While global efforts are made to reduce the emission of greenhouse gases and move towards a more sustainable society, the global energy demand is continuing to increase. Building energy consumption represents 20-40% of the world's total energy use, and Heating, Ventilation, and Air Conditioning (HVAC) answer for around 50% of this amount. Only a small share of the European Union's building stock is considered to be energy efficient, and many of these buildings will continue to operate until the year 2050 and on-wards. The main objective of this thesis was to benchmark the economic and environmental implications of increasing building heating efficiency. To answer the framed research questions, an experimental study was carried out. In the study, a machine learning based solution was constructed and then implemented in a multi-tenant building for 24 days. Using an Artificial Neural Network a new heating curve was predicted, based on historical data from the building. The post-experimental data was then analyzed using STATA as statistical software tool. The results show that the new heating curve was able to reduce the heating system supply temperature by 1.9°C, with a decrease in average indoor temperature of 0.097°C. The decrease in supply temperature resulted in a reduction of energy expenditure by approximately 10%. Using the new building specific heating curve, yearly cost reductions of almost 11,700SEK could be achieved. Furthermore, the increased efficiency was able to reduce CO2 emissions by 127,5kg yearly. This results helps shed light on the general weaknesses in building heating systems out there today, and shows that there is great potential of reducing building energy consumption in cost effective ways. Although the implemented solution might not be generally applicable for all building owners out there, it should act as an eye opener for building owners and help motivate them into assessing their building operation and start looking into new technologies. Moreover, the study provides legible incentives for both building owners and the society to further work together towards a more efficient and sustainable society.

abstract: Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated. Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer. / Dissertation/Thesis / Doctoral Dissertation Environmental Social Science 2016

Diploma thesis "Low-energy building" presents low-energy construction trends and their price comparision with conventional contruction. This thesis defines basic low-energy building terms, both building and construction work evaluation concepts and specifics of low-energy construction. Practical outcome is comparision between passive and conventional buildings, including economic appraisal.

Цель работы – создание математической модели технологического процесса энергопотребления здания, создание набора данных, создание нейросети для прогнозирования энергопотребления здания, выявление закономерностей и аномалий. Объект исследования – энергопотребление здания. Рассматриваются различные факторы, такие как работа системы кондиционирования, системы подачи воды, бытовые приборы, освещение помещения. Детально рассмотрена система кондиционирования. Построена математическая модель в итераторе OpenModelica, учитывающая факторы внешней и внутренней среды здания. Собран набор данных в формате .csv. Проведён анализ результатов, выявлены взаимодействия признаков системы, аномалии влияющие на энергопотребление здания. Написана нейросеть прогнозирующая энергопотребление здания, создан pipeline для выявления и визуализации аномалий. Результаты представлены на графиках, сделаны выводы. В ходе полученных результатов были предложены методы оптимизации работы системы, которые привели к экономическому и экологическому эффекту. / The purpose of the work is to create a mathematical model of the technological process of energy consumption of a building, create a data set, create a neural network to predict the energy consumption of a building, identify patterns and anomalies. The object of study is the energy consumption of the building. Various factors are considered, such as the operation of the air conditioning system, water supply system, household appliances, and room lighting. The air conditioning system is examined in detail. A mathematical model was built in the OpenModelica iterator, considering the factors of the external and internal environment of the building. Collected data set in .csv format. An analysis of the results was carried out, interactions of system characteristics and anomalies affecting the energy consumption of the building were identified. A neural network was written to predict the energy consumption of a building, and a pipeline was created to identify and visualize anomalies. The results are presented in graphs and conclusions are drawn. As a result of the results obtained, methods were proposed to optimize the operation of the system, which led to economic and environmental benefits.

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.

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.

The thesis deals with energy intensity of a low energy office building. In the thesis, em-phasis is put especially on the calculation of energy consumption for heating (cooling). The energy intensity of the building is determined and evaluated by four methods of calculation, which are compared with a valid certificate of energy performance of the building, which arose at the time of its construction. In the experimental part of the the-sis, the energy intensity of the building is compared to the actual energy consumption obtained by two experimental measurements in heating season 2012/2013. (The building is operating partly only). Different methods of solutions and valid legal regulations are elaborated in detail in the first part of the thesis.