was applied where the bimonthly electricity consumptions were adjusted by dividing the values by 1000, when it was clear that it is a problem of units.

Parameters Set-up and boundary conditions

The following parameters have been used for the calculations.

We took into consideration the fact that many occupants use electrical heaters during winter, which have different coefficient of performance than air conditioners, and we assumed that they represent 70% of the occupants. According to [START_REF] Schimschar | A Roadmap for developing Energy Indicators for Buildings in Lebanon[END_REF], reversibe split units are used in residential buildings and retail (therefore in mixed buildings). Heating and domestic hot Coefficient of Performance, defined as the ratio between the useful heating provided and the electric energy usage.

2 Seasonal Energy Efficiency Ratio, defined as the ratio between the output cooling energy and the input electical energy.

3 Domestic Hot Water * Values are obtained from [START_REF] Schimschar | A Roadmap for developing Energy Indicators for Buildings in Lebanon[END_REF].

Table 5.2: Loads and conditioning parameters used for the electricity consumption calculations.

water are assumed to be provided by diesel biolers in hospitals. Therefore, they were not considered in the electicity consumption budget. Central chiller is the main cooling system in hospitals, and we assumed it is also available in recent governmental buildings and schools.

Shading surfaces such as balconies and surrounding buildings, and the ground were set as boundary conditions.

3D geometric model was generated for each building as described in section 3.4.2. It was then fed to EnergyPlus via ArchSim tool, which stored the building's properties in an idf file, ready to run. The files were distributed on four virtual machines created on Microsoft cloud service Azure and run by EnergyPlus for the thermal model execution. For the calibration process, the results were then processed and compared to the EDL data, when available, as discussed next.

Model Calibration

As previously mentioned, bimonthly EDL data was available for a certain number of buildings. Outliers were removed using density-based spatial clustering of applications with noise (DBSCAN) and autoencoders algorithms, based on the buildings' area, number of floors, type, year of construction and EDL consumption. Once outliers were identified, the remaining buildings were clustered based on their simulated electricity consumption and actual electricity consumption (EDL). This clustering helps identify buildings with similar occupancy schedules.

The offset between EDL values and those predicted by the model can be mainly attributed to behavioral patterns, HVAC systems and systematic errors. Hence, any correction to be applied must be consistent with the above categorical errors, adaptable to the city's context and justified at the same time. For this purpose, the following interventions were implemented:

Systematic errors: 3 hours long electricity cutoff in Beirut accounts for a reduction factor of 1/8, when averaged across the year. Hence, the simulated electricity consumption was reduced by that amount. In addition, errors related to numerical algorithms were eliminated. These errors are the results of the Energy Plus scheme to adjust the zone temperature to meet the desired value defined by the control system set points.

HVAC systems architecture: cooling in Beirut relies mostly on unitary cooling units associated to given areas. The units are rarely running at the same time. To this end, we assume that only 50% of the floor area is cooled or heated at a given time, so that the simulated cooling and heating consumption are reduced to the half. In addition, when the outdoor temperature was less than 20 • C, the cooling loads were nullified.

Energy use and occupancy profiles: after applying the previous corrections, we assumed that the remaining discrepancies were caused by occupancy profiles, linearly altering the total bimonthly electricity consumption by a certain factor. This assumption is justified in section 5.4.2. Therefore, the calibrated energy consumption for each building was obtained as the multiplication of model estimated energy consumption and the averaged multipliers of its cluster.

Results

Archetypes distribution and 3D model

The USJ data covered two districts in Beirut as shown in figure 5 The figure illustrates the use of archetypes to generate realistic 3D models from the thermal point of view, including windows and balconies.

Data processing and model calibration results

After generating the thermal model, annual hourly loads from equipment, lighting, domestic hot water, cooling and heating were aggregated into simulated electricity consumption for residential and mixed buildings. In hospitals, the electricity consumption does not account for the heating and hot water demands since they are met by steam boilers. As for schools and governmental buildings, hot water is not available in general and therefore not accounted for. The electricity consumption profiles for residential and mixed buildings are described in section 5.4.3. Note that other building types were excluded from the calibration process as we will see in the next paragraph.

Following the interventions implemented in section 5.3.3, 31% of the buildings had to be removed due to missing EDL data entries. Note that all governmental buildings and hospitals lacked EDL data and were, as such, eliminated at this stage. To ensure consistency of the calibration process, schools, representing 1.3% of the remaining dataset, were removed because their schedules are different than those of residential and mixed No specific relation between the clusters distribution and the buildings coordinates could be concluded. Moreover, the clusters distribution was shown to be independent of the buildings' type, year of construction and archetypes, as shown in figures 5.6-5.8.

Thus, energy use and occupancy profiles can be considered as the main source of the disparities between actual and predicted consumption. More specifically, maximum occupancy corresponds to buildings whose electricity consumption is the highest compared to their counterparts with the same number of floors. Figure 5.9 shows a strong correlation between the maximum actual consumption and the predicted consumption of each building normalized to its acutal consumption, which validates our hypothesis. As a result, we assumed that the discrepancies in electricity consumption between EDL and our model were related to usage profiles and hence, we adjusted the consumption of each building according to its cluster coefficients representative of specific energy patterns. Table 5.3

summarizes the clusters multipliers for both districts.

The calibrated results, plotted against EDL data in figures 5.10b and 5.11b, show that the buildings consumption of each cluster are more grouped around the identity axis.

Outliers

Loads profiles

Comparison of the electricity consumption from EDL data with model predicted and calibrated output at a bimonthly resolution for the buildings in both districts is shown in figure 5.12. Table 5.4 illustrates the differences between the model outputs and EDL data before and after calibration. We found out that the monthly variation of the energy consumption predicted by the model before and after calibration follows a similar trend to that the of actual data: March and April were the months of least consumption, while peaks were detected during the hot months (July and August). The figure 5.12 also depicted the overestimation of the electricity needs variation over the year. The increase in energy demand between the less consumer months (March and April) and the most consumer months (July and August) was predicted by 97% and 107% for residential and mixed buildings respectively in district A, while in reality, it was only of 32% for both buildings types in district A. Very similar trends were found in district B, except that the actual increase was of 42% between the second couple and the fourth couple of months. 5.5 summarizes the percentage of energy consumption for each end-use in the two buildings types. Electricity consumption for cooling needs was the highest during the period from May till October, accounting for 43%, 54%, 55% and 74% of the total electricity consumption during July and August in residential and mixed buildings respectively, in district A and similarly in district B. Heating, on the other hand, reached its maximum shares during the first two months of the year with 14% and 16% in residential and mixed respectively in district A and with 16% and 15% in these buildings in district B. Equipment had almost fixed energy consumption in both buildings types, with maximum shares of 39% and 52% during March and April in residential and mixed buildings respectively, in both districts. Hot water, provided by electrical heaters in residential floors, shared the highest proportion of the total energy consumption in residential buildings during the last two months of the year with 40% and shared a lower proportion of 22% during summer in both districts. In mixed buildings, its share ranged between 10% in hot months and 23% in cold ones in both districts. Lastly, lighting needs decreased slightly during summer season and its shares were quite moderate, ranging between 9% and 18% in residential buildings, 8% and 21% in mixed buildings. However, by calculating the proportion of each component over the entire year, the equipment and hot water in residential buildings shared 32% and 31% of the total annual electricity consumption, while the cooling revealed to be of 19% in both districts.

Mixed buildings, on the other hand, had 39% of their electricity consumption for appliances, followed by 31% for cooling in both districts.

Residential

Archetypes Spatial correlation

In order to determine if buildings of different archetypes are linearly related in terms of electricity consumption, the Pearson coefficient was used. The value of the Pearson coefficient ranges from -1 to 1. When it approaches 0, the correlation of the two variables is weak. If the correlation factor is near 1 or -1, a strong correlation exists between the two variables. The

Pearson coefficient is used to analyze the relation between a building's energy consumption with the neighboring buildings. However, the definition of neighbors buildings can vary.

Therefore, we applied the correlation analysis to a radius of 1500m with a step of 50m.

When no buildings of the corresponding archetype reside within the given distance or no sufficient information can be deduced, null values were forced. In district A, correlation was only found between the electricity consumption of mixed buildings from the third period of construction and residential buildings from the second period on one hand and mixed buildings from the fourth period on the other hand, as shown in figure 5.19. The correlation

Temporal correlation

In order to determine the temporal variation of the electricity consumption, we applied linear correlation analysis between the energy consumption of the different months by the use of the Pearson coefficient. The analysis was conducted at the monthly resolution (figures 5.21-5.24). As in the previous section, null values were forced when there was no consumption of electricity.

A significant correlation characterized the electricity consumption of cooling equipment in hot months. For March and April, the correlation factor was around 0.85 and 0.95 respectively in district A and 0.7 and 0.8 respectively in district B with respect to months between May and October. The cold months were also highly correlated in terms of heating.

Discussion and conclusion

The previous sections have shown how to build, generate and calibrate an UBEM, capable of predicting the electricity consumption of a city by capturing the buildings behaviors.

The model takes as input, data containing buildings footprints, heights, floor area, number of floors, the corresponding archetypes as well as a weather file and a topographic map.

Here, an updated weather file, encapsulating the global climate change and the UHI in Beirut is required for the full-scale energy model of the city, especially with the high density of diesel generators dispersed over the city and between buildings. Moreover, a high-resolution topographic map can reduce the complexity of the model when generating the elevation profile of the city and projecting the buildings onto the inclined plane.

The geometrical properties such the WWR and the construction materials were obtained from previous studies, while the non-geometrical properties were collected from local reports when available and existing libraries. Efforts in this regard should be carried out to create an appropriate database for Beirut that incorporates construction materials and their thermal properties. The discrepancies between BEEM predictions and actual consumption data can be associated to the number of appliances, and occupancy schedules related to occupant's comfort and income level. In this context, most residents in Beirut tend to reduce their electricity usage for heating in winter. Moreover, during hot seasons, occupants tend to switch between different modes of cooling, including air conditioners, electric fans and natural ventilation. The penetration rates of HVAC systems and their properties should be further examined, especially in buildings built prior to 1945, characterized by their climate responsive architecture and application of sustainable concept [START_REF] Yeretzian | Beirut a sustainable dimension of the city and its buildings[END_REF]. These buildings rely on environmental conditions for indoor thermal comfort.

However, these buildings may have lost their intended characteristics with the random construction and the effect of climate change. Identifying the occupancy patterns and population densities should be a first basic step to enhance the reliability of UBEMs.

Moreover, extensive periodic audits are required to investigate occupant-related parameters, such as economic status, internal thermal comfort, appliances usage, daily and seasonal occupancy schedules and activities.

The main challenge faced during this study was the setup of schedules. As previously mentioned, the absence of energy use and occupancy profiles led the authors to adopt standardized schedules. The model exhibited a heavy burden on the energy sector in case citizens adopt a western expensive and comfort lifestyle. In fact, Lebanon is considered an economically weak country. Moreover, the electricity rationing already thrusts residents to modify their behavioral patterns resulting in an amount of suppressed demand. However, in presence of bimonthly metered data (EDL data), it was possible to inspect the Lebanese energy use patterns and relate them to socio-economic and political circumstances of the country. Predicted energy was adjusted by an analytical automated objective process as shown in section 5.3.3. It was clear that a lower monthly variability characterizes the electricity consumption of residential and mixed buildings, compared to the simulated one, mainly attributed to the overestimated cooling demand. This was partially corrected by the assumption that buildings floors are partially cooled (and heated). The remaining differences can be explained by the fact that many Lebanese families (up to 15%) own two or more houses [START_REF] Mortada | The First Energy Indicators Report of the Republic of Lebanon[END_REF] in other regions in the country and leave the city during summer vacation. Moreover, high numbers of residential units are unsold or vacant, owned by expatriates or investors. Up-to-date statistics of this regard are needed to correctly estimate the energy consumption per household and per capita. However, issues related to data accessibility and privacy may be raised and should be properly addressed.

The calibration was employed at the temporal scale of the real data and yield satisfactory accuracies for the overall building energy demand. However, the authors cannot claim accuracy at hourly level with no metered data for validation and/or calibration.

Efforts should be placed on recording hourly electricity consumption by EDL or by occupant behavior modeling techniques [START_REF] Yan | Occupant behavior modeling for building performance simulation: Current state and future challenges[END_REF][START_REF] Wilke | A bottom-up stochastic model to predict building occupants' time-dependent activities[END_REF][START_REF] Haldi | The impact of occupants' behaviour on building energy demand[END_REF]. The calibrated electricity consumption showed similar spatial clustering as the metered data from EDL in both districts, as was found in section 5.4.4. This proves the relevance of our calibration process.

The energy model can be employed to relate the observations of electricity consumption at one location to those at other locations. The main interest in revealing spatial clustering is its importance in generating zones for smart grid distribution. It is important to note that the perimeter of the neighborhood or city divided by the auto-correlation length is supposed lead to the same number of similarly behaviors clusters of buildings obtained in section 5.3.3 or the same number of archetypes. However, our district perimeter over the obtained correlation length did not match the number of clusters neither the number of archetypes for many reasons: first, our data is incomplete and many buildings were eliminated before the calibration process. Secondly, on the top of a cluster or archetype, there may be multiple classes of behaviors, especially since in our model, we are attributing each floor an archetype. Exemplary, mixed buildings include residential, offices and retail floors, each with its own schedules. Moreover, their proportions depend on the total number of floors, so a mixed building with 4 floors differ in terms of occupancy-related parameters from a mixed building with 8 floors, and so on. In addition, Beirut City represents the central administrative and commercial area of Lebanon. Human's mobility in and out the city needs to be investigated as highly possible influence on the energy demand [START_REF] Mohammadi | Urban Energy Flux : Human Mobility as a Predictor for Spatial Changes[END_REF],

especially when extending the scope of the model to the whole city and integrating commercial and governmental buildings. Therefore, further research are required to improve the spatial auto-correlation study.

In the generation of an UBEM, each building is represented as an individual 3D thermal modeling dependent on its urban context, attributed an archetype based on a set of parameters. Temporal loads profiles are then coupled with GIS mapping techniques to support urban decision making. Considering these characteristics, no UBEM studies within the Mediterranean region was previously achieved. Bottom-up studies were mainly focusing on defining representative buildings for the building stock and assessing their energy performance [START_REF] Yathreb | Analysis of a Residential Building Energy Consumption as Base Model in Tripoli, Lebanon[END_REF][START_REF] Attia | Development of benchmark models for the Egyptian residential buildings sector[END_REF].

In Spain, a bottom-up approach combining statistical and engineering models was used to predict the energy performance and indoor thermal comfort of a neighborhood of a city located on the Mediterranean coast of the country [START_REF] Braulio-Gonzalo | A Methodology for Predicting the Energy Performance and Indoor Thermal Comfort of Residential Stocks on the Neighbourhood and City Scales. A Case Study in Spain[END_REF]. The methodology consisted of correlating covariates such as the urban layout geometry and solar influence to variables such as the cooling and heating energies, and discomfort heating and cooling hours. The prediction models were then applied to buildings in a neighborhood without representing them by 3D architectural thermal models. Other studies were applied to correlate the urban texture to UHI [START_REF] Salvati | Climatic performance of urban textures : Analysis tools for a Mediterranean urban context[END_REF] with a top-down approach and a simplified representation of the urban morphology. A similar study was applied to Beirut using the Town Energy Model (TEB) [START_REF] Kaloustian | Effects of urbanization on the urban heat island in Beirut[END_REF]. TEB [11] is a conceptual model that simulates the energy and water exchanges between the city and the atmosphere by parametrizing the dynamic and thermodynamics interactions. It is based on the urban-canyon geometric model [12].

These approaches are limited to specific applications. Though they compute fast simulations, the simplified representation of the urban canyon leads to a lack of necessary details for the assessment of any future interventions at the building level and end-uses level. In addition, the model does not incorporate the stochastic occupant behaviors. The bottom-up approach presented in this paper overcomes all these drawbacks and enriches the accuracy of the model. Furthermore, its calibration concentrates on integrating the significant weight of occupant behaviors into the urban model. Its capability to handle the complexity of an urban area can be used to enhance the findings of the previous studies within the Mediterranean region. The adopted methodology can be replicated in any other districts or cities in Lebanon and the region. Its multi-scalability is recognized spatially when ranging from building to the city level, and temporally when ranging from hourly to yearly resolution. This allows for a spatiotemporal energy patterns analysis to allocate hot spots and peak times of energy demands. In this regard, 2D and 3D urban energy maps are very informative (figure 5.15) and enable critical analysis. Application of energy conservation or retrofit measures to specified buildings with high energy demands can then be explored. Moreover, the scaling down to hourly energy consumption patterns is crucial for the management of the energy distribution by smart rationing of electricity. In this context, the model may provide insights into the optimal integration strategy of the solar power on buildings rooftops, as will be studied in future research.

Policy Implications

To meet the GHG emissions reduction goals, limit the energy demand and achieve security of energy supply, the country needs to introduce energy policies to manage the energy sector and resources in the city and that are more social and environmental oriented.

Energy policy can be d.efined as a strategy adopted by a given entity (often the government) to address issues related to energy development to insure its sustainability, including energy production, distribution and consumption [START_REF] Solangi | A review on global solar energy policy[END_REF][START_REF] Banovac | Designing a global energy policy model[END_REF]. Legislation, international treaties and incentives are among the main outcomes of an energy policy.

The main purpose of UBEM is to act as a decision support tool for architects, urban planners and energy policymakers to assess the current energy demand patterns and the future impacts of energy retrofitting programs and energy supply infrastructure changes [18]. In the previous sections, the generation of an UBEM for Beirut City has been shown.

Due to uncertainties related mainly to human behaviors, the model had to be calibrated for reliable results compared to metered data so that it can inform energy policies.

Recommendations for the management of the energy in Beirut based on scientific evidence from this research were published in the policy brief entitled Beirut as a Smart City:

Redefining Urban Energy, in collaboration with Issam Fares Institute for Public Policy and International Affairs at the American University of Beirut (Appendix B). For Lebanon, a country with chronic energy sector failings reflected by the widening deficit of power delivery and quality, this work offers three distinct policy connections that could help with resolving real and existing challenges:

First, due to the unrefined and bi-monthly billing of electricity as well as the distorted electricity market in the country, EDL, the national power utility does not have an accurate account for consumption loads and their variations spatially and temporally.

The developed model provides a powerful tool to understand better load variations, and thus plan future strategies and investments accordingly. For example, one of the main techno-policy challenges lies in estimating Lebanons real demand for electricity and its growth rate in the future due to the complex interactions between EDL and operators of private diesel generators, some of which are metered and some remain largely unregulated; and the existence of suppressed demand that is due to EDLs failure to provide power around the clock. With the help of smart metering that EDL is starting to implement, this model can be used to scientifically calibrate, verify and estimate growing demand on a national and sub-national scales.

Second, until EDL can provide electricity without any interruptions, power outages will continue to occur. Predicting electricity consumption patterns in Beiruts urban environment, as shown in this work, could ultimately help inform smart rationing of the electricity that provides an efficient mapping between loads and available supply capacity.

Third, the developed model could be utilized to test various policy instruments and ideas that promote energy savings such as the implementation of building efficiency codes, installation of water heaters, etc. One particularly useful application is to test the potential of rooftop solar PV systems to meet demand, or part of it, during certain daily and yearly peak times. Separately, the authors are working on a PV supply study that can ultimately be coupled with this work to provide a real assessment of the potential of rooftop solar PV systems to provide cost savings as well as increase power coverage in Beirut.

Conclusion and perspectives

The raising concerns regarding cities' sustainability oriented recent research to focus on possible demand reduction and clean supply of the energy. Technologies to satisfy the twofold objective of energy management are available, but the problem is confined on how to contextualize their implementation for a specific application. This difficulty is mainly related to lack of information describing the specific demand-supply nexus. Based on the state of the art overview, urban building energy models have proved to be effective management tools for present and future energy estimations, and for assessment of possible technological interventions' impacts on urban energy, for the purpose of informing policy and programmatic decision making.

The limited application of such tools is partially explained by the availability of data.

Chapter 3 focuses on the nature of data indispensable to create an urban energy model.

Accordingly, an examination of the availability, completeness and usefulness of the data sources are required. This leads to an assessment of the required efforts to complete, pre-process and organize this data, considering its dispersal nature and reliability. Data management and flexible storage are two key points to facilitate its use and provide organized and clean inputs for the simulation tools. In order to construct adaptable data, remote sensing tool and machine learning algorithms were applied. An archetypal classification of buildings, based on their types and periods of construction, was adopted to reduce the complexity of dealing with large number of buildings at the urban scale. After this crucial step of data management, 3D geometrical approach was entirely developed to automatically generate the 3D thermal buildings and further subdivide them into floors.

Balconies and DTM are added features to the model, and their integration was justified with a simplified example in chapter 4, as affecting parameters in the radiative budget, and hence the energy budget of buildings. Given the limited amount of useful data, the study was limited to two districts within the city of Beirut.

In chapter 4, a new approach to simulate daylight accessibility in urban areas and thus link it to urban morphology was presented.

The methodology is based on advanced computational tool to simulate the radiative budget in urban environment. Given the complexity of the 3D model of the districts involved in this chapter, the simulations were complex and time consuming. Of course this could be controlled by simplifying input parameters such as the illumination grid size and geometries meshing, but this intervention would be at the expense of results accuracy. By adopting a simplified approach, the impact of urbanization on daylight access was assessed and revealed a significant decrease. Due to unavailability of satellite images for old Beirut for 3D radiative budget comparison, another approach was adopted, where urban aspects such as the average height and orientation of surrounding buildings, their standard deviation, the building' height and position in space were associated to daylight availability on buildings' facades with artificial neural networks algorithms. With a MAPE error of 17%, the trained algorithm presents a reliable and consistent method to assess daylight accessibility for urban planning, future buildings' design and estimate its variation over the years if input features could be obtained. Chapter demand was also reported, suggesting possibility for smart grid zoning. The results of BEEM were convenient to provide scientific evidence to inform policy. Recommendations based on the analysis of BEEM outputs were made and published in a policy brief, enclosed in chapter ??.

Perspectives

The work presented in this thesis is the first UBEM at this scale in Lebanon and the region to the author's knowledge. While it has proved to present several advances, further work are still needed to promote its use in urban energy planning and some relevant developments can be considered.

Large-scale data management

Data availability was a major obstacle that limited the scope of the thesis to the one represented in this dissertation. Therefore, an inevitable need for databases is raised. Data repository should include:

Up-to-date list of materials used in buildings' construction, along with their thermal and optical properties, in addition to construction methods such as building components' layers.

Recent and old satellite images for cities of interest that can be used in remote sensing, not restricted to buildings' digitizing and vegetation classification, but also for urban properties' identification.

Characteristics of energy end-uses, such as HVAC, appliances and lighting, including their rates of penetration, their efficacy and their use patterns.

Occupant related parameters, such as their activities, behaviors and preferences. In this context, complexity raises due to the stochastic nature of humans patterns.

Data management and organization in a key factor for an efficient and less time-consuming generation of UBEMs. Common templates of information and the possibility for sharing will increase the adoption of these tools, enhance their reliability and facilitate their importation to the models.

microclimatic conditions by linking it to computational fluid dynamics CFD analysis, or by using urban weather generators to simulate the UHI effect..

Further studies

The presented model allows the assessment of energy demand within a spatiotemporal frame.

It assess the present energy consumption and estimates future energy patterns under certain technological interventions, such as the implementation of energy conservation measures. In addition, it allows the estimation of the contribution of urban forms and energy end-uses in energy demand at building scale and city scale. The results of the energy model, the daylight model and their relation to urban metrics can be combined for a more holistic analysis of different designs, energy and low-carbon strategies, and urban planning. We started to explore these relations in sections 4.5.3 and 4.5.4, proving that the model is suitable for such analysis. Another important aspect of BEEM, is its capability to manage energy supply as well. The spatiotemporal energy maps can be coupled with solar irradiation and solar maps to estimate the potential saving from rooftop PV systems, the optimal distribution of the produced energy, and grid management to meet energy demand. It can also be used for network simulations such as district heating. Such capabilities help in informing urban planners and policy makers about possible scenarios to reduce energy demand, meet the urban needs and mitigate GHG emissions. Assessing the economical and social impacts of these interventions must be complementary to the model to provide a full adaptable plan for urban energy management.

Introduction

The expansion of cities worldwide is accompanied by socio-economic problems that range from challenges in providing services to compounding the impact of climate change. One major issue cities face is soaring demand for energy. Consequently, designing energy efficient cities will go a long way in reducing demand for power while also reducing emissions and air pollution. As such, energy modeling has been adopted to simulate buildings' energy consumption at early design stage, evaluate the efficacy of various design options, and optimize the overall performance of building systems in Beirut.

When extending the scope of the energy performance simulation to the urban scale, two main categories of urban energy models can be found: top-down models and bottom-up models. Top-down models are mainly used to explore the interrelations between the energy sector and variables like socio-economic indicators, energy price, and climate. However, they lack technical details and hence fail to study the impacts of new technologies and intervention. Bottom-up models, on the other hand, estimate individual end-uses then aggregate results to get the urban energy consumption.

MAIN REcoMMENDATIoNS

▸ Predicting spatial and temporal peak-loads could ultimately help in informing smart rationing of the electricity by the power utility company, EDL when the grid is strained. Additionally, it could match local demand to supply from solar energy through smart distribution and optimization of demand management ▸ The developed model could be utilized to test various policy instruments that promote energy savings such as the implementation of building efficiency codes, installation of water heaters, etc.

A priori, non-geometrical properties such as the occupancy and use schedules were set by referring to The American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards and previous studies. The apriori values were then modified to take into consideration the Beirut context (working hours, occupancy, and heating/ cooling systems)

Thermal model generation and calibration

Each building was represented by a 3D thermal model, divided into floors, with windows on facades and balconies if residential.

When simulating the energy consumption of each building, discrepancies between the predicted model outputs and the actual metered data from EDL were found. They can be attributed to three categorical errors as follows:

• Systematic errors resulted from the daily 3 hours of blackouts not accounted for during simulations, and from the constraints on indoor temperature and humidity that need to be met despite them being behaviorally unrealistic, such as keeping indoor temperature at 21°C in winter.

• Heating, Ventilation, and Air Conditioning (HVAC) systems operating when they are not supposed to, for example covering all floor's area and operating even under 20°C for cooling.

• Variation of equipment use and occupancy profiles.

A case Study: The city of Beirut

Beirut city, the capital of Lebanon, holds with its suburbs 50 percent of the urban population. It consumes 12 percent of the total national energy produced while it only covers 0.2 percent of the country's total area. Its building sector accounts for almost all of its electricity consumption. The city and its residents suffer from a minimum of 3 hours of blackouts per day. These key indicators demonstrate that Beirut is an energy-starved city.

In 2015, during the 2015 United Nations Climate Change Conference (COP21), Lebanon pledged to reduce its emissions by 30 percent by 2030 within a conditional commitment. Four years later, the country is still facing a significant challenge to manage its energy sector and integrate renewable energy. Therefore, developing an urban scale energy model could prove to be a very useful tool to assess Beirut's energy resources and provide insights for the management of its energy supply. The developed model could serve as a decision support system by estimating energy consumption patterns and identifying grid peak demands with a spatiotemporal distribution. The latter, integrated with the potential solar production findings, will offer a great potential in estimating the savings and recommending targeted energy-use policies to alleviate peaks and ensure an efficient and optimal resources distribution.

Another feature of the energy model for Beirut is its capability to project energy consumption under normal conditions. Currently, estimates of demand do not account for the suppressed amount of electricity, since during outages, occupants modify their behaviors and alter their energy consumption patterns and preferences.

They are suitable for improvement and technological intervention studies. They can be further subdivided into statistical and engineering models. The former relies on a large amount of historical dataset to estimate energy consumption at the metered data scale. Engineering models apply thermodynamic and heat transfer equations at the building level, leading to high accurate results, and offer the maximum flexibility to test end-use energy conservation measures.

The work presented in this policy brief has been extended from the individual building scale to the urban scale, allowing for the assessment of building to building interactions as well as of buildings to other urban forms interactions.

Methods

Urban Building Energy Model UBEM, which has been utilized in this study, combines the bottom-up statistical and engineering models and communicates results with GIS platform for energy maps generation, used for results analysis and comparison with measured data or surveys to help designers and policy makers.

Data collection and processing

The conducted analysis utilized data collected from a range of sources to create the model's dataset, namely:

• Hourly weather data from Beirut International Airport.

• Topographic map to create the digital elevation profile.

• GIS data incorporating buildings' footprints, area, number of floors, year of construction, function.

Cleaning the data was a crucial step to ensure model's consistency and accuracy. Buildings with the following drawbacks were removed from the data set:

• Mismatch of buildings' footprints with their corresponding position in the satellite image.

• Missing entries such as the number of floors, function or EDL electricity consumption.

Buildings' segmentation: Beirut buildings were grouped based on two parameters, the building function and the year of construction based on a historical architectural study of the buildings. Building's function helps in setting a building's occupancy patterns and determining internal heat loads, while its year of construction informs about construction materials and methods.

For the specific case of Beirut, five distinguished construction periods were identified based on Georges Arbid's study: 1900Arbid's study: to 1923Arbid's study: , 1924Arbid's study: to 1940Arbid's study: , 1941Arbid's study: to 1960Arbid's study: , 1961 to 1990, and after 1991. Regarding the function, the buildings were grouped into six classes (residential, mixed, hospitals, schools and governmental buildings). In total, 30 archetypes were generated.

Buildings' Characterization: Thermal properties were obtained from the Technical Guide for the application of the Thermal Standard for Buildings in Lebanon published in 2005, the simulation software's default library and online libraries.

Therefore, one of the major advantages of the developed model is to provide projected estimates for demand, which are currently underestimated.

Results

Data for buildings in the Bachoura area was obtained. After the cleaning process and removal of outliers (buildings with abnormal EDL electricity consumption), 1830 residential and mixed buildings were represented by thermal models. Most of these buildings were built between 1940 and 1990. The difference between the metered data and the predicted data was of 203534 MWh, representing an overestimation of 200 percent. After eliminating the impacts of the systematic errors and the HVAC systems related errors, we found that the remaining disparity was independent of the buildings position and archetype.

More than 70 percent of mixed buildings' floors are residential. Therefore, the majority of the floors in the Bachoura area are residential, sharing similar occupancy schedules.

Accordingly, we could think about energy use and occupancy profiles as the main reason behind the disparities and mismatch between actual and predicted consumption. More specifically, maximum occupancy corresponds to buildings whose electricity consumption is the highest compared to their counterparts with the same number of floors. Therefore, the ratio of the latter consumptions is an indicator of the occupancy level and should explain the aforementioned mismatch. Results showed a strong correlation between this indicator and the ratio of the model's predictions to the actual consumptions, which validates our hypothesis. In terms of load profiles, the model could replicate the overall bimonthly electricity consumption of the buildings. March and April are the least consumptionintensive months while July and August are the highest. The equipment and hot water in residential buildings shared around a third of the total annual electricity consumption, while cooling's share has been estimated to be around 20 percent. Mixed buildings, on the other hand, had 39 percent of their electricity consumption for appliances, followed by 31 percent for cooling.

"The developed model could serve as a decision support system by estimating energy consumption patterns and identifying grid peak demands"

The electricity consumption results also showed similar spatial clustering as the metered data from EDL with the strongest correlation at 40 meters, i.e. the energy consumption of two buildings is most similar when they are 40 meters apart. Hence, the energy model can be employed to relate the observations of electricity consumption at one location to those at other locations.

Model Applicability

The model multi-scalability is recognized spatially when ranging from building to the city level, and temporally when ranging from hourly to yearly resolution. This allows for spatiotemporal energy patterns analysis to allocate hot spots and peak times of energy demands, as shown in Figure 2. Therefore, energy measures can be optimized to specified buildings with high energy demands. Figure 2 shows peak consumptions across the study area in residential and mixed buildings constructed between 1941 and 1990.

Calibration du modèle

3 List of Figures 1 . 1

 311 Fig. (b), then windows and balconies generation in (c) and (d) respectively, and finally the adjacency and shading are shown in (e). . . . . . . . . . . .

4. 9

 9 Different 3D models of the urban area used in the simulation of the radiative budget in DART. The models represent different Level of Details (LoD). . . 4.10 Elevation profile of mean incoming radiation along the buildings' facades, at 7am on 21 June (Band [470nm, 620nm]. . . . . . . . . . . . . . . . . . . . . . 4.11 Elevation profile of windows mean incoming radiation along the buildings' heights, at 7am on 21 June (Band [470nm, 620nm]. . . . . . . . . . . . . . . 4.12 Buildings distribution in zones 1 and 2. . . . . . . . . . . . . . . . . . . . . . 4.13 Spatial distribution of the buildings heights in zone 1 and 2. The driving network is also shown. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.14 Buildings' heights distribution in both zones. . . . . . . . . . . . . . . . . . . 4.15 Buildings' orientations distribution in both zones. . . . . . . . . . . . . . . . 4.16 3D surface radiative budget of the intercepted energy by buildings in the 6 subzones of zone 1, at 9am on March 21 (Band [400nm, 475nm], Illumination grid = 10cm). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.17 A top view of the 3D surface radiative budget of the intercepted energy by buildings in zone 1, at 5pm on September 21 (Band [400nm, 475nm], Illumination grid = 1cm). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.18 Vertical profile of the intercepted daylight energy on the buildings' windows in zone 1, at different hours of fours days of the year. . . . . . . . . . . . . . 4.19 Vertical profile of the intercepted energy on the buildings' windows in zone 2, at different hours of fours days of the year. . . . . . . . . . . . . . . . . . . . 4.20 Mean facade intercepted daylight energy variation in function of buildings' orientation in zone 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.21 Facades intercepted energy variation in function of buildings' orientation in zone 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.22 Mean absolute percentage error of the trained neural networks for each simulation. The simulation index represents the number in sequence representing different dates. For example, simulation index 1 refers to the simulation of the radiative budget at 12pm on March 21. . . . . . . . . . . . 4.23 Neural Network architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.23 Weights matrices of the neural network hidden and output layer, for estimating daylight at 3pm on 21December. . . . . . . . . . . . . . . . . . . . 4.24 Mean electricity consumption as function of the daylight potential, normalized by floor area, in zone 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.25 Mean electricity consumption as function of the daylight potential, normalized by floor area, in zone 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Buildings functions distribution in districts A and B . . . . . . . . . . . . . . 5.2 Buildings years of construction distribution in districts A and B . . . . . . . 5.3 Sample of the generated 3D model of buildings in the Bachoura area, Beirut, Lebanon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Distribution of district A buildings based on their clusters . . . . . . . . . . . 5.5 Distribution of district B buildings based on their clusters . . . . . . . . . . . 5.6 Ratio of actual consumption over predicted consumption of buildings in district A, distributed based on their year of construction (right) and their type (left) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Ratio of actual consumption over predicted consumption of buildings in district B, distributed based on their year of construction (right) and their function (left) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.8 Clusters distribution as function of archetypes (District A: left, District B: right) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.9 Correlation between the ratio of the maximum EDL consumption by floor to EDL consumption, and the ratio of the predicted consumption to EDL consumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.10 Scatter plots of buildings consumption in District A for (simulated electricity consumption in kwh/m 2 ) in x axis versus (actual electricity consumption in kwh/m 2 ) in y axis after clustering. . . . . . . . . . . . . . . . . . . . . . . . 5.11 Scatter plots of buildings consumption in District B for (simulated electricity consumption in kwh/m 2 ) in x axis versus (actual electricity consumption in kwh/m 2 ) in y axis after clustering. . . . . . . . . . . . . . . . . . . . . . . . . 5.12 Total electricity demand in districts A and B . . . . . . . . . . . . . . . . . . 5.13 Bimonthly calibrated electricity consumption by buildings types in District A 5.14 Bimonthly predicted electricity consumption by buildings types in District B List of Tables 3.1 Original variables datasets and the selected variables for BEEM. . . . . . . . 4.1 Central wavelength, spectral bandwidth and mean luminosity function of the 6 spectral bands of the visible spectrum defined in this study. . . . . . . . . . 4.2 Urban metrics characterizing the two zones. . . . . . . . . . . . . . . . . . . . 5.1 Discrete sky clearness categories . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Loads and conditioning parameters used for the electricity consumption calculations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Multipliers used to equate the bimonthly profile consumption with the cluster average consumption. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4 Comparison between the energy model outputs and the actual metered data before and after calibration. . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Percentage of energy consumption by end-uses for residential and mixed buildings in both districts. . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Figure 1 .

 1 Figure 1.2 shows the development of the Total Primary Energy Supply in Lebanon between 1971 and 2014. The net imports increased from 5.45 million of toe (tons of oil equivalent) in 2008 to 7.61 million of toe in 2014, of which 43% were used for electricity generation in 2014

Figure 1 . 1 :

 11 Figure 1.1: Aerial photos of Beirut in different years from multiple sources.

Figure 1 . 2 :

 12 Figure 1.2: Total Primary Energy Supply evolution between 1971 and 2014 [1].

Figure 3 . 1 :

 31 Figure 3.1: Buildings' distibution over Beirut.

Figure 3 .

 3 3 shows a high accuracy concerning the (x,y) geolocation of the two images. A high correlation was revealed between the elevation points and the DSM pixels values, as shown in figure 3.4. It was found that 50% of the differences between the elevation points and the DSM fall within the range of [0, 3m]. The buildings' heights were then obtained by subtracting the Digital Terrain Model (DTM) provided by the National Council for Scientific Research (CNRS) from the DSM.

Figure 3 . 2 :

 32 Figure 3.2: Elevation points of buildings (left) and DSM profile (right)

Figure 3 . 4 :

 34 Figure 3.4: Correlation between elevation points from the Army field work and the DSM raster image obtained from processing Pleaides images. R 2 = 0.866

Figure 3 . 5 :

 35 Figure 3.5: Beirut Energy Model BEEM methodology flowchart.

Figure 3 . 6 :

 36 Figure 3.6: Fig. (a) shows the DTM creation, followed by a simple extrusion shown in Fig. (b), then windows and balconies generation in (c) and (d) respectively, and finally the adjacency and shading are shown in (e).

Figures 4 .5 and 4. 2 represent

 42 exemples of these products results. Terms BOA (Bottom of the Atmosphere), Sensor and TOA (Top of the Atmosphere) indicate that the images are simulated for a sensor at the bottom, within and top of the atmosphere.

4 .Figure 4 . 1 :

 441 Figure 4.1: DART simulation of Chris sensor. Howland forest, USA. a) Image. b) TOA VIS-NIR spectra.

Figure 4 . 2 :

 42 Figure 4.2: DART images of St. Sernin basilique (Toulouse). Radiometer: a) BOA, b) TOA, c) Airborne LIDAR (display with SpdLib software). Atmosphere: mid-latitude summer. Visibility = 23Km.

Figure 4 . 3 :

 43 Figure 4.3: Earth-atmosphere representation

  parameters of foliar specular behavior (roughness, refractive index), calculated by implemented models in DART PROSPECT-5 and PROSPECT-D, given the leaf biochemical input parameters, such as the water content and chlorophyll concentration to list few. Leaf dimension for hot spot Leaf angular distribution Leaf clumping or leaf agglomeration at certain points Temperature property for thermal emission calculation 4.2.3 Earth-atmosphere radiative transfer The earth-atmosphere radiative transfer is simulated in five stages as shown in figure 4.4: Stage 1 or Illumination stage: the earth is illuminated by the sun radiation and atmosphere scattering. In case mode T is used, the thermal emission form the atmosphere is added. This stage gives the downward BOA radiance L BOA (Ω ↓), upward TOA radiance L T OA (Ω ↑) and the upward and downward sensor radiance. Stage 2: landscape RT with/without thermal emission, by tracking the L BOA (Ω ↓) computed in stage 1. This stage gives the landscape radiative budget, albedo and upward BOA radiance L BOA (Ω ↑).Stage 3 atmosphere backscattering down to the landscape by tracking the upward BOA radiance computed in stage 2. Stage 4 landscape RT of the radiation that the atmosphere backscatters at stage 3. Stage 5 Transfer of the upward fluxes of stage 2 and 4. This stage provides the radiance at sensor and the upward TOA radiance L T OA (Ω ↑).

Figure 4 . 4 :

 44 Figure 4.4: Earth-atmosphere representation

  extinction coefficients (total, absorption and scattering), α e = α a + α d L B (r, Ω n ) = radiance emitted by a black body P (r, Ω m → Ω n )/4π = normalized phase function of the medium. It is the fraction of radiation that is intercepted along direction m that is scattered per unit solid angle along direction Ω n . The terms of right-hand side of the equation give the variation of L(r, Ω) per path unit dr(Ω): 1st term: attenuation (absorption + scattering "Ω → 4π") of a wave during its propagation dr(Ω).

  2nd term: scattering along (Ω) due to scattering at point r of radiation incident from all directions of space. 3trd term: thermal emission. At the top of the earth scene BOA (Refer to section Earth-Atmosphere scene), we have two components that form the irradiance: the direct sun and the atmosphere. The source vector of both components are computed as follows: Direct sun source vector W (Ω s ) = E s (Ω s ).|µ s |.∆x.∆y E s (Ω s ) is the direct sun irradiance at the top of the scene (W/m 2 /m) µ s = cosθ s with θ s the sun zenith angle ∆x.∆y is the cell face area Atmosphere source vector W a (Ω s ) = L a (Ω n ).|µ n |.∆x.∆y.∆Ω n due to atmosphere scattering and/or thermal emission L a (Ω n ) is the atmospheric radiance (W/m 2 /sr/m) along direction Ω n n ∈ [1, N ] with N the number of downward discrete directions

Figure 4 .

 4 Figure 4.5 illustrates the 3D RB at 0.56µm of a schematic building. The building is made of 4 walls with 2 windows each one, and a roof with a swimming pool. All surfaces are lambertian,

Figure 4 .

 4 5d shows RB images for the scene and the roof. It shows also 2 vertical sections of the 3D RBcell that corresponds to a sunlit wall (x = 20) and a shadowed wall (x = 60).

  Figure 4.6: 3D view of Beirut intercepted energy (W/m 2 /µm) by triangles for two different orders, on June 21 2017 at 17h (Band: 425nm).

  construction of each building, buildings built after 1985 were removed to represent the scene of the 1980s (see Figures 4.7 and 4.8).

Figure 4 . 7 :

 47 Figure 4.7: Side view of the 3D model of the district case study

Figure 4 . 8 :

 48 Figure 4.8: Top view figures representing the district in the 1980s (left) and recently (right).

2 and 3

 3 is compared to the intercepted energy by the walls in model 0, as shown in Figure4.11. A further decrease of the intercepted radiation is detected, especially at the middle and highest floors, since the windows surfaces' area is less than that of the walls (not all walls have windows). At lower floors, the intercepted energy is almost unchangeable because all the buildings are mixed, which means that the windows of the first floors are in fact glazed facades for shops which cover all the walls, i.e. the area of the windows is almost equal to that of the walls.

  Figure 4.9: Different 3D models of the urban area used in the simulation of the radiative budget in DART. The models represent different Level of Details (LoD).

Figure 4 . 10 :

 410 Figure 4.10: Elevation profile of mean incoming radiation along the buildings' facades, at 7am on 21 June (Band [470nm, 620nm].

Figure 4 . 11 :

 411 Figure 4.11: Elevation profile of windows mean incoming radiation along the buildings' heights, at 7am on 21 June (Band [470nm, 620nm].

4. 4

 4 Effect of urban morphology on daylight accessibility 4.4.

  Since we are looking to represent the daylight, we use the standard illuminant D65 defined by the Commission Internationale de l'Eclairage (CIE) to convert the radiant energy into luminous (i.e., visible) energy. An illuminant is a mathematical representation of the light source based on human visual model, in this case the daylight. The average spectral sensitivity of human visual perception is also presented, called luminosity function. The CIE distributes standard tables with luminosity function values at 5nm intervals from 380nm to 780nm (the interval can be reduced to 400nm to 700nm since the values of the luminosity function becomes negligible at the wavelengths outside this interval). The following equation calculates the total luminous flux in a source of light:

Figure 4 . 12 :

 412 Figure 4.12: Buildings distribution in zones 1 and 2.

2 Figure 4 . 13 : 2 Figure 4 . 14 :Figure 4 . 15 :

 24132414415 Figure 4.13: Spatial distribution of the buildings heights in zone 1 and 2. The driving network is also shown.

Figure 4 . 16 :

 416 Figure 4.16: 3D surface radiative budget of the intercepted energy by buildings in the 6 subzones of zone 1, at 9am on March 21 (Band [400nm, 475nm], Illumination grid = 10cm).

Figure 4 .Figure 4 . 18 :

 4418 Figure 4.17: A top view of the 3D surface radiative budget of the intercepted energy by buildings in zone 1, at 5pm on September 21 (Band [400nm, 475nm], Illumination grid = 1cm).

Figure 4 . 19 :

 419 Figure 4.19: Vertical profile of the intercepted energy on the buildings' windows in zone 2, at different hours of fours days of the year.

4. 5 . 3

 53 Daylight potential and urban forms: Neural networks approach Many studies have proved the impact of buildings' orientations on solar potential and daylight access[START_REF] Li | Solar potential in urban residential buildings[END_REF]. The analysis of the results in the study hereby exhibits a difference of the intercepted energy of maximum 26.3% at 9am on March 21 between buildings oriented 105 • from north and buildings oriented 45 • from north (clockwise), for example.Figures 4.20

and 4 .

 4 21 show that the variations of the daylight as function of the buildings' orientation in both districts.

Figure 4 .

 4 22 shows the variation of the MAPE as function of the number of hidden layers. The optimum number of hidden layers is one. The number of nodes of this layer is 5, representing 2/3 of the input layer nodes. The sigmoid function was chosen and the following parameters were set: the number of iterations (20 iter) and the learning rate (0.0001).The data set was divided into a training set and a test set of 70% and 30% respectively. Simulation were carried out on the training set.

Figure 4 .Figure 4 . 20 :

 4420 Figure 4.20: Mean facade intercepted daylight energy variation in function of buildings' orientation in zone 1.

Figure 4 . 21 :

 421 Figure 4.21: Facades intercepted energy variation in function of buildings' orientation in zone 2.

Figure 4 . 22 :

 422 Figure 4.22: Mean absolute percentage error of the trained neural networks for each simulation. The simulation index represents the number in sequence representing different dates. For example, simulation index 1 refers to the simulation of the radiative budget at 12pm on March 21.

Figure 4 . 23 :

 423 Figure 4.23: Neural Network architecture

Figure 4 . 23 :

 423 Figure 4.23: Weights matrices of the neural network hidden and output layer, for estimating daylight at 3pm on 21December.

5. 3 .

 3 3 and 5.4.2). The daylight range was divided into bins, of which the mean electricity consumption was computed. Results were plotted in figures 4.24 and 4.25. The analysis of the graph showed that relation between daylight accessibility and electricity consumption is not enough clear, with a general trend of increase of electricity consumption when daylight availability increases. Two reasons can be thought of as possible explanations of these results.

Figure 4 . 24 :Figure 4 . 25 :

 424425 Figure 4.24: Mean electricity consumption as function of the daylight potential, normalized by floor area, in zone 1.

  convective heat transfers from the zone surfaces Nzones i=1ṁi C p (T zi -T z ) = heat transfer due to interzone air mixing ṁinf C p (T ∞ -T z ) = heat transfer due to infiltration and ventilation ṁsys C p (T sup -T z ) = system energy provided to the zone formulated from the difference between the supply air enthalpy and the enthalpy of the air leaving the zone through the system return air plenum.

  rate of exterior convective heat transfer h c,ext = exterior convection coefficient A = surface area T surf and T air = surface temperature and outdoor air temperature The modeler can specify the values of the exterior convection coefficient for the surfaces, even use schedules to encounter the values variations over time. However, EnergyPlus offers a range of model equations to estimate this coefficient without the modeler intervention.

  εσF sky (T 4 sky -T 4 surf ace )+εσF ground (T 4 ground -T 4 surf ace )+εσF air (T 4 air -T 4 surf ace ) (5.9) where: ε = longwave emissivity of the surface σ = Stephan-Boltzmann constant F ground = 0.5(1 -cosφ) =angle factor between the surface and the ground F sky = 0.5(1 -cosφ) = angle factor between the surface and the sky F air = F sky (1 -β) = view factor of wall surface to air temperature φ = tilt angle of the surface β = 0.5(1 + cosφ)

  irradiance on surface from the sky dome I circumsolar = I h F 1 a/b = irradiance on surface from circumsolar region where: I h = horizontal solar irradiance (W/m2) S = surface tilt (radians) a = max(0, cosα) b = max(0.087, cosZ) α = incidence angle of sun on the surface (radians) Z = solar zenith angle (radians)

F 2 =

 2 f (ε, ∆, Z) = horizon brightening coefficient ε = (I h + I)I h + kZ 3 (1 + kZ 3 ) = sky clearness factor (0 value means an overcast sky, 2-3 means intermediate to clear) ∆ = (I h m)I 0 = sky brightness factor I = direct normal solar irradiance (available in the weather file) k = 1.041 m = relative optic air mass

  α(I b cos S s S + I sky F sky + I g F ground ) (5.13) where: α = solar absorptance of the surface θ = angle of incidence of the suns rays S = area of the surface S s = sunlit area I b = intensity of direct radiation I s ky = intensity of sky diffuse radiation I g = intensity of ground reflected diffuse radiation (See Ground Reflectance section) F sky = (1 + cosφ)/2 = angle factor between the surface and the sky F sky = (1 -cosφ)/2 = angle factor between the surface and the ground

  Air Changes per Hour × Zone volume in m 3 /s F schedule = a value for a user-defined schedule (always 1) T odb = Outdoor air dry-bulb temperature The coefficients A, B, C and D depends on the infiltration situation the user wants to simulate. Natural ventilation It is the ventilation caused by wind entering the zone through an open area (an open window for example). The equation used is:

  volumetric air flow rate driven by wind C w = opening effectiveness A opening = opening area F schedule = user-defined schedule valueThe opening effectiveness is auto calculated in EnergyPlus:C w = 0.55 -|Ef f ectiveAngle -W indDirection|/180 × 0.25 (5.18)The wind direction is available in the weather file at an hourly scale. The effective angle is the normal angle of the opening area. It is important to point out that Energy Plus also takes other parameters that act as the threshold below or above which the natural ventilation is shut off:Minimum and maximum indoor temperature (ArchSim only takes the minimum value and calls it Setpoint, while the maximum value is put by default at 100 • C) Minimum and maximum outdoor temperature Maximum relative humidity (40% by default)

Figure 5 . 1 :Figure 5 . 2 :Figure 5 . 3 :

 515253 Figure 5.1: Buildings functions distribution in districts A and B

Figure 5 . 4 :Figure 5 . 5 :Figure 5 . 6 :Figure 5 . 7 :Figure 5 . 8 :Figure 5 . 9 :

 545556575859 Figure 5.4: Distribution of district A buildings based on their clusters

Figure 5 . 10 :

 510 Figure 5.10: Scatter plots of buildings consumption in District A for (simulated electricity consumption in kwh/m 2 ) in x axis versus (actual electricity consumption in kwh/m 2 ) in y axis after clustering.

Figure 5 . 11 :

 511 Figure 5.11: Scatter plots of buildings consumption in District B for (simulated electricity consumption in kwh/m 2 ) in x axis versus (actual electricity consumption in kwh/m 2 ) in y axis after clustering.

  The predicted model overestimated the electricity consumption by a minimum of 6,607 MWh in March -April and a maximum of 17,281 MWh in July-August in district A. In district B, the overestimation was much dramatic ranging between 22,757 MWh in March -April and 53,614 MWh in July-August. After calibration, the model almost attained the overall actual bimonthly consumption with an absolute difference between calibrated and actual values ranging from 33 to 132 MWh in district A and from 25 to 475 MWh in district B. The average RMSE of the yearly consumption was reduced from 105 kWh/m 2 the 25 kWh/m 2 in District A and from 112 kWh/m 2 to 22 kWh/m 2 in District B.

Figure 5 . 12 :

 512 Figure 5.12: Total electricity demand in districts A and B

Figure 5 . 13 :Figure 5 . 14 : 107 Total

 513514107 Figure 5.13: Bimonthly calibrated electricity consumption by buildings types in District A

Figure 5 .Figure 5 . 16 :Figure 5 . 17 :Figure 5 .

 55165175 Figure 5.15: Spatio-temporal distribution of the bimonthly predicted electricity consumption of the buildings in district B

Figure 5 . 18 :

 518 Figure 5.18: Spatial distribution of buildings energy consumption in district B

Figure 5 . 24 :

 524 Figure 5.24: Monthly correlation of heating loads (District B)

5

  represents the fundamental core of this thesis. The development of BEirut Energy Model was represented. The 3D model generated in chapter 3 fed a dynamic energy simulation engine to calculate the energy loads of 3,360 buildings, and then their electricity consumption. However, the limited available information and substantial uncertainties mainly related to simulation tools, energy systems and occupant behaviors should be addressed to adjust the predictable capacity of the model to study urban energy interventions and therefore avoid leading to erroneous conclusions. Accordingly, the use of metered data was indispensable to calibrate the model outputs. The calibration process was achieved by clustering the buildings based on their actual electricity and predicted consumptions, and find the multiplicative coefficients of their representative clusters centroids. These coefficients were used to correct the model outputs, but they also informed about the compatibility of the model inputs to represent the city under study. The applied methodology proved to be effective to reach acceptable accuracy in matching predicted outputs with actual data.Consequently, the analysis was pushed forward for a spatiotemporal identification of hot spots and peaks of energy demands. These energy maps are very informative for any intervention planning. Spatial autocorrelation of energy 123

Figure 1 :Figure 2 :

 12 Figure 1: Sample of the generated 3D model of Buildings in the Bachoura area, Beirut, Lebanon

Figure 1 :

 1 Figure 1: Organigramme de la méthodologie de BEEM Les informations nécessaires pour créer BEEM sont :

Figure 2 :

 2 Figure 2: La figure (a) montre la création du DTM, suivie d'une extrusion simple illustrée à la figure (b), puis de la génération de fenêtres et de balcons respectivement en (c) et (d), et enfin, les contours et les ombrages sont illustrés en (e).

Figure 3 :

 3 Figure 3: Exemple du modèle 3D généré de bâtiments dans la région de Bachoura, Beyrouth, Liban.Une fois le modèle 3D terminé, les paramètres de simulation de la base de données des



  Consommation d'énergie et profil d'occupation: après application des corrections précédentes, nous avons réalisé que les écarts restants ont été causés par des profils d'occupation, modifiant de manière linéaire la consommation totale bimestrielle d'électricité d'un facteur déterminé. Par conséquent, la consommation d'énergie calibrée pour chaque bâtiment a été obtenue en multipliant la consommation d'énergie estimée par le modèle et les multiplicateurs moyens de sa grappe. Les valeurs aberrantes ont été supprimées à l'aide d'un clustering spatial basé sur la densité d'applications avec bruit (DBSCAN) et d'auto-encodeurs, en fonction de la superficie des bâtiments, du nombre d'étages, du type, de l'année de construction et de la consommation EDL. Une fois les valeurs aberrantes identifiées, les bâtiments restants ont été regroupés en fonction de leur consommation d'électricité simulée et de leur consommation d'électricité réelle (EDL). Ce regroupement aide à identifier les bâtiments avec des horaires d'occupation similaires. La calibration du modèle est effectuée en appliquant une classification k-means au rapport entre EDL et les consommations prédites. Cela a conduit à l'identification de quatre groupes de bâtiments dans chaque district, comme indiqué dans les figures 4 et 5. Le nombre de clusters a été défini en fonction d'une procédure d'optimisation pour chaque district. Le modèle a surestimé la consommation d'électricité dans la plupart des bâtiments, principalement dans les groupes 0 et 1 des deux districts. Les clusters 3 regroupaient les bâtiments avec EDL avec un ratio de consommation d'électricité simulé compris entre 0,6 et 1,3 dans le district A et entre 0,5 et 1,5 dans le district B. La consommation de seulement 21 et 34 bâtiments a été sous-estimée dans les districts A et B, respectivement.

Figure 4 :Figure 5 :

 45 Figure 4: Configuration EPlus par défaut: occupation complète avec les normes ASHRAE

Figure 6 : 2 . 3

 623 Figure 6: Distribution spatio-temporelle de la consommation d'électricité prédite bimestriellement des bâtiments du district B

Figure 7 :

 7 Figure 7: Profil vertical de l'énergie interceptée sur les fenêtres des bâtiments de la zone 1, à différentes heures sur quatre jours de l'année.

Figure 8 :

 8 Figure 8: Bilan radiatif 3d de l'énergie interceptée par les surfaces des bâtiments dans les 6 sous-zones de la zone 1, à 9 h le 21 mars (bande [400 nm; 475 nm], grille d'éclairage = 10 cm).Des aspects urbains tels que la hauteur moyenne et l'orientation des bâtiments environnants,

  types et leurs périodes de construction est adoptée. Les informations supplémentaires requises pour générer le mod le 3D des bâtiments sont le nombre d'étages, la superficie des bâtiments et une carte topographique des zones d'étude. En couplant les modèles aux conditions météorologiques horaires, le modèle thermodynamique de 3,630 bâtiments est simulé dans EnergyPlus. L'adaptation du modèle à l'occupation de Beyrouth et aux comportements des utilisateurs est cruciale pour renforcer la fiabilité de BEEM. La disponibilité des données d'électricité actuelles permet la calibration du modèle, qui repose sur le regroupement des bâtiments et la recherche des coefficients des regroupements représentatifs de modèles d'énergie spécifiques. Après la phase de formation, la précision du modèle en matière de prévision de la consommation d'électricité est améliorée. En comparant la consommation réelle et les résultats calibrés, le pourcentage de l'erreur absolue moyenne de la consommation d'électricité a été réduite de 310% à 41% dans le quartier A et de 326% à 39% dans le quartier B.

BEEM, est généré pour estimer la consommation d'électricité du stock de bâtiment. Afin de réduire le temps de modélisation et de calcul, une classification archétypale des bâtiments iii basée sur leurs

  In 2016, 54.5% of world's population lived in urban areas. By 2030, cities are projected to house 60% of the global population

	Y	Building y coordinate	m
	α	Extinction coefficient	-
	∆	Sky brightness factor	-
	ε	Longwave emissivity of the surface	-
	ε 1. Introduction Sky clearness factor	-
	λ	Wavelength	nm
	φ	Tilt angle of the surface	• or radians
	A C φ V φ e,λ 1.1 General context Area Circuity Luminous flux Spectral radiant flux	m 2 -W W/nm
	C z ȳ(λ)	Air capacitance Luminosity function	KJ/m 3 -
	C p Ω i	Specific heat capacity of air Angular direction	KJ/Kg/K sr
	D µ	Pairwise Distance Mean	m [-]
	D e ρ	Euclidean Distance Reflectance	m -
	E σ	Irradiance Stephan-Boltzmann constant	W/m 2 /µm -
	h σ H S	Convective heat transfer coefficient Standard deviation of surrounding buildings' heights	W/m 2 /K m
	h σ O S	Enthalpy of the air Standard deviation of surrounding buildings' orientations	J/Kg •
	H θ s	Height Sun zenith angle	m •
	HS Θ	Mean height of surrounding buidlings Neural Network parameters	m -
	I ζ, η and µ	Irradiance Cosine angles	W/m 2 -
	L	Radiance	W/sr/m 2
	ṁ	Flow rate	m 3 /s
	N	Number of bins	-
	ŌS	Mean orientation of surrounding buidlings	•
	p	Probability	-
	q	Heat flux	W/m 2
	R f	Roughness coefficient	-
	T	Temperature	• C, • K
	Q	Heat load	J
	X	Building x coordinate	m

Table 3 .

 3

1: Original variables datasets and the selected variables for BEEM.

  • , 135 • ) and "specular" (30 • , 45 • ). Reflectance and RB have a spatial variability that depends on the illumination grid and the size of triangles used to simulate the scene. For example, in the nadir image, ρ roof is between 5.77 and 5.83 with an average IterX). As in remote sensing images, extreme RBtriangle values appear due to a few extremely small triangles that are illuminated or not. For example, %f int,IterX reaches

	of 5.77.
	3D RB is shown at triangle level (Figure 4.5b) and cell level (Figure 4.5c) for interception
	at direct sun illumination (i.e., Illudir), total illumination (i.e., Illudif), and infinite scattering
	order (i.e.,

1.61 for a few extremely small triangles, whereas its "real" maximum is equal to 1; it occurs for the roofs. Such extreme values do not occur for RBcell since all cells have the same size. Compared to walls, windows have the same irradiance, larger absorption and smaller scattering.

table 4 .

 4 1. Then, we calculated the mean of the luminosity function values for each spectral band as shown in the same table. Note that mean luminosity values must be divided by 100 since the relative value is 100 (for λ). Equation 4.9 becomes[START_REF] Saunders | Calculating luminous flux and lighting levels for domesticated mammals and birds[END_REF][START_REF] Lee | Introduction to Color Imaging Science[END_REF]:

		φ V =
	1	425	50	93.55659
	2	475	50	114.5002
	3	525	50	106.5425
	4	575	50	96.26877
	5	625	50	86.35938
	6	675	50	77.131782

6 i=1 ȳ(∆λ i )φ e,λ (∆λ i ) (4.10) Spectral band number Central wavelength [nm] Spectral bandwidth [nm] Mean Luminosity function [-]

Table 4 .

 4 1: Central wavelength, spectral bandwidth and mean luminosity function of the 6 spectral bands of the visible spectrum defined in this study.

Table 4 .

 4 2: Urban metrics characterizing the two zones.

		Zone 1 Zone 2
	Buildings' orientations entropy	1.06	1.08
	Streets' orientations entropy	1.53	1.866
	Road network circuitry	1.013	1.039

  

	HS : mean height of surrounding buildings
	ŌS : mean orientation of surrounding buildings
	σ H S : standard deviation of surrounding buildings' heights
	σ O S :	standard deviation of surrounding buildings'
	orientations
	H: building height
	X: building x coordinate
	Y : building y coordinate

Table 5 .

 5

	extraterrestrial irradiance

1: Discrete sky clearness categories

  .1. Attributing archetypes to buildings shows that residential and mixed buildings are the two main types in the two districts with 57.9% and 38.4% respectively in district A and 62.7% and 35% respectively in district B, as shown in figure5.1. Almost 40% of the buildings in both districts were built between 1941 and 1960 (the golden period) while 34% and 23% were built between 1961 and 1990 in district A and district B respectively, as shown in figure5.2. The main difference between the two districts is the buildings facing the sea in the north and the west of district A. All of these buildings are residential and mixed. In addition, most schools, hospitals and governmental buildings are within district B.The 3D model of a neighborhood in the Bachoura area in district B is shown in figure5.3.

Table 5 . 3

 53

		Jan-Feb	Mar-Apr	May-Jun	Jul-Aug	Sep-Oct	Nov-Dec
	District A Cluster	0.35	0.351	0.313	0.271	0.274	0.391
	Cluster	0.793	0.82	0.743	0.618	0.665	0.933
	Cluster	1.556	1.618	1.38	1.136	1.165	1.779
	Cluster	3.18	3.01	2.54	2.07	2.219	3.16
	District B Cluster	0.326	0.306	0.236	0.199	0.216	0.408
	Cluster	0.697	0.764	0.689	0.610	0.629	0.945
	Cluster	1.38	1.483	1.310	1.139	1.187	1.815
	Cluster	2.307	2.767	2.592	2.186	2.346	3.331

: Multipliers used to equate the bimonthly profile consumption with the cluster average consumption.

Table 5 .

 5 4: Comparison between the energy model outputs and the actual metered data before and after calibration.

	EDL (Mwh)	6, 302	5, 675	6, 674	7, 506	6, 650	6, 413	39, 220
	Predicted (Mwh)	13, 444	12, 282	18, 144	24, 788	20, 522	13, 289 102, 469
	RMSE (Kwh/m 2 )	15	12.5	18.8	27.5	22	13.1	105.4
	Calibrated (Mwh)	6, 250	5, 733	6, 737	7, 473	6, 693	6, 545	39, 433
	RMSE (Kwh/m 2 )	6.5	4	4.6	5.2	4.8	4	23.7
	District B	Jan-Feb Mar-Apr May-Jun Jul-Aug Sep-Oct Nov-Dec Yearly
	EDL (Mwh)	15, 631	14, 017	16, 993	20, 229	17, 702	18, 013 102, 585
	Predicted (Mwh)	40, 989	36, 774	53, 977	73, 844	60, 904	39, 632 306, 119
	RMSE (Kwh/m 2 )	17.8	12.5	19.6	29.1	23	13.4	112
	Calibrated (Mwh) 15, 156	14, 237	17, 017	19, 913	17, 602	17, 898 101, 823
	RMSE (Kwh/m 2 )	4.7	3.4	4.4	5.1	4.4	4.5	22

Table 5 . 5

 55

		Equipment	33	39	32	26		37	33
		Lighting	16	16	11	9		18	13
		Heating	15	3	0	0	0	4	4
		Cooling	0	4	29	44		0	19
		Hot water	36	38	28	22		40	31
	Mixed	Equipment	45	53	37	28		53	40
		Lighting	18	17	10	8		21	13
		Heating	15	2	0	0	0	5	4
		Cooling	0	9	41	56		0	28
		Hot water	20	20	12	9		22	15
	District B		Jan-Feb Mar-Apr May-Jun Jul-Aug Sep-Oct Nov-Dec Yearly
	Residential Equipment	32	39	32	25		37	33
		Lighting	15	16	11	9		18	13
		Heating	18	3	0	0	0	5	4
		Cooling	0	4	29	44		0	19
		Hot water	35	38	28	22		40	31
	Mixed	Equipment	47	53	37	28		53	41
		Lighting	18	17	10	8		21	13
		Heating	17	2	0	0	0	5	3
		Cooling	0	8	41	55		0	28
		Hot water	19	20	12	9		22	15

: Percentage of energy consumption by end-uses for residential and mixed buildings in both districts.

  Révéler l'utilisation énergétique spatio-temporelle d'une ville côtière méditerranéenne: le cas de Beyrouth amène la main-d'oeuvre à passer du secteur agricole aux secteurs industriels ou de services de la ville, phénomène bien connu comme tertiarisation. Ce transfert qui accompagne l'exode rural contribue indirectement à l'augmentation de la consommation d'énergie. Tout d'abord, en raison du manque de main-d'oeuvre, les produits agricoles doivent être mécanisés et transportés des zones de production vers les villes. Deuxièmement, les besoins en services de transport augmentent, entraînant une augmentation de la demande des combustibles fossiles et donc de ses impacts sur le climat urbain. Dernier point mais non le moindre, le développement économique des villes affecte les comportements et les modes de vie des habitants qui ont tendance à poursuivre les produits et services commerciaux, par exemple Une autre caractéristique de UBEM est la possibilité de les combiner avec une plateforme GIS. Les cartes d'énergie résultantes sont ensuite utilisées pour l'analyse des résultats et la comparaison avec des données mesurées ou des enquêtes pour aider les concepteurs et les décideurs. UBEM applique des équations de transfert de chaleur dans et autour des bâtiments représentés un en tant que modèle thermique dynamique 3D individuel. UBEM est donc capable de supporter développement de scénarios complexes. En outre, ils peuvent être combinés avec la simulation énergétique programmes. Le flux de travail de modélisation énergétique d'un UBEM nécessite beaucoup d'effort et de temps ressources étant donné la quantité massive de données pour potentiellement des milliers de bâtiments. Assembler, gérer et automatiser le flux de travail est essentiel. À cette fin, le stock de bâtiments est divisé en archétypes pour réduire la complexité et les exigences de calcul [13]. Le modèle de Boston développé par le MIT Sustainable Lab est un exemple illustrant UBEM [18]. Le modèle a été réalisé à l'aide d'un ensemble d'outils comprenant des systèmes GIS pour l'importations des polygones des bâtiments, Rhinoceros 3D [19] en tant qu'environnement de CAO et EnergyPlus en tant que le moteur de simulation thermique. Le Cette situation remet en cause l'engagement pris par le Liban de réduire ses émissions de gaz à effet de serre de 30% d'ici 2030. Le pays est alors confronté à un défi important pour gérer son secteur énergétique et intégrer les énergies renouvelables. Il est donc essentiel de développer un modèle énergétique à l'échelle urbaine pour la gestion des ressources et de l'approvisionnement en énergie à Beyrouth. Un tel modèle sert de système de support de décision en estimant les schémas de consommation d'énergie et en identifiant le pic de demandes du réseau avec une distribution spatio-temporelle. Ce dernier, intégré aux résultats du potentiel solaire produit [32], aide à estimer les économies et à recommander des politiques d'utilisation de l'énergie visant à réduire les pointes et à assurer une distribution efficace des ressources. Une autre caractéristique du modèle énergétique de Beyrouth est sa capacité à

	qu'elle ne couvre que 0,2% de la superficie totale du pays. Son secteur tertiaire (secteur
	commercial, bureaux publics, hôtels et hôpitaux) et le secteur résidentiel représentent 73% et
	26% de sa consommation d'électricité respectivement. Celles-ci représentent 39% et 14% des
	B. Appendix: French Summary (30 émissions de GES de l'électrification respectivement [27]. Alors que la demande en énergie projeter la consommation d'énergie dans des conditions normales. Actuellement, les
	augmente, le Liban souffre depuis de nombreuses années, d'une crise dans le secteur de estimations de la demande ne tiennent pas compte la quantité d'électricité supprimée, car lors
	pages) l'énergie. Le pays compte sur les produits pétroliers importés pour satisfaire ses besoins en des pannes, les occupants modifient leurs comportements et leurs habitudes et préférences de
	énergie, ce qui place le pays dans un état de grande vulnérabilité. Les importations nettes sont consommation d'énergie.
	passées de 5,45 millions de tep (tonnes d'équivalent pétrole) en 2008 à 7,61 millions d'euros
	en 2014, dont 43% ont été utilisés pour la production d'électricité en 2014 [28]. La capacité
	disponible pour la production d'électricité est de 2670 MW [29], dont seulement 1500 MW 2 BEirut Energy Model
	alternatives et environnementales, l'organisation technique, le rapport coût-efficacité et
	changement de comportement pour améliorer la qualité de l'énergie, sa disponibilité et ses
	impacts sur l'environnement et la nature [6]. Cependant, comprendre comment le système BEM et UBEM doivent être fiables et adaptables en ce sens qu'ils doivent fournir des
	énergétique d'une ville évolue dans le temps et dans l'espace sous ces interventions est estimations de la performance énergétique des bâtiments. Cependant, des divergences se
	cruciale pour soutenir le processus de prise de décision. Le secteur des bâtiments a été produisent souvent entre le modèle prédit et la consommation énergétique réelle mesurée du
	identifié comme responsables de 30% à 70% de la consommation d'énergie primaire dans les bâtiment, principalement en raison des imprécisions/incertitudes dans la paramétrisation et la
	structure du modèle [20]. Par conséquent, l'étalonnage de modèles énergétiques est essentiel
	pour atteindre un niveau de confiance dans les prévisions des modèles et encourager leur

appareils électriques et accessoires, augmentant ainsi la consommation d'énergie

[4]

. La ville étant un grand consommateur d'énergie et contributeur aux émissions des GES, une compréhension pertinente de son métabolisme est essentielle au développement des stratégies d'efficacité énergétique

[5]

. Afin d'assurer l'optimisation et la priorisation des mesures de conservation d'énergie à appliquer, une prise de décision programmatique ou alors la gestion de l'énergie est nécessaire. Elle consiste en la planification, la mise en oeuvre et le suivi de l'approvisionnement en énergie, sa distribution et son utilisation de manière efficace et efficiente pour réduire ses pertes et sa consommation. En outre, elle étudie les ressources villes

[7]

et de 30% des émissions en GES

[8]

. Par conséquent, développer des modèles énergétique à l'échelle de la ville peuvent être basés sur une approche « top-down » [10]-

[12]

. Dans ce cas, ils sont utilisés pour la modélisation climatique, mais ne fournissent pas les détails nécessaires pour tester des scénarios innovants à l'échelle du bâtiment. D'autre part, les modèles « bottom-up » de simulation physique ont été introduits comme outils de simulation efficaces pour modéliser l'impact du contexte urbain sur la demande énergétique des bâtiments

[13]

-

[16]

. Dans ce contexte, Reinhart et Davila

[17]

ont présenté les modèles énergétiques des bâtiments urbains (UBEM), qui sont des modèles de simulation physique ascendant utilisés comme outils de simulation efficaces pour simuler l'impact du contexte urbain sur la demande énergétique des bâtiments. Ils permettent de surmonter les lacunes des modèles statistiques et techniques en leur capacité à fournir des hypothèses d'énergie horaire, à estimer les impacts des nouvelles technologies, et incorporer les comportements des occupants

[13]

.

workflow consiste à générer les archétypes à partir de l'année de construction et les types de bâtiments, en extrudant l'empreinte du bâtiment pour créer la forme tridimensionnelle, en la divisant en étages, en générant des fenêtres et en assignant les propriétés thermiques spécifiques en fonction de l'archétype du bâtiment. Les surfaces d'ombrage étaient déterminées et chaque bâtiment a ensuite été représenté par un modèle thermique et son énergie la performance a été simulée dans EnergyPlus. Une étude suivante, où le même flux de travail a été appliqué pour un quartier de Boston, a exploré différents ECM pouvant être appliqués à réduire la consommation d'énergie

[14]

. jusqu'à un maximum de 2000 MW sont assurés par EDL (Electricité du Liban)

[30]

, la seule institution publique responsable de la production, la transmission et la distribution de l'énergie électrique au Liban

[31]

. En d'autres termes, un maximum de 65% de la puissance générée est émise par l'État et le reste par des générateurs privés de secours. Même si Beyrouth est soumis à la plus faible période de rationnement (seulement 3 heures alors que celle-ci dure jusqu'à 12 heures certaines régions du pays), réduire sa demande en énergie peut atténuer les coupures de courant quotidiennes dans les autres régions. BEEM, BEirut Energy Model, est un modèle énergétique urbain pour Beyrouth, et dont l'organigramme est présenté ci-dessous.

  Ce décalage entre les valeurs EDL et celles prédites par le modèle peut être principalement attribué aux modèles de comportement, aux systèmes CVC et aux erreurs systématiques. Par conséquent, toute correction à appliquer doit être compatible avec les erreurs catégoriques ci-dessus, être adaptable au contexte de la ville et justifiée en même temps. À cette fin, les interventions suivantes ont été mises en oeuvre:  Erreurs systématiques: une coupure de courant de 3 heures à Beyrouth représente un facteur de réduction de 1/8, calculé sur une moyenne annuelle. Par conséquent, la consommation d'électricité simulée a été réduite de ce montant. De plus, les erreurs liées aux algorithmes numériques ont été éliminées. Ces erreurs sont les résultats du programme EnergyPlus permettant d'ajuster la température de la zone afin d'atteindre la valeur souhaitée définie par les points de consigne du système de contrôle. Architecture des systèmes HVAC: le refroidissement à Beyrouth repose principalement sur des unités de refroidissement unitaires associées à des zones données. Les unités fonctionnent rarement en même temps. À cette fin, nous supposons que seulement 50% de la surface du sol est refroidie ou chauffée à un moment donné, de sorte que la consommation simulée de refroidissement et de chauffage est réduite de moitié. En outre, lorsque la température extérieure était inférieure à 20 ° C, les charges de refroidissement étaient annulées.

Après avoir généré le modèle thermique, les charges horaires annuelles provenant des équipements, de l'éclairage, de l'eau chaude sanitaire, du refroidissement et du chauffage ont été agrégées dans une consommation électrique simulée pour les bâtiments résidentiels et mixtes. En comparant les résultats obtenus aux données réelles bimensuelles de l'Electricité du Liban (EDL) disponibles pour un certain nombre d'immeubles, des différences ont été identifiés.

Lebanese army, Elbeyrouthy [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)]

https://www.google.com/earth/

https://www.esrl.noaa.gov/gmd/grad/solcalc/

IntroductionEn

2016, 54,5% de la population mondiale vivait en zone urbaine. D'ici 2030, les villes devraient abrite 60% de la population mondiale[1]. Les zones urbaines connaissent une croissance sans précédente raison de l'exode rural dû à des facteurs socio-économiques tels que la recherche de meilleures opportunités d'emploi ou niveaux d'enseignement supérieur.En outre, le développement de l'économie ainsi que l'industrialisation concentrée dans certaines régions au détriment d'autres, avec migration de pays pauvres souffrant de problèmes économiques ou asile de citoyens s'échappant des guerres et des zones de crise politique sont tous des facteurs contribuant à l'augmentation de la population urbaine.L'urbanisation a le potentiel de rendre les villes plus prospères et des pays plus développés, en créant de la richesse et des emplois et en stimulant le progrès humain. Cependant, de nombreuses villes du monde souffrent de problèmes persistants: l'augmentation du nombre de résidents dans les taudis et les quartiers informels, la difficulté d'approvisionnent en services urbains, le changement climatique, l'exclusion et la montée des inégalités et de l'insécurité durant les migrations internationales[2]. Le réchauffement urbain est l'un des défis climatiques en milieu urbain, mesuré dans de nombreuses villes du monde en plus des émissions des gaz à effet de serre (GES)[3]. L'un des facteurs de l'augmentation des températures urbaines est la prédominance du caractère artificiel des villes aux dépenses des fractions vertes naturelles qui s'y trouvent. Un autre aspect de l'urbanisation est le changement structurel économique du pays. La concentration des activités économiques dans les villes

Acknowledgement

Acknowledgement

We acknowledge the Lebanese University's support through which we were granted access to Microsoft Azure cloud computing.

We thank Prof. Haitham Zaraket for his assistance all throughout, Prof. Jocelyne Gerard for sharing the surveyed data of Beirut, and the technical team at the National Center for Remote sensing. Special thanks to Prof. Aram Yeretzian for his major assistance in archetype generation.

This work is part of Alaa Krayem's PhD thesis funded by the CNRS-L. d'énergie des bâtiments (BEM) au niveau de la ville est essentiel pour la gestion de l'approvisionnement en énergie. Ces modèles ont été développés et ont servi comme étant des outils de conception au niveau des bâtiments [9]. Ils sont généralement utilisés à la phase de conception initiale et tout au long du processus de conception pour évaluer diverses options de conception et optimiser la performance globale des systèmes de construction. L'extension de la portée de la modélisation énergétique à l'échelle urbaine permet d'évaluer les interactions entre les bâtiments (ombrage, échange de chaleur, etc.), et d'interactions avec des composantes urbaines telles que l'îlot thermique urbain et le trafic. Les modèles de bilan

List of Abbreviations

AC (a) Building mock-up and Remote sensing images for 3 viewing directions (v,v): nadir (v =0), (30,135) and "specular" (30,45). Calculate the supply air temperature as function of the zone loads, entering air flow rate and temperature, but limit to the applicable temperature limit.

Calculate the supply humidity ratio, but limit to the applicable humidity limit.

Set the zone inlet node conditions to the supply air conditions.

Calculate the unit output and load components.

Outside Surface Heat Balance

The outside surface heat balance is: q asol + q LW R + q conv -q KO = 0 (5.4) where:

q asol = absorbed direct and diffuse solar short wavelength radiation heat flux q LW R = net long wavelength radiation flux exchange at either face of the surface of any generic building element to the current and some of the previous temperatures at both the interior and exterior surfaces as well as some of the previous flux values at the interior surface. To solve the CTFs, a state-space formulation is used. After calculating the coefficient matrices of the state space system, Leveriers algorithm [START_REF] Wiberg | Theory and Problems of State Space and Linear Systems[END_REF] is applied to obtain the CTFs [START_REF] Xiang Qian | Applicability of calculation methods for conduction transfer function of building constructions[END_REF].

Inside Surface Heat Balance

The inside heat balance is:

q LW X + q SW + q LW S + q Ki + q sol + q conv = 0 (5.14)

Where:

q LW X = longwave radiant exchange flux between zone surfaces q SW = shortwave radiant flux to surface from lights q LW S = longwave radiation flux from equipment q Ki = conduction heat flux through the wall q sol = transmitted solar radiation flux q conv = convective heat flux to zone air Before explaining each term in the equation, it is important to d zone internal gains, comprised of convective, radiant and latent gains, from lights, people, and equipment.

Zone internal gains

Lights

The radiant gains from lights are divided into visible, thermal and convective portions based on the light type.

People

By applying a polynomial function, the metabolic heat gain is divided sensible and latent portions.

where M is the metabolic rate (W) and the air temperature (C). predicted energy consumption in district B showed similar results as the EDL data with the strongest correlation being at 40m for all end-uses. The p-value was less than 1% which indicates that the distribution of the energy consumption is not random.

was quite moderate in both cases and was lost at 200m in the first case and at 100m in the second. In district B, a significant correlation was found between residential buildings of the fourth period and mixed buildings from the third period of construction ranging from 0.75 at distances less than 50m to 0.25 at 100m. Furthermore, mixed buildings from the second period of construction had positive correlation with mixed and residential buildings from the third and fourth periods of construction.

Geometrical and non-geometrical model refinements

Many improvements can be thought of to further enhance the accuracy of the presented model and to broaden its applications. The geometrical model can be improved by considering tilted roofs, buildings' zoning into core and perimeter, trees and parks modeling, roads and pedestrian walkways, to list a few. These improvements are important for both energy model and radiative model. The archetypal classification can be improved by adding other key parameters than the building's type and year of construction. However, identification of key parameters should be based on a sensitivity analysis to avoid generation of useless archetypes.

Model functionality

BEEM model presented in chapters 3 and 5 is an automated algorithm to generate the 3D models and the ready-to-run files for energy simulations. However, the inputs and outputs of the model require huge effort to use and process. The outputs on the other hand are stored in excel files that require good programming skills to extract, analyze and visualize results.

In other words, BEEM can be further developed for data management functionalities, and promoted to an effective and feasible platform for users from different backgrounds. It should adapted for compatibility with other formats, such as CityGML so it can be used in other studies. One more barrier is the computational cost of the simulations. This study was accomplished with cloud computing service. Energy simulations are time consuming unless powerful computers are in use. Therefore, attention should be taken in this regard.

Improvement of the energy budget assessment

The radiative budget contributes in the energy budget through surfaces' solar gain and energy savings from daylight. The radiative module in EnergyPlus is simple, while that in DART is more advanced and accurate. Coupling the outputs of DART with EnergyPlus or any dynamic energy modeling software would be useful.

Climatic parameters such as UHI, local wind conditions, evaporation near the sea and released heat from transportation should be taken into account given their influence on the energy and radiative budgets of buildings and therefore on the electricity demand.

Therefore, the study could be extended to consider the effects of

A. Appendix: Energy Policy brief

Issam Fares Institute for Public Policy and International Affairs at the American University of Beirut

The Issam Fares Institute for Public Policy and International Affairs at the American University of Beirut (AUB Policy Institute) is an independent, research-based, policy-oriented institute. Inaugurated in 2006, the Institute aims to harness, develop, and initiate policyrelevant research in the Arab region.

We are committed to expanding and deepening policy-relevant knowledge production in and about the Arab region; and to creating a space for the interdisciplinary exchange of ideas among researchers, civil society and policy-makers.

The Energy Policy and Security Program

The