<p dir="ltr">This thesis investigates approaches to classify and anticipate the energy consumption of commercial office buildings using external and performance benchmarking to reduce the energy consumption. External benchmarking in the context of building energy consumption considers the influence of climate zones that significantly impact a building's energy needs. Performance benchmarking recognizes that different types of commercial buildings have distinct energy consumption patterns. Benchmarks are established separately for each building type to provide relevant comparisons.</p><p dir="ltr">The first part of this thesis is about providing a benchmarking baseline for buildings to show their consumption levels. This involves simulating the buildings based on standards and developing a model based on real-time results. Software tools like Open Studio and Energy Plus were utilized to simulate buildings representative of different-sized structures to organize the benchmark energy consumption baseline. These simulations accounted for two opposing climate zones—one cool and humid and one hot and dry. To ensure the authenticity of the simulation, details, which are the building envelope, operational hours, and HVAC systems, were matched with ASHRAE standards.</p><p dir="ltr">Secondly, the neural network machine learning model is needed to predict the consumption of the buildings based on the trend data came out of simulation part, by training a comprehensive set of environmental characteristics, including ambient temperature, relative humidity, solar radiation, wind speed, and the specific HVAC (Heating, Ventilation, and Air Conditioning) load data for both heating and cooling of the building. The model's exceptional accuracy rating of 99.54% attained across all, which comes from the accuracy of training, validation, and test about 99.6%, 99.12%, and 99.42%, respectively, and shows the accuracy of the predicted energy consumption of the building. The validation check test confirms that the achieved accuracy represents the optimal performance of the model. A parametric study is done to show the dependency of energy consumption on the input, including the weather data and size of the building, which comes from the output data of machine learning, revealing the reliability of the trained model. Establishing a Graphic User Interface (GUI) enhances accessibility and interaction for users. In this thesis, we have successfully developed a tool that predicts the energy consumption of office buildings with an impressive accuracy of 99.54%. Our investigation shows that temperature, humidity, solar radiation, wind speed, and the building's size have varying impacts on energy use. Wind speed is the least influential component for low-rise buildings but can have a more substantial effect on high-rise structures.</p>

In the United States, over 40% of the country's total energy consumption is in buildings, most of which are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for energy saving and demand response (DR), but these opportunities are rarely utilized due to lack of effective building energy management systems and automated algorithms that can assist a building to participate in a DR program. Considering the low load factor in US and many other countries, DR can serve as an effective tool to reduce peak demand through demand-side load curtailment. A convenient option for the customer to benefit from a DR program is to use automated DR algorithms within a software that can learn user comfort preferences for the building loads and make automated load curtailment decisions without affecting customer comfort. The objective of this dissertation is to provide such a solution. First, this dissertation contributes to the development of key features of a building energy management open source software platform that enable ease-of-use through plug and play and interoperability of devices in a building, cost-effectiveness through deployment in a low-cost computer, and DR through communication infrastructure between building and utility and among multiple buildings, while ensuring security of the platform. Second, a set of reinforcement learning (RL) based algorithms is proposed for the three main types of loads in a building: heating, ventilation and air conditioning (HVAC) loads, lighting loads and plug loads. In absence of a DR program, these distributed agent-based learning algorithms are designed to learn the user comfort ranges through explorative interaction with the environment and accumulating user feedback, and then operate through policies that favor maximum user benefit in terms of saving energy while ensuring comfort. Third, two sets of DR algorithms are proposed for an incentive-based DR program in a building. A user-defined priority based DR algorithm with smart thermostat control and utilization of distributed energy resources (DER) is proposed for residential buildings. For commercial buildings, a learning-based algorithm is proposed that utilizes the learning from the RL algorithms to use a pre-cooling/pre-heating based load reduction method for HVAC loads and a mixed integer linear programming (MILP) based optimization method for other loads to dynamically maintain total building demand below a demand limit set by the utility during a DR event, while minimizing total user discomfort. A user defined priority based DR algorithm is also proposed for multiple buildings in a community so that they can participate in realizing combined DR objectives. The software solution proposed in this dissertation is expected to encourage increased participation of smaller and medium-sized buildings in demand response and energy saving activities. This will help in alleviating power system stress conditions by employing the untapped DR potential in such buildings. / Ph. D. / In the US and many other countries around the world, the daily peak load experienced is frequently much higher than the daily average load. This low load factor causes inefficient use of generation and transmission resources. Besides inefficient use, the peak load also increases system stress conditions resulting from inadequate generation, transmission line outages or transformer failures. This can create supply-limit conditions which may induce cascaded failures and large area blackouts. To avoid system stress conditions due to increasing demand and to use power system resources more efficiently, demand response (DR) serves as an effective tool to reduce peak demand through demand-side load curtailment. This dissertation focuses on DR applications in buildings. In the United States, buildings consume over 40% of the country’s total energy use. These includes both commercial and residential buildings. Most of the commercial buildings are either small-sized (<5,000 sqft) or medium-sized (5,000-50,000 sqft). These buildings offer excellent opportunities for demand response, which can be implemented through use of building energy management/building automation software. But, building automation software is not yet very popular in small and medium-sized buildings due to lack of low-cost and easy-to-use software solutions. A DR program offered by a utility can be price-based or incentive-based. Price-based DR programs employ dynamic pricing structure to encourage customers to reduce consumption to save bills, whereas incentive-based programs focus on customer commitment to the utility for providing requested load curtailment during peak load situations, in return for monthly or yearly monetary incentives. As most of the peak load reduction potential comes from incentive-based DR programs, this dissertation focuses on an incentive-based DR program. A customer can conveniently participate in such a program by using automated DR algorithms within an energy management software that can control building loads without customer intervention. Providing load curtailment may interfere with customer comfort, and therefore these algorithms must learn customer comfort preferences and consider them while making load shedding decisions. In this dissertation, a software solution is developed for demand response implementation in buildings, which includes contribution to a secure software platform that enables monitoring and control of loads, and automated learning-based algorithms that can learn customer comfort ranges for building loads and use this learning to make load curtailment decisions in an incentive-based DR program, while ensuring customer comfort.

Ce travail propose un modèle thermique dynamique des appareils électriques dans les bâtiments basse consommation. L'objectif de ce travail est d'étudier l'influence des gains thermiques de ces appareils sur le bâtiment. Cette étude insiste sur la nécessité d'établir un modèle thermique dynamique des appareils électriques pour une meilleure gestion de l'énergie du bâtiment et le confort thermique de ses habitants.Comme il existe des interactions thermiques entre le bâtiment et les appareils électriques, sources de chaleur internes au bâtiment, il est nécessaire de modéliser le bâtiment. Le bâtiment basse consommation est modélisé dans un premier temps par un modèle simple reposantl'étude d'une pièce quasi-adiabatique. Ensuite, dans le but d'établir le modèle des appareils électriques, ceux-ci sont classés en quatre catégories selon leurs propriétés thermiques et électriques. A partir de cette classification et du premier principe de la thermodynamique, un modèle physique générique est établi. Le protocole expérimental et la procédure d'identification des paramètres thermiques des appareils sont ensuite présentés. Afin d'analyser la pertinence du modèle générique appliqué à des cas pratiques, plusieurs appareils électriques utilisés fréquemment dans les résidences – un écran, un ordinateur, un réfrigérateur, un radiateur électrique à convection et un micro-onde – sont choisis pour étudier et valider ce modèle ainsi que les protocoles d'expérimentation et d'identification. Enfin, le modèle proposé est intégré dans le modèle d'un bâtiment résidentiel développé et validé par le CSTB. Ce modèle couplé des appareils et du bâtiment est implémenté dans SIMBAD, un outil de simulation du bâtiment. A travers cette simulation, le comportement thermique du bâtiment et la quantité d'énergie nécessaire à son chauffage sur une période hivernale, ainsi que l'inconfort thermique dû aux appareils électriques durant l'été, sont observés.Ce travail fournit des résultats quantitatifs de l'effet thermique de différents appareils électriques caractérisés dans un bâtiment basse consommation et permet d'observer leur dynamique thermique et leurs interactions. Finalement, cette étude apporte une contribution aux études de gestion de l'énergie des bâtiments à basse consommation énergétique et du confort thermique des habitants. / This work proposes a dynamic thermal model of electrical appliances within low energy buildings. It aims to evaluate the influence of thermal gains of these appliances on the buildings and persuades the necessity of dynamic thermal modeling of electrical appliances for the energy management of low energy buildings and the thermal comfort of inhabitants.Since electrical appliances are one of the free internal heat sources of a building, the building which thermally interact with the appliances has to be modeled. Accordingly, a test room which represents a small scale laboratory set-up of a low energy building is first modeled based on the first thermodynamics principle and the thermal-electrical analogy. Then, in order to establish the thermal modeling of electrical appliances, the appliances are classified into four categories from thermal and electrical points of view. After that, a generic physically driven thermal model of the appliances is derived. It is established based also on the first thermodynamics principle. Along with this modeling, the used experimental protocol and the used identification procedure are presented to estimate the thermal parameters of the appliances. In order to analyze the relevance of the proposed generic model applied to practical cases, several electrical appliances which are widely used in residential buildings, namely a monitor, a computer, a refrigerator, a portable electric convection heater, and microwave are chosen to study and validate the proposed generic model and the measurement and identification protocols. Finally, the proposed dynamic thermal model of electrical appliances is integrated into a residential building model which was developed and validated by the French Technical Research Center for Building (CSTB) on a real building. This coupled model of the appliances and the building is implemented in a building energy simulation tool SIMBAD, which is a specific toolbox of Matlab/Simulink®. Through the simulation, thermal behavior and heating energy use of the building are observed during a winter period. In addition, thermal discomfort owing to usages of electrical appliances during a summer period is also studied and quantified.This work therefore provides the quantitative results of thermal effect of differently characterized electrical appliances within a low energy building and leads to observe their thermal dynamics and interactions. Consequently, it permits the energy management of low energy buildings and the thermal comfort of inhabitants in accordance with the usages of electrical appliances.

The energy retrofit of existing buildings has always been a challenging task to accomplish. The example of the Swecohuset building, proves how an integrated approach design between architectural and energetic aspects as well as the use of well-known and efficient technologies are key aspects to achieve the energy-saving goal. This work, in the first part describes the Swecohuset retrofit process, along with the reasons behind the choices which have led to the current result of a reduction by 2/3 of the energy need for space conditioning purposes. Then, in the second part, after a brief focus on the passive aspects which characterize the current energy performance of the building, it is carried out a feasibility analysis of an EWIS (external wall insulation system) by studying its interaction with a complex system as an optimization problem, with the main purpose of understanding the basis of the BPO and explore further building potentialities. / SIRen

Cities play a crucial role in sustainable energy system transformation. Urban energy systems account for 75% of global primary energy use, and 70% of global greenhouse gas (GHG) emissions (IEA, 2021). There is currently a large, untapped potential for reducing both energy demand and emissions by focusing measures on one of the largest consumers of energy: buildings. In North Macedonia, there is an estimated energy savings potential of 57% in the residential sector, and 29% in the public service sector (Apostolska et al., 2020). In the midst of the country’s ambitious targets of decreasing energy demand and GHG reductions, the city of Bitola is in the process of developing an action plan for a sustainable transition of the city. For this purpose, there is a need to investigate the current challenges in the energy system of the city and to evaluate potential future pathways to address these challenges, with a focus on the built environment. In this thesis, a city-scale urban building energy model (UBEM) of the city of Bitola was developed using the software City Energy Analyst (CEA). This involved modeling a total of 14 024 buildings in the city ranging from residential buildings to commercial and industrial facilities. Out of these 14 024 buildings, 10 792 were included in the analysis after excluding abandoned buildings which account for an estimated 25% of the total residential building stock. One Baseline scenario based on the current energy use in the built environment in the city, and four scenarios investigating building retrofit measures and alternative heating solutions were developed for the time period 2023-2040 which were then assessed based on three key performance indicators (KPIs). A 2% implementation rate was used for the measures included in the scenarios, resulting in a total of 34% of the buildings being included in the scenario assessment. The scenarios included in the analysis are Business-as-Usual (BAU), decentralized natural gas boilers (NGB), district heating (DH) and decentralized heat pumps (HP). The KPIs include the total primary energy demand, the total operational CO2 emissions, and the economic performance of the system, measured as a net present value (NPV). All scenarios were also evaluated with and without solar photovoltaic (PV). The results showed the BAU scenario to be the lowest performing scenario for all three KPIs, while the HP scenario showed to be the best-performing scenario regarding the reduction of energy demand and CO2 emissions, with a 99% reduction of CO2 emissions and a 65% lower energy demand than in the baseline year. However, this comes at a relatively high cost compared to the other scenarios. The DH and NGB scenarios performed moderately regarding demand and CO2 emission savings while performing better from an economic standpoint. All scenarios showed a low share of buildings on an individual level having a positive NPV, thus failing to reach a positive total NPV for the entire system. On the other hand, the sensitivity analysis demonstrated how a reduction of the capital expenditure (CAPEX) led to a positive NPV for all scenarios with PV, and for all scenarios except BAU without PV. This indicates that subsidies provided by local or national stakeholders could result in a profitable investment. Two important conclusions can be drawn from the results: firstly, taking any action and implementing either of the HP, NGB and DH scenarios will be more beneficial than taking no action, and secondly, the sustainable development of the city needs to be led by the local municipality, as well as national stakeholders to enable a long-lasting transition. / Städer spelar en avgörande roll för omställningen till hållbara energisystem. Energisystem i städer står för 75% av den globala primära energianvändningen och 70% av de globala växthusgasutsläppen (IEA, 2021). För närvarande finns det en stor, outnyttjad potential för minskning av både energibehov och utsläpp genom att fokusera på åtgärder för en av de största energikonsumenterna: byggnader. I Nordmakedonien uppskattas det finnas potential för energibesparingar på 57% i bostadssektorn och 29% i offentlig sektor (Apostolska et al., 2020). I samband med landets ambitiösa mål om att minska energianvändning och växthusgasutsläpp genomgår staden Bitola för närvarande en process för att utveckla en handlingsplan för en hållbar omställning av staden. För detta ändamål krävs en undersökning av de aktuella utmaningarna i stadens energisystem och utvärdering av potentiella framtida riktningar för att möta dessa utmaningar, med fokus på den bebyggda miljön. I detta examensarbete utvecklades en modell i stadsskala av energianvändningen i byggnader för staden Bitola i Nordmakedonien med hjälp av programvaran City Energy Analyst (CEA). Modellen omfattade totalt 14 024 byggnader, från bostadshus till kommersiella och industriella fastigheter. Då 25% av stadens bostadsbyggnader uppskattas vara övergivna ingick totalt 10 792 byggnader i den slutgiltiga analysen. Ett basscenario som beskriver dagens energianvändning i byggnaderna, och fyra framtida scenarier, som omfattar energieffektiviseringsåtgärder och alternativa värmesystem, utvecklades för tidsperioden 2023-2040. En implementeringstakt om 2% av byggnadsbeståndet, vilket resulterade i att totalt 34% av byggnadsbeståndet inkluderades i scenarioanalysen. De fyra framtida scenarierna som ingick i analysen är Business-as-Usual (BAU), decentraliserade gasvärmepannor (NGB), fjärrvärme (DH) och decentraliserade värmepumpar (HP). Scenarierna bedömdes med hjälp av tre nyckeltal (KPI:er): den totala primärenergianvändningen, de totala operativa CO2 utsläppen och den ekonomiska prestandan, mätt som investeringens nu värde (NPV). Samtliga scenarier utvärderades med och utan implementering av solceller. Resultaten visade att scenariot BAU presterade sämst för alla tre KPI:er, medanHP-scenariot visade sig vara det bäst presterande scenariot för minskning avenergibehovet och CO2-utsläppen, med 99% minskning av CO2-utsläpp och 65%lägre energianvändning jämfört med basscenariot. Dock är detta förknippat medrelativt höga kostnader jämfört med de andra scenarierna. DH- och NGB-scenariotpresterade måttligt gällande besparing av energibehov och CO2-utsläpp, samtidigt somde presterade bättre ur ett ekonomiskt perspektiv. Alla scenarier resulterade i en lågandel av byggnader på individuell nivå med ett positivt NPV, vilket innebär att demisslyckas med att nå ett positivt totalt NPV för hela systemet. Å andra sidan visadekänslighetsanalysen att en minskning av investerings kostnaderna (CAPEX) ledde tillett positivt NPV för alla scenarier med solceller, och för alla scenarier utom BAU utan solceller. Detta indikerar att subventioner från lokala och nationella aktörer kan leda till en lönsam investering. Två viktiga slutsatser kan dras från dessa resultat: för det första, att vidta åtgärder och implementera något av HP-, NGB- eller DH-scenariot är mer fördelaktigt än att inte vidta några åtgärder, och för det andra, behöver den hållbara utvecklingen av staden ledas av den lokala kommunen samt nationella aktörer för att möjliggöra en långvarig omställning.

Energy and environment have become hot topics in the world. The building sector accounts for a high proportion of energy consumption, with over one-third of energy use globally. A variety of optimization methods have been proposed for building energy management, which are mainly divided into two types: model-based and model-free. Model Predictive Control is a model-based method but is not widely adopted by the building industry as it requires too much expertise and time to develop a model. Model-free Deep Reinforcement Learning(DRL) has successful applications in game-playing and robotics control. Therefore, we explored the effectiveness of the DRL algorithms applied to building control and investigated which DRL algorithm performs best. Three DRL algorithms were implemented, namely, Deep Deterministic Policy Gradient(DDPG), Double Deep Q learning(DDQN) and Soft Actor Critic(SAC). We used the building optimization testing (BOPTEST) framework, a standardized virtual testbed, to test the DRL algorithms. The performance is evaluated by two Key Performance Indicators(KPIs): thermal discomfort and operational cost. The results show that the DDPG agent performs best, and outperforms the baseline with the saving of thermal discomfort by 91.5% and 18.3%, and the saving of the operational cost by 11.0% and 14.6% during the peak and typical heating periods, respectively. DDQN and SAC agents do not show a clear advantage of performance over the baseline. This research highlights the excellent control performance of the DDPG agent, suggesting that the application of DRL in building control can achieve a better performance than the conventional control method. / Energi och miljö blir heta ämnen i världen. Byggsektorn står för en hög andel av energiförbrukningen, med över en tredjedel av energianvändningen globalt. En mängd olika optimeringsmetoder har föreslagits för Building Energy Management, vilka huvudsakligen är uppdelade i två typer: modellbaserade och modellfria. Model Predictive Control är en modellbaserad metod men är inte allmänt antagen av byggbranschen eftersom det kräver för mycket expertis och tid för att utveckla en modell. Modellfri Deep Reinforcement Learning (DRL) har framgångsrika tillämpningar inom spel och robotstyrning. Därför undersökte vi effektiviteten av DRL-algoritmerna som tillämpas på byggnadskontroll och undersökte vilken DRL-algoritm som presterar bäst. Tre DRL-algoritmer implementerades, nämligen Deep Deterministic Policy Gradient (DDPG), Double Deep Q Learning (DDQN) och Soft Actor Critic (SAC). Vi använde ramverket Building Optimization Testing (BOPTEST), en standardiserad virtuell testbädd, för att testa DRL-algoritmerna. Prestandan utvärderas av två Key Performance Indicators (KPIs): termiskt obehag och driftskostnad. Resultaten visar att DDPG-medlet presterar bäst och överträffar baslinjen med besparingen av termiskt obehag med 91.5% och 18.3%, och besparingen av driftskostnaden med 11.0% och 14.6% under topp och typisk uppvärmning perioder, respektive. DDQN- och SAC-agenter visar inte en klar fördel i prestanda jämfört med baslinjen. Denna forskning belyser DDPG-medlets utmärkta prestanda, vilket tyder på att tillämpningen av DRL i byggnadskontroll kan uppnå bättre prestanda än den konventionella metoden.

A very economical way of reducing the operational energy consumed by large commercial buildings such as an airport terminal is the automatic control of its active energy systems. Such control can adjust the indoor environment systems setpoints to satisfy comfort during occupancy or when unoccupied, initiate energy conservation setpoints and if necessary, shut down part of the building systems. Adjusting energy control setpoints manually in large commercial buildings can be a nightmare for facility managers. Incidentally for such buildings, occupancy based control strategies are not achieved through the use of conventional controllers alone. This research, therefore, investigated the potential of using a high-level control system in airport terminal building. The study presents the evolution of a novel fuzzy rule-based supervisory controller, which intelligently establishes comfort setpoints based on flow of passenger through the airport as well as variable external environmental conditions. The inputs to the supervisory controller include: the time schedule of the arriving and departing passenger planes; the expected number of passengers; zone daylight illuminance levels; and external temperature. The outputs from the supervisory controller are the low-level controllers internal setpoint profile for thermal comfort, visual comfort and indoor air quality. Specifically, this thesis makes contribution to knowledge in the following ways: It utilised artificial intelligence to develop a novel fuzzy rule-based, energy-saving supervisory controller that is able to establish acceptable indoor environmental quality for airport terminals based on occupancy schedules and ambient conditions. It presents a unique methodology of designing a supervisory controller using expert knowledge of an airport s indoor environment systems through MATLAB/Simulink platform with the controller s performance evaluated in both MATLAB and EnergyPlus simulation engine. Using energy conservation strategies (setbacks and switch-offs), the pro-posed supervisory control system was shown to be capable of reducing the energy consumed in the Manchester Airport terminal building by up to 40-50% in winter and by 21-27% in summer. It demonstrates that if a 45 minutes passenger processing time is aimed for instead of the 60 minutes standard time suggested by ICAO, energy consumption is significantly reduced (with less carbon emission) in winter particularly. The potential of the fuzzy rule-based supervisory controller to optimise comfort with minimal energy based on variation in occupancy and external conditions was demonstrated through this research. The systematic approach adopted, including the use of artificial intelligence to design supervisory controllers, can be extended to other large buildings which have variable but predictable occupancy patterns.

There is much potential to decrease energy consumption in the U.S. by encouraging compact, centralized development. Although many studies have examined the extent to which built environment and demographic factors are related to household energy use, few have considered both building and transportation energy together. We hypothesized that residents living further from city centers, or urban cores, consume more energy for both purposes than their inner city counterparts, resulting in a direct relationship between building and transportation energy usage. This hypothesis was tested with two case studies. The first focused on New York City. Annual building energy per unit of parcels, or tax lots, containing large multi-family structures was compared to the daily transportation energy use per household of traffic analysis zones (TAZs) (estimated with a regional travel demand model). Transportation energy showed a strong spatial pattern, with distance to urban core explaining 63% of variation in consumption. Building energy use was randomly distributed, resulting in a weak negative correlation with transportation energy. However, both correlation with distance to urban core and transportation energy became significant and positive when portion of detached single-family units for TAZs was used as a proxy for building energy. Structural equation models (SEMs) revealed a direct relationship between log lot depth and both uses of energy, and inverse relationship between portion of attached housing units and transportation energy. This supports the notion that sprawling development increases both the building and transportation energy consumption of households. For the second analysis, annual building and automobile energy use per household were estimated for block groups across the 50 most populous U.S. metropolitan regions with Esri Consumer Expenditure Data. Both forms of energy consumption per household were lowest in inner cities and increased at greater distances from urban cores. Although there may be some error in estimates from modeled expenditure data, characteristics associated with lower energy use, such as portion of attached housing units and commuters that utilize transit or pedestrian modes, were negatively correlated with distance to urban core. Overall, this work suggests there are spatial patterns to household energy consumption, with households further from urban cores using more building and transportation energy. There is the greatest gain in efficiency to be had by suburban residents.

Les modèles énergétiques des bâtiments à l’échelle du quartier sont généralement simplifiés pour faire face au manque de données et pour réduire le coût de calcul. Cependant, l’impact de ces simplifications sur la validité des modèles n’est pas systématiquement analysée, en particulier lorsqu’on s’intéresse à la courbe de charge. Pour combler ce manque, une méthodologie permettant de quantifier la validité des simplifications, notamment vis-à-vis de la courbe de charge, est proposée. Cette méthodologie est appliquée aux simplifications couramment utilisée pour les modèles thermiques d’enveloppe de bâtiments grâce à une plateforme numérique développée dans le cadre de cette thèse. Cette plateforme permet de générer et simuler automatiquement des modèles énergétiques de bâtiments, avec différents niveaux de détails, à partir de données issues de systèmes d’information géographique. La parallélisation des simulations énergétiques des bâtiments est utilisée à l’échelle du quartier, afin de tirer avantage de la structure du modèle global et de réduire les temps de calculs. La définition d’indicateurs spécifiques selon l’objectif de simulation apparait clairement comme l’étape essentielle lorsque l’on s’intéresse à la courbe de charge. Les résultats indiquent que la puissance est plus sensible aux simplifications que la consommation annuelle d’énergie. Les différents effets induits sont quantifiés et analysés physiquement. La capacité de l’échelle du quartier à atténuer les impacts des simplifications et d’intégrer les données statistiques est démontrée. La quantification des impacts des simplifications permet de guider l’adaptation des modèles vis-à-vis des objectifs de simulation et vis-à-vis des contraintes techniques. Cette contribution a pour objectif d’améliorer la performance des simulations énergétiques à l’échelle de la ville, et de favoriser leur développement, afin de répondre aux enjeux futurs. / District-scale building energy models are generally simplified to cope with a lack of data and to reduce computational cost. However, the impacts of these simplifications on model accuracy are not systematically studied, particularly when considering power demand. The present manuscript introduces a methodology to determine the suitability of any simplifications, notably those at the district scale, and considering the power demand. This methodology was applied to usual simplifications of the building envelope model thanks to a specific platform developed in the frame of this thesis. This platform enables automatically generating and simulating building energy models with different modelling levels of detail from geographical information systems. The parallelisation of the building energy simulations was notably implemented at the district scale in order to benefit from the model structure and to efficiently reduce the computational duration. The definition of indicators related to specific simulation objectives appears to be a necessary step when focusing on power demand. The results show a higher sensitivity to simplifications of the power demand than the annual energy consumption. These effects are quantified and physically analysed. The district-scale ability to attenuate the impacts of simplifications and to integrate statistical sources of data were demonstrated. The resulting quantification of the impacts of the simplifications made it possible to guide the adaptations of models to the simulation objectives and to the technical constraints. Such contribution aims to increase the efficiency and to favour the development of city-scale energy simulations, which are particularly needed to cope with future challenges.

Being located in subtropical area, the weather in Taiwan is constantly hot and humid which imposes huge cooling load on buildings. Especially, the economic booms in Taiwan further boosted power demand, and worsened the power shortage situation. Dr. H.T. Lin and Dr. K.H. Yang had conducted systematic research since mid-1980s, which constructed a solid ground in this field in Taiwan. Among these results, the ENVLOAD index has become legal binding since 1997 while the PACS index is now under investigation. However, it is in short of analysis and full-scale experimental investigation on energy use of hotels in Taiwan. Therefore, the establishment of the EUI and DUI indexes in Taiwan is the goal of this study. A simplified calculation method has been established in analyzing the energy use and demand use of hotels in Taiwan, by normalizing experimental data from full-scale tests. The result can be drawn accurately based on a few terms, which are available from daily building operations such as occupancy, and is thus practically straightforward and easy to use. In addition, the accuracy was validated by experiments performed and data collected through information technology with Internet access in 4 different forms, which yielded successful results. It is anticipated that the calculation methodology developed in this study on EUI and DUI, and the experimental validation would provide a foundation for the establishment of hotel building energy codes in Taiwan in the future.