The European Union (EU) has expressed determination of reducing its energy consumption and the EU’s 2010 Energy Performance of Buildings Directive states that all new buildings must be nearly zero energy by the end of the year 2020. Dynamic or “smart” windows have been shown to be able to reduce HVAC energy consumption, lighting energy and peek cooling loads in hot climates in the US but it is difficult to find any work concerned with colder climates. This study is intended to capture the performance of dynamic windows in a variety of European climates to explore potential contributions to reaching the EU’s energy goals. The building energy simulations of this study have been conducted in IDA ICE for an office section with a large window. Three model variants are compared: without a window shading, with an external window blind and with a dynamic window. This comparison is repeated for six different locations; Kiruna, Reykjavik, Stockholm, Copenhagen, Paris and Madrid. The results of this study show that the dynamic window can reduce the total consumed energy for lighting, heating and cooling in the range of 10%-30% more than the external blind, depending on location. The reduction is 50%-75% when compared to the unshaded window. This level of performance can move Europe a step closer to zero energy buildings.

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This thesis explores the building energy performance of the domestic sector in the city of Stockholm based on the building energy declaration database. The aims of this master thesis are to analyze the big data sets of around 20,000 buildings in Stockholm region, explore the correlation between building energy performance and different internal and external affecting factors on building energy consumption, such as building energy systems, building vintages and etc. By using clustering method, buildings with different energy consumptions can be easily identified. Thereafter, energy saving potential is estimated by setting step-by-step target, while feasible energy saving solutions can also be proposed in order to drive building energy performance at city level. A brief introduction of several key concepts, energy consumption in buildings, building energy declaration and big data, serves as the background information, which helps to clarify the necessity of conducting this master thesis. The methods used in this thesis include data processing, descriptive analysis, regression analysis, clustering analysis and energy saving potential analysis. The provided building energy declaration data is firstly processed in MS Excel then reorganized in MS Access. As for the data analysis process, IBM SPSS is further introduced for the descriptive analysis and graphical representation. By defining different energy performance indicators, the descriptive analysis presents the energy consumption and composition for different building classifications. The results also give the application details of different ventilation systems in different building types. Thereafter, the correlation between building energy performance and five different independent variables is analyzed by using a linear regression model. Clustering analysis is further performed on studied buildings for the purpose of targeting low energy efficiency groups, and the buildings with various energy consumptions are well identified and grouped based on their energy performance. It proves that clustering method is quite useful in the big data analysis, however some parameters in the process of clustering needs to be further adjusted in order to achieve more satisfied results. Energy saving potential for the studied buildings is calculated as well. The conclusion shows that the maximal potential for energy savings in the studied buildings is estimated at 43% (2.35 TWh) for residential buildings and 54% (1.68 TWh) for non-residential premises, and the saving potential is calculated for different building categories and different clusters as well.

With an increasing awareness of energy consumption and CO 2emission in the population, several initiatives to reduce CO2emissions have been presented all around the world. The main part of these initiatives is a reduction of the energy consumption for existing buildings, while the others concern the building of eco-districts with low-energy infrastructures and even zero-energy infrastructures. In this idea of reducing the energy consumption and of developing new clean areas, this master thesis will deal with the high energy quality services for new urban districts. In the scope of this master thesis project, the new concept of sustainable cities and of clusters of buildings will be approached in order to clearly understand the future challenges that the world’s population is going to face during this century. Indeed, due to the current alarming environmental crisis, the need to reduce human impacts on the environment is growing more and more and is becoming inescapable. We will present a way to react to the current situation and to counteract it thanks to new clean technologies and to new analysis approaches, like the exergy concept. Through this report, we are going to analyze the concepts of sustainable cities and clusters of buildings as systems, and focus on their energy aspects in order to set indoor climate parameters and energy supply parameters to ensure high energy quality services supplies to high performance buildings. Thanks to the approach of the exergy concept, passive and active systems such as nocturnal ventilation or floor heating and cooling systems have been highlighted in order to realize the ‘energy saving’ opportunities that our close environment offers. This work will be summarized in a methodology that will present a way to optimize the energy use of all services aspects in a building and the environmental friendly characteristics of the energy resources mix, which will supply the buildings’ low energy demands.

Cooling demand in Europe is predicted to grow 25-50% between 2020-2050. Meanwhile, the EU aims to lower the greenhouse gas emissions from its building stock by 60%. Therefore, it is essential to find solutions that can meet the growing cooling demand with less energy and integrate renewable energy sources. The goal of this thesis is to technically evaluatehigh-temperature cooling systems and their contributions to the targets mentioned above. The study was conducted using advanced building energy simulations and developing analytical methods. IDA Indoor Climate and Energy 4.8was selected as the simulation tool. The study is a part of GEOFIT project, and the used building physics and measurement data were based on one of the project pilots. The selected building is a representative office building that is a part of a three-building school complex. The building is located in Sant Cugat near Barcelona, in an area which has a typical Mediterranean climate. The simulated building model was validated using onsite measurement data. Two types of high-temperature cooling systems were studied: a radiant cooling system and an all-air cooling system. For the study, the systems were designed to create equal thermal comfort conditions, so that their energy and exergy use could be compared. In the studied case, the radiant cooling system was found to use 40% less energy and consume 85% less exergy than a conventional low-temperature all-air cooling system. It was also found that a passive geothermal radiant cooling system requires 66% less electricity for pumps and fans than a passive geothermal all-air cooling system. The results demonstrate that radiant cooling systems have the potential to lower exergy consumption in cooling applications thanks to the high supply temperature and that using water as a heat transfer medium is more efficient than using air. / Kylningsefterfrågan i Europa förutses att växa 25-50% mellan 2020-2050. Samtidigt strävar EU efter att sänka utsläppen av växthusgaser från sina byggnader med 60%. Det är därför viktigt att hitta lösningar som kan tillgodose det växande kylbehovet med mindre energi och att integrera förnybara energikällor. Målet med denna avhandling är en teknisk evaluering av högtemperatur-kylsystem och deras bidrag till ovan nämnda mål. Studien genomfördes med avancerade simuleringar av byggnadsenergi och utvecklade analytiska metoder. IDA Indoor Climate and Energy 4.8 valdes som simuleringsverktyg. Studien är en del av GEOFIT-projektet och den använda byggnadsfysiken och mätdata baserades på en av projektpiloterna. Den valda byggnaden är en representativ kontorsbyggnad som ingår i ett skolbyggnad med tre byggnader. Byggnaden ligger i Sant Cugat nära Barcelona, i ett område som har ett typiskt medelhavsklimat. Den simulerade byggnadsmodellen validerades med hjälp av mätdata på plats. Två typer av högtemperatur-kylsystem studerades: ett strålande kylsystem och ett luftkylsystem. För studien designades systemen för att skapa lika termiska komfortförhållanden, så att deras energi och exergianvändning kunde jämföras. I det studerade fallet visade sig att det strålande kylsystemet använde 40% mindre energi och förbrukade 85% mindre exergi än ett konventionellt högtemperatur-kylsystem med låg temperatur. Man fann också att ett passivt geotermiskt strålkylsystem kräver 66% mindre el för pumpar och fläktar än ett passivt geotermiskt luftkylsystem. Resultaten visar att strålningskylsystem har potential att sänka exergiförbrukningen i kylapplikationer tack vare den höga framledningstemperaturen och att användning av vatten som värmeöverföringsmedium är effektivare än att använda luft. / <p>QC 210204</p>

<p>The residential sector is responsible for approximately 21% of the total energy use in the U.S. As a result, there have been various programs and studies aiming to reduce energy consumption and utility burden on individual households. Among various energy efficiency strategies, behavior-based approaches have received considerable attention because they significantly affect operational energy consumption without requiring building upgrades. For example, up to 30% of heating and cooling energy savings can be achieved by having an efficient temperature setpoint schedule. Such approaches can be particularly beneficial for multi-family residential buildings because 88% of their residents are renters paying their own utility bills without being allowed to upgrade their housing unit.</p> <p>In this context, eco-feedback has emerged as an approach to motivate residents to reduce energy use by providing information (feedback) on human behavior and environmental impact. This research has gained significant attention with the development of new smart home technology such as smart thermostats and home energy management systems. Research on the design of effective eco-feedback focuses on how to motivate residents to change their behavior by identifying and notifying implementable actions in a timely manner via energy analytics such as energy prediction models, energy disaggregation, etc.</p> <p>However, unit-level energy analytics pose significant challenges in multi-family residential buildings tasks due to the inter-unit heat transfer, unobserved variables (e.g., infiltration, human body heat gain, etc.), and limited data availability from the existing infrastructure (i.e., smart thermostats and smart meters). Furthermore, real-time model inference can facilitate up-to-date eco-feedback without a whole year of data to train models. To tackle the aforementioned challenges, three new modeling approaches for energy analytics have been proposed in this Thesis is developed based on the data collected from WiFi-enabled smart thermostats and power meters in a multi-family residential building in IN, U.S.</p> <p>First, this Thesis presents a unit-level data-driven modeling approach to normalize heating and cooling (HC) energy usage in multi-family residential buildings. The proposed modeling approach provides normalized groups of units that have similar building characteristics to provide the relative evaluation of energy-related behaviors. The physics-informed approach begins from a heat balance equation to derive a linear regression model, and a Bayesian mixture model is used to identify normalized groups in consideration of the inter-unit heat transfer and unobserved variables. The probabilistic approach incorporates unit- and season-specific prior information and sequential Bayesian updating of model parameters when new data is available. The model finds distinct normalized HC energy use groups in different seasons and provides more accurate rankings compared to the case without normalization.</p> <p>Second, this Thesis presents a real-time modeling approach to predict the HC energy consumption of individual units in a multi-family residential building. The model has a state-space structure to capture the building thermal dynamics, includes the setpoint schedule as an input, and incorporates real-time state filtering and parameter learning to consider uncertainties from unobserved boundary conditions (e.g., temperatures of adjacent spaces) and unobserved disturbances (i.e., window opening, infiltration, etc.). Through this real-time form, the model does not need to be re-trained for different seasons. The results show that the median power prediction of the model deviates less than 3.1% from measurements while the model learns seasonal parameters such as the cooling efficiency coefficient through sequential Bayesian update.</p> Finally, this Thesis presents a scalable and practical HC energy disaggregation model that is designed to be developed using data from smart meters and smart thermostats available in current advanced metering infrastructure (AMI) in typical residential houses without additional sensors. The model incorporates sequential Bayesian update whenever a new operation type is observed to learn seasonal parameters without long-term data for training. Also, it allows modeling the skewed characteristics of HC and non-HC power data. The results show that the model successfully predicts disaggregated HC power from 15-min interval data, and it shows less than 12% of error in weekly HC energy consumption. Finally, the model is able to learn seasonal parameters via sequential Bayesian update and gives good prediction results in different seasons.

About 12% of the greenhouse gas emissions and 40% of the total energy use in the EU derive from the buildings. User behavior, construction, and HVAC systems has a significant impact on a building’s energy use. If a building is to be energy-efficient it is important to understand how all these parameters are connected. This study is motivated by the need to decrease the energy use in buildings to reach the goals of energy use and greenhouse gas emissions.  In this thesis, measurements of indoor climate and electricity use, together with time diaries was used to create input data for an energy simulation model of a retirement home. A parametric study was conducted to simulate how energy demand was affected by changes in five different parameters in an office building. Also, two different energy-efficiency indicators were used to see how indicators can affect the perceived energy-efficiency of buildings. High amount of airing and low electricity use had the most impact on the heating demand in the retirement home, and electricity use had the highest impact on the total energy demand in the office building. The model of the retirement home using data gathered on-site had 24% higher energy use than the model using standard user input data. In the office building, total energy demand for heating and cooling could be lowered with 12-31% by lowering the electricity use with 30% compared to standard user input data. For office buildings the most important thing to lower total energy demand seems to be lowering the electricity use. Using today’s standard user input data does not correspond well to using on-site gathered data in a retirement home and it is therefore important to develop the standard user input data further. The indicator kWh/m2, seems to promote buildings with low occupancy. This could lead to buildings being utilized in an in-efficient way. The indicator kWh/m2 should either be replaced or combined with an indicator that takes occupancy into consideration. / Runt 12% av utsläppen av växthusgaser och 40% av den totala energianvändningen i EU kommer från byggnader. Brukarbeteende, konstruktion och HVAC-system har signifikant påverkan på en byggnads energianvändning. Om en byggnad ska bli så energieffektiv som möjligt är det viktigt att förstå hur dessa parametrar hör ihop. Denna studie motiveras av behovet att minska energianvändning i byggnader för att nå målen för energianvändning och utsläpp av växthusgaser.  I denna avhandling användes mätningar av inomhusklimat och elanvändning, tillsammans med tidsdagböcker, för att skapa indata till en energisimuleringsmodell av ett ålderdomshem. En parameterstudie genomfördes för att simulera hur energibehovet påverkades av ändringar i fem olika parametrar i en kontorsbyggnad. Två olika indikatorer för energieffektivitet användes också, för att se hur olika indikatorer påverkar hur en byggnads energieffektivitet uppfattas. Hög grad av vädring och låg elanvändning hade störst påverkan av energibehovet i ålderdomshemmet, och i kontorsbyggnaden påverkades det totala energibehovet mest av elanvändningen. Modellen av ålderdomshemmet där data insamlad på plats användes hade 24% högre värmebehov än modellen som använde standardiserade brukarindata. Det totala energibehovet för värme och kyla i kontorsbyggnaden kunde sänkas med 12-31% genom att sänka elanvändningen med 30% jämfört med standardiserad brukarindata. Det viktigaste för att få ner det totala energibehovet i kontorsbyggnader verkar vara att sänka elanvändningen. Att använda dagens standardvärden för brukarindata överensstämmer inte väl med att använda data insamlad på plats för ett ålderdomshem. Det är därför viktigt att vidareutveckla standardiserad brukarindata. Indikatorn kWh/m2 verkar främja byggnader med låg beläggning. Detta skulle kunna leda till att byggnader utnyttjas på ett ineffektivt sätt. Indikatorn kWh/m2 skulle därför behöva ersättas eller kombineras med en indikator som även tar byggnadens beläggning i beaktande.

The process of energy consumption and monitoring of a buildingis time-consuming. Therefore, an feasible approach for using trans-fer learning is presented to decrease the necessary time to extract re-quired large dataset. The technique applies a bidirectional long shortterm memory recurrent neural network using sequence to sequenceprediction. The idea involves a training phase that extracts informa-tion and patterns of a building that is presented with a reasonablysized dataset. The validation phase uses a dataset that is not sufficientin size. This dataset was acquired through a related paper, the resultscan therefore be validated accordingly. The conducted experimentsinclude four cases that involve different strategies in training and val-idation phases and percentages of fine-tuning. Our proposed modelgenerated better scores in terms of prediction performance comparedto the related paper.

L’intérêt croissant pour les matériaux biosourcés dans le domaine de la construction se heurte à des difficultés quant à la simulation de leur comportement hygrothermique. En particulier, les matériaux isolants tels que les panneaux de fibres concentrent toutes les difficultés car ils sont peu conducteurs thermiquement, très hygroscopiques et très diffusifs à la vapeur d’eau. Conséquemment, en régime transitoire le couplage chaleur masse est exacerbé et les phases de l’eau ne sont pas à l’équilibre localement.Afin de mettre en évidence ce second phénomène, un nouveau dispositif expérimental a été développé. Il permet de soumettre un échantillon de quelques centimètres d’épaisseur à une perturbation de l’humidité relative sur sa face avant puis de mesurer simultanément l’évolution de l’humidité relative sur sa face arrière et de sa masse. En situation de non-équilibre, il existe un déphasage entre ces deux grandeurs que la formulation de transferts couplés classique n’arrive pas à prédire. Afin d’obtenir une prédiction correcte, une nouvelle formulation a été utilisée. Celle-ci se base sur l’emploi de fonctions mémoires caractérisant la diffusion microscopique. De manière à démontrer la capacité prédictive de la nouvelle formulation, ces fonctions ont été déterminées avec des essais gravimétriques réalisés sur de très petits échantillons à l’aide d’une balance à suspension magnétique. En parallèle, une analyse rigoureuse du couplage chaleur masse dans ces matériaux a permis de souligner l’impact sur leur caractérisation de différents paramètres macroscopiques.L’utilisation de la nouvelle formulation alimentée par les fonctions mémoires et les différents paramètres macroscopiques permet une excellente prédiction de l’humidité relative et de la masse. Cette nouvelle formulation validée expérimentalement est désormais utilisable dans des logiciels de simulation énergétique du bâtiment. / The growing interest in biosourced materials in the construction sector is confronted with difficulties in simulating their hygrothermal behavior. Insulating materials such as fiberboard concentrate all the difficulties because they are not very thermally conductive, very hygroscopic and very diffusive to water vapor. Consequently, in transient state, heat and mass coupling is exacerbated, and the phases of water are not in equilibrium locally.In order to highlight this second phenomenon, a new experimental device has been developed. It allows to subject a sample a few centimeters thick to a disturbance of relative humidity on its front face and then to simultaneously measure the evolution of relative humidity on its back face and its mass. In a situation of non-equilibrium, there is a phase shift between these two quantities that the classic coupled transfer formulation cannot predict. In order to obtain a correct prediction, a new formulation was used. It is based on the use of memory functions characterizing microscopic diffusion. In order to demonstrate the predictive capacity of the new formulation, these functions have been determined with gravimetric tests performed on very small samples using a magnetic suspension balance. In parallel, a rigorous analysis of the heat and mass coupling in these materials made it possible to highlight the impact of different macroscopic parameters on their characterization.The use of the new formulation fed by the identified memory functions and the various macroscopic parameters allows an excellent prediction of relative humidity and mass. This new formulation, experimentally validated, can now be used in energy simulation of the building.

abstract: Thermal extremes are responsible for more than 90% of all weather-related deaths in the United States, with heat alone accounting for an annual death toll of 618. With the combination of global warming and urban expansion, cities are becoming hotter and the threat to the well-being of citizens in urban areas is growing. Because people in modern societies (and in particular, vulnerable groups such as the elderly) spend most of their time inside their home, indoor exposure to heat is the underlying cause in a considerable fraction of heat-related morbidity and mortality. Notably, this can be observed in many US cities despite the high prevalence of mechanical air conditioning in the building stock. Therefore, part of the effort to reducing the overall vulnerability of urban populations to heat needs to be dedicated to understanding indoor exposure, its underlying behavioral and physical mechanisms, health outcomes, and possible mitigation strategies. This dissertation is an effort to advance the knowledge in these areas. The cities of Houston, TX, Phoenix, AZ, and Los Angeles, CA, are used as test beds to assess exposure and vulnerability to indoor heat among people 65 and older. Measurements and validated whole-building simulations were used in conjunction with heat-vulnerability surveys and epidemiological modelling (of collaborators) to (1) understand how building characteristics and practices govern indoor exposure to heat among the elderly; (2) evaluate mechanical air conditioning as a reliable protective factor against indoor exposure to heat; and (3) identify potential impacts from the evolving building stock and a warming urban climate. The results show strong associations between indoor heat exposure and certain health outcomes and highlight the vulnerability of elderly populations to heat despite the prevalence of air conditioning systems. Given the current construction practices and urban warming trends, this vulnerability will continue to grow. Therefore, policies promoting climate adaptive buildings features, as well as better access to reliable and affordable AC are needed. In addition, this research draws attention to the significant potential health consequences of large-scale power outages and proposes the implementation of passive survivability in regulations as one important preventative action. / Dissertation/Thesis / Doctoral Dissertation Engineering 2019