Climate change is a global phenomenon that strongly affect cities and urban areas. Due to the intensive industrial activities and global population growth leading to more fossil energy demands for the last century, the global warming effect appeared to have been significantly exacerbated. To overcome the issues related to the increase of greenhouse emissions amplifying the global warming, multiple initiatives and engagements have appeared for the last 10 years in order to reduce our global energy demands and reduce the dependency to fossil energy and engage a transition to renewable energy. One way to achieve these objectives is to engage a technological and societal shift in the building industry by reducing energy demands and increasing local energy productions based on renewable energy or, at least, carbon neutral systems. In order to qualify these new types of construction, the concept of positive energy district (PED) has arisen through multiple initiative around the world. This thesis aims to assess the possibility to meet the PED requirements for the new Jakobsgardarna district extension project proposed by Sweco in Borlange, Sweden. This project is based on 144 buildings composed of schools, residentials, retails shop, and offices spread on an 80 ha of land. The Building Energy Modelling (BEM) has been performed on IDA ICE to assess the energy demands and energy production of the entire district following multiple scenarios. These simulations have been performed with either a district heating system or a heat pump as base system. Then, the models have been extended with photovoltaic (PV) panel in multiple configurations in order to find the bes tsolution to meet the PED requirements. First results of the baseline configuration (district heating) shows that the yearly energy demand was around 14,227 MWh which represent almost 69 kWh/m2, mainly dominated up to 75% by the heating demands including domestic hot water (DHW). Moreover, an uncomfortable situation has been met in almost all residential building during summer with temperature reaching up to 31°C. The second configuration considering a heat pump with bore holes in replacement of the district heating shows an overall yearly energy demands of 9,738 MWh representing 47.2kWh/m2 per heated area. This results in a 67% reduction of the energy demands in comparison with the base case. This is due to the high coefficient of performance (COP=4) of the heat pump compared to the district heating system’s (COP=1). In this configuration the heating demands still corresponds to 70% of the overall energy demands. The addition of PV panels compensated the entire electrical needs of the district when combined with district heating and even allowed to reach the positive energy requirements when combined with heat pumps with bore holes. The latter case generates up to 20% of electrical energy in excess of what it produced, even while considering solar panels at a15° of tilt angle in a region where the optimal inclination is defined at 45°. According to the preliminary results obtained in this study, positive energy requirements could be met by the combination of heat pump and PV panels according to our assumptions. This work could then be used to further refine the district design and propose suggestions to improve both the thermal modeling of the district and the design rules for architects and local stakeholders.

With the growing installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy consumption saved, known as negawatt-hours, has also emerged as another commodity that can be exchanged. Users may tune their heating, ventilation, and air conditioning (HVAC) system setpoints to adjust building hourly energy consumption to generate negawatt-hours. Both photovoltaic (PV) energy and negawatt-hours are two major resources of peer-to-peer electricity trading. Blockchain has been touted as an enabler for trustworthy and reliable peer-to-peer trading to facilitate the deployment of such distributed electricity trading through encrypted processes and records. Unfortunately, blockchain cannot fully detect anomalous participant behaviors or malicious inputs to the network. Consequentially, end-user anomaly detection is imperative in enhancing trust in peer-to-peer electricity trading. This dissertation introduces machine learning-based anomaly detection techniques in peer-to-peer PV energy and negawatt-hour trading. This can help predict the next hour's PV energy and negawatt-hours available and flag potential anomalies when submitted bids. As the traditional energy trading market is agnostic to tangible real-world resources, developing, evaluating, and integrating machine learning forecasting-based anomaly detection methods can give users knowledge of reasonable bid offer quantity. Suppose a user intentionally or unintentionally submits extremely high/low bids that do not match their solar panel capability or are not backed by substantial negawatt-hours and PV energy resources. Some anomalies occur because the participant's sensor is suffering from integrity errors. At the same time, some other abnormal offers are maliciously submitted intentionally to benefit attackers themselves from market disruption. In both cases, anomalies should be detected by the algorithm and rejected by the market. Artificial Neural Networks (ANN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN) are compared and studied in PV energy and negawatt-hour forecasting. The semi-supervised anomaly detection framework is explained, and its performance is demonstrated. The threshold values of anomaly detection are determined based on the model trained on historical data. Besides ambient weather information, HVAC setpoint and building occupancy are input parameters to predict building hourly energy consumption in negawatt-hour trading. The building model is trained and managed by negawatt-hour aggregators. CO2 monitoring devices are integrated into the cloud-based smart building platform BEMOSS™ to demonstrate occupancy levels, further improving building load forecasting accuracy in negawatt-hour trading. The relationship between building occupancy and CO2 measurement is analyzed. Finally, experiments based on the Hyperledger platform demonstrate blockchain-based peer-to-peer energy trading and how the platform detects anomalies. / Doctor of Philosophy / The modern power grid is transforming from unidirectional to transactive power systems. Distributed peer-to-peer (P2P) energy trading is becoming more and more popular. Rooftop PV energy and negawatt-hours as two main sources of electricity assets are playing important roles in peer-to-peer energy trading. It enables the building owner to join the electricity market as both energy consumer and producer, also named prosumer. While P2P energy trading participants are usually un-informed and do not know how much energy they can generate during the next hour. Thus, a system is needed to guide the participant to submit a reasonable amount of PV energy or negawatt-hours to be supplied. This dissertation develops a machine learning-based anomaly detection model for an energy trading platform to detect the reasonable PV energy and negawatt-hours available for the next hour's electricity trading market. The anomaly detection performance of this framework is analyzed. The building load forecasting model used in negawatt-hour trading also considers the effect of building occupancy level and HVAC setpoint adjustment. Moreover, the implication of CO2 measurement devices to monitor building occupancy levels is demonstrated. Finally, a simple Hyperledger-based electricity trading platform that enables participants to sell photovoltaic solar energy/ negawatt-hours to other participants is simulated to demonstrate the potential benefits of blockchain.

Energy efficiency of the building sector is a key strategy to achieve national climate goals in Sweden and other European countries. In this thesis, several renovation scenarios for a case study office building in Sweden are analysed and compared based on their energy performance, environmental impacts, and economic costs from a life cycle perspective. As a baseline, the case study building was simulated in IDA ICE and compared with the simulated renovation scenarios. For the Life Cycle Analysis (LCA) and the Life Cycle Costs (LCC), the commercially available software OneClickLCA was used. The renovation scenarios were carried out over three rounds: (i) material type scenarios where five insulation materials (glass wool, rock wool, hemp fiber, Expanded Polystyrene (EPS), and Extruded Polystyrene (XPS)) and two frame materials (wood and steel) are compared; (ii) insulation thickness optimization from economic and environmental performance perspectives (iii) comparison of combination with other typical renovation measures such as changing of windows, improving specific fan power, heat exchanger efficiencies, and lightings. The results show that glass wool gives the most economical and environmental performance, followed by rock wool and EPS. When considering other environmental indicators, hemp fiber presents the best environmental option. However, it is not competitive with traditional insulation materials from an economic perspective. The insulation thickness scenarios show different optimal economic and environmental performance points, giving total energy savings of 5 % and 9,5 %, respectively. When considering other typical energy efficiency measures, the highest impact on the energy performance was found when improving the specific fan power (SFP) and switching to LED lights with total electricity reductions (including user-based electricity consumption) of 4 % and 14 %, respectively. Conclusively, the case study showed how the electricity and heating demand of the studied office building could be reduced, and the environmental and economic consequences of the different energy-efficiency measures.

Along with the increasing use of intermittent electricity sources, such as wind and sun, comes a growing demand for user flexibility. This has paved the way for a new market of services that provide electricity customers with energy saving solutions. These include a variety of techniques ranging from sophisticated control of the customers’ home equipment to information on how to adjust their consumption behavior in order to save energy. This master thesis work contributes further to this field by investigating an additional incentive; predictions of future energy savings related to indoor temperature. Five different machine learning models have been tuned and used to predict monthly heating energy consumption for a given set of homes. The model tuning process and performance evaluation were performed using 10-fold cross validation. The best performing model was then used to predict how much heating energy each individual household could save by decreasing their indoor temperature by 1°C during the heating season. The highest prediction accuracy (of about 78%) is achieved with support vector regression (SVR), closely followed by neural networks (NN). The simpler regression models that have been implemented are, however, not far behind. According to the SVR model, the average household is expected to lower their heating energy consumption by approximately 3% if the indoor temperature is decreased by 1°C.

The content of greenhouse gases in the atmosphere is increasing resulting in climate change and efforts to stop the negative trend need to be intensified. The energy use in the Swedish residential and service sector constitutes 40 % of the total energy use of 378 TWh in the country. Nationally there is a target to reduce the energy use per heated area with 20 % to 2020 and 50 % to 2050. Energy renovation of buildings from the Million Programme is foreseen to be able to contribute to achieving the targets owing to the large building stock and energy efficiency potential. In the master thesis cost optimal energy renovation strategies are investigated for two multi-family buildings in Linkoping built during the Million Programme, one with an unheated attic and one with a heated attic. The thesis is carried out by using life-cycle cost optimisation (LCC-optimisation) by utilising the software OPERA-MILP, developed at Linkoping University. The aim of the thesis is to obtain the energy renovation strategy that is optimal from an LCC-perspective and to investigate the energy reduction and LCC. Optimal energy renovation strategies are also investigated for energy renovation to levels of the Energy Classes of the National Board of Housing, Building and Planning in Sweden and the stricter limits for nearly zero-energy buildings (NZEB) that will likely come into force in 2021. Greenhouse gas emissions and primary energy use are also investigated for the different cases with the purpose of putting energy renovation in relation to climate impact. Local environmental factors are used for district heating while electricity is assigned values based on the Nordic electricity mix and Nordic marginal electricity respectively. The current LCC and annual energy use is 2 945 kSEK and 133 MWh for the building with an unheated attic and 3 511 kSEK and 162 MWh for the building with a heated attic. The result shows that LCC can be reduced by approximately 70 kSEK and 90 kSEK respectively. The optimal solution constitutes of a window change from windows with U=3,0 W/m2°C to windows with U=1,5 W/m2°C and results in a reduction of the energy use by 13 % and 15 % respectively. LCC increases with 240 kSEK for the building with unheated attic and decreases with 18 kSEK for the other building when Energy Class D is reached. Energy Class C is attained through an increase in LCC by 300 – 590 kSEK and Energy Class B through an increase by 1610 – 1800 kSEK. It is not possible to reach Energy Class A or the future requirements for NZEB (55 kWh/m2Aheated) with the energy renovation measures that are implemented in OPERA-MILP. The largest energy reduction that can be attained is approximately 60 %. The most cost optimal insulation measure is additional insulation of the attic floor/pitched roof followed by additional insulation of the ground concrete slab. It was shown to be more cost efficient to change to windows with U=1,5 W/m2°C in combination with additional insulation compared to changing to windows with better energy performance. For greater energy savings additional insulation on the inside of the external wall is applied, while insulation on the outside of the external wall is never cost optimal. To reach Energy Class B installation of HRV is required which gives a large increase in cost. Less extensive energy renovation is needed to reach the energy classes for the building with heated attic compared to the building with unheated attic. The annual use of primary energy in the reference case is 22 MWh for the building with an unheated attic and 26 MWh for the building with a heated attic. The emissions of greenhouse gases are 18 tonnes CO2e and 22 tonnes CO2e per year respectively when the emission factor of the Nordic electricity mix is applied and 20 tonnes CO2e and 25 tonnes CO2e respectively when the Nordic marginal electricity is applied. The yearly primary energy use can be reduced with up to 7 MWh through energy renovation. When the energy renovation leads to an increase in electricity use the primary energy can however increase with up to 12 MWh. The yearly greenhouse gas emissions can be decreased with up to 14 tonnes CO2e. When Nordic marginal electricity is applied to estimate the emissions of greenhouse gases for an energy renovation strategy that leads to an increase in electricity use the result is less beneficial from a climate perspective compared to when Nordic electricity mix is applied.

The field of a research of this diploma thesis is building with controlled internal temperature. The research is focused on main heat fluxes at this type of buildings and how it contributes to the energy consumption of the building. The main objective of the analysis is heat loss caused by heat flux through the building envelope and air exchange at the building. As next it is described heat gain resulting from the operation inside of the building. To obtain the results of the research part is used software on the platform Delphi Pascal, temporarily called SIM_Chlad. The aim of this computer modeling is non-stationary heat fluxes from the mentioned heat sources in the building. The computed heat balance analyzes the energy consumption of the building for a period of one year. The diploma thesis evaluates impacts reflecting local weather conditions, the structural system of the building and the operation in the building. A cooling machinery analysis is not the subject of the research at this diploma thesis.

Les normes de construction pour des bâtiments de plus en plus économes en énergie (BBC) nécessitent une attention particulière. Ces normes reposent sur l’amélioration des performances thermiques de l’enveloppe du bâtiment associé à un effet capacitif des murs augmentant la constante de temps du bâtiment. La prévision de la demande en énergie de bâtiments BBC est plutôt complexe. Ce travail aborde cette question par la mise en œuvre d’intelligence artificielle(IA). Deux approches de mise en œuvre ont été proposées : « all data » et « relevant data ». L’approche « all data » utilise la totalité de la base de données. L’approche « relevant data » consiste à extraire de la base de données un jeu de données représentant le mieux possible les prévisions météorologiques en incluant les phénomènes inertiels. Pour cette extraction, quatre modes de sélection ont été étudiés : le degré jour (HDD), une modification du degré jour (mHDD) et des techniques de reconnaissance de chemin : distance de Fréchet (FD) et déformation temporelle dynamique (DTW). Quatre techniques IA sont mises en œuvre : réseau de neurones (ANN), machine à support de vecteurs (SVM), arbre de décision (DT) et technique de forêt aléatoire (RF). Dans un premier temps, six bâtiments ont été numériquement simulés (de consommation entre 86 kWh/m².an à 25 kWh/m².an) : l’approche « relevant data » reposant sur le couple (DTW, SVM) donne les prévisions avec le moins d’erreur. L’approche « relevant data » (DTW, SVM) sur les mesures du bâtiment de l’Ecole des Mines de Nantes reste performante. / High-energy efficiency building standards (as Low energy building LEB) to improve building consumption have drawn significant attention. Building standards is basically focused on improving thermal performance of envelope and high heat capacity thus creating a higher thermal inertia. However, LEB concept introduces alarge time constant as well as large heat capacity resulting in a slower rate of heat transfer between interior of building and outdoor environment. Therefore, it is challenging to estimate and predict thermal energy demand for such LEBs. This work focuses on artificial intelligence (AI) models to predict energy consumptionof LEBs. We consider two kinds of AI modeling approaches: “all data” and “relevant data”. The “all data” uses all available data and “relevant data” uses a small representative day dataset and addresses the complexity of building non-linear dynamics by introducing past day climatic impacts behavior. This extraction is based on either simple physical understanding: Heating Degree Day (HDD), modified HDD or pattern recognition methods: Frechet Distance and Dynamic Time Warping (DTW). Four AI techniques have been considered: Artificial Neural Network (ANN), Support Vector Machine (SVM), Boosted Ensemble Decision Tree (BEDT) and Random forest (RF). In a first part, numerical simulations for six buildings (heat demand in the range [25 – 85 kWh/m².yr]) have been performed. The approach “relevant data” with (DTW, SVM) shows the best results. Real data of the building “Ecole des Mines de Nantes” proves the approach is still relevant.

Modelling solar radiation data at a high spatiotemporal resolution for an urban environment can inform many different applications related to climate action, such as urban agriculture, forest, building, and renewable energy studies. However, the complexity of urban form, vastness of city-wide coverage, and general dearth of climatological information pose unique challenges doing so. To address some climate action goals related to reducing building emissions in the City of Victoria, British Columbia, Canada, applied geomatics and climatology were used to model solar radiation data suitable for informing renewable energy feasibility studies, including photovoltaic system sizing, costing, carbon offsets, and financial payback. The research presents a comprehensive review of solar radiation attenuates, as well as methods of accounting for them, specifically in urban environments. A novel methodology is derived from the review and integrates existing models, data, and tools – those typically available to a local government. Using Light Detection and Ranging (LiDAR), a solar climatology, Esri’s ArcGIS Solar Analyst tool, and Python scripting, daily insolation (kWh/m2) maps are produced for the city of Victoria. Particular attention is paid to the derivation of daily diffuse fraction from atmospheric clearness indices, as well as LiDAR classification and generation of a Digital Surface Model (DSM). Novel and significant improvements in computation time are realized through parallel processing. Model results exhibit strong correlation with empirical data and support the use of Solar Analyst for urban solar assessments when great care is taken to accurately and consistently represent model inputs and outputs integrated in a methodological approach. / Graduate

Baigiamajame magistro darbe nagrinėjama Lietuvos pastatų energinio naudingumo sertifikavimo metodika pagal STR 2.01.09:2005 „Pastatų energinis naudingumas. Energinio naudingumo sertifikavimas“. Ši metodika lyginama su STR 2.09.04:2002 „Pastato šildymo sistemos galia. Energijos sąnaudos šildymui“ šilumos nuostolių skaičiavimo metodika ir su faktiniu šilumos suvartojimu. Nagrinėjamos suminės energijos sąnaudos ir energinio naudingumo kvalifikacinis rodiklis esant skirtingo aukštingumo pastatams ir jo pokytis diegiant renovacijos priemones. Taip pat atliekama renovacijos priemonių ekonominio efektyvumo analizė. Sudaromas A klasės pastato modelis. Analizuojama energinio naudingumo klasės suteikimo sistema ir pateikiamos rekomendacijos energinio naudingumo sertifikavimui Lietuvoje. / In the master thesis the methodology of building energy performance by The national building regulation STR 2.01.09:2005 „Building Energy performance. Certificate of energy performance” is analysed . The methodology is compared with heat gains calculation methodology in regulation STR 2.09.04:2002 „Capacity of building heating system. Energy input for heating“ and actual heat consumption. The total energy consumption and the energy performance qualifying index is analyzed in the buildings of different height and the difference of index after renovation. The economical efficiency analysis of recommended renovation is analysed. The model of energy performance class is analised and the recomendations for energy performance certification in Lithuania are presented.

Le contexte d’application de cette recherche a été le projet CRIBA. CRIBA vise à industrialiser une solution intégrée de rénovation et d’isolation de grands bâtiments. De ce fait, une part importante de la valeur ajoutée est transférée des chantiers de rénovation vers des usines de fabrications devant être synchronisées avec les chantiers. La planification est l'une des étapes importantes de la gestion de projets. S’adaptant à une organisation, elle vise une réalisation optimale en considérant les facteurs de temps, coût, qualité ainsi que l’affectation efficace des ressources. Cette affectation est d’autant plus complexe lorsqu’un ensemble de projets se partagent les ressources, renouvelables ou non renouvelables. L'objectif global de notre étude est de développer un outil d’aide à la décision pour un décideur visant à planifier plusieurs projets en intégrant l'allocation des ressources renouvelables, et la planification des flux de ressources non-renouvelables vers ces projets. Dans ce cadre, les ressources non renouvelables telles que les machines et la main-d'œuvre ont une disponibilité initiale limitée sur les chantiers. Cependant, nous supposons que des quantités limitées supplémentaires peuvent être achetées. En outre, nous prenons en compte la volonté des coordinateurs des projets pour l’approvisionnement des chantiers en juste à temps (just in time), en particulier pour les ressources peu demandées, encombrantes et à forte valeur. Ceci oblige à étendre le cadre du modèle de la planification des projets en incluant la planification de la chaîne logistique qui approvisionne les ressources non renouvelables des chantiers. Enfin, pour répondre au besoin d’outils décisionnels responsables sur le plan environnemental, le modèle prévoit le transport et le recyclage des déchets des chantiers dans les centres appropriés. Un modèle linéaire mixte du problème est ainsi posé. Puisqu’il rentre dans la classe des modèles d'optimisation NP-durs, une double résolution est proposée. D’abord à l’aide d’un solveur puis une méta-heuristique basée sur un algorithme génétique. De plus, pour faciliter l'utilisation du modèle par des utilisateurs peu familiers avec la recherche opérationnelle, un système d'aide à la décision basé sur une application web a été développé. L’ensemble de ces contributions ont été évaluées sur des jeux de test issus du projet CRIBA. / The application context of the current study is on a CRIBA project. The CRIBA aims to industrialize an integrated solution for the insulation and thermal renovation of building complexes in France. As a result, a significant part of the added value is transferred from the renovation sites to the manufacturing centers, making both synchronized. Planning is one of the important steps in project management. Depending on the different viewpoints of organizations, successful planning for projects can be achieved by performing to optimality within the time, cost, quality factors as well as the efficient assignment of resources. Planning for the allocation of resources becomes more complex when a set of projects is sharing renewable and non-renewable resources. The global objective of the study is to develop a decision-making tool for decision-makers to plan multiple projects by integrating the allocation of the renewable resources and planning the flow of non-renewable resources to the project worksites. In this context, non-renewable resources such as equipment and labor have a limited initial availability at the construction sites. Nevertheless, we assume that additional limited amounts can be added to the projects. In addition, we take into account the interest of the project coordinators in supplying the non-renewable resources in a just-in-time manner to the projects, especially for low-demand resources with a high price. This requires extending the framework of the project planning by including the planning of the supply chain which is responsible. Finally, in order to meet the requirements for environmentally responsible decision-making, the model envisages the transportation and recycling of waste from project sites to appropriate centers. A mixed integer linear model of the problem is proposed. Since it falls within the class of NP-hard optimization models, a double resolution is targeted: first, using a solver and then a metaheuristic based on the genetic algorithm. In addition, in order to facilitate the use of the model by users unfamiliar with operational research, a web-based decision-making support system has been developed. All the contributions are evaluated in a set of case studies from the CRIBA project.