Global concern about climate change and its impacts on the environment isprogressively increasing. This has raised an important issue in the buildings andconstruction industry regarding the effects of climate change on the buildingenergy performance. Currently, many residential buildings do not fulfill theenergy requirements even with the current weather conditions, mainly becauseof poor design or because the buildings are designed according to olderregulations. Consequently, there is a need for significant changes in the buildingdesign and construction in order to create a more sustainable built environmentwith lower energy consumption. However, it is not possible to change all thesebuildings in order to meet the needs of today. It is therefore of utmostimportance that the energy production comes from renewable sources as ameans to mitigate the potential environmental impacts of climate change.In Sweden, the field of renewable energy has seen a significant growth inrecent years, and particularly in Gotland, where the project under investigationis located. In Gotland, several wind farms and wind turbines have already beeninstalled in order to benefit from the advantageous wind conditions of the island.However, the development of the project for improving the connection ofGotland’s electricity grid with the Swedish mainland power grid has come intoa halt since 2017, and Gotland is now facing major issues in terms of electricityconsumption. And as climate change only escalates in the future, it is crucial toaddress this issue. Through an extensive study of the residential sector ofGotland, this thesis examines the buildings’ energy performance with theintention of finding and proposing possible solutions and alternatives that caneventually flatten the peaks in the energy consumption of the built environmentin Gotland.

This study analyses a carport located in the Hylte municipality equipped with eight EV charging stations that use solar panels as its only roof material. The main focus of the report is to review the maximizing of internal electricity consumption, implementation of load management, effects on the local grid and to report general improvements. Data was collected from driving logs, analysis of charging patterns and the inverter. The data was then compiled with the software Polysun and MS Excel for further investigation of internal electricity consumption, load management, electricity quality and more. The report shows that there is a 29.1 % internal energy consumption and that implementation of a 100 kWh battery may raise it to as much as 57.4 %. It also shows that there is a possibility of saving up to 39 238 SEK per year with the implementation of load management and that a 19 % increase of electricity production would be possible if the construction had the optimized direction and roof angle.

The amount of electricity used in individual UK homes varies considerably. Previous UK energy research has identified that high electricity consuming homes not only use more electricity, compared with others, but appear to be consuming even more electricity over time. Furthermore, there is additional evidence which shows that high consuming dwellings also have a greater potential to make energy savings than those who consume less. It has been suggested that future UK energy policy might focus on reducing the demand of high electricity consumers in order to reduce overall CO2 emissions. Therefore, understanding what drives high usage in domestic buildings is essential to support informed decisions. This thesis asserts that to improve knowledge and understanding of the factors affecting high electrical energy consumption in UK domestic buildings, it is necessary to combine an analysis of the occupants socio-economic characteristics, dwelling technical characteristics and appliance related aspects, with detailed monitoring of the ownership, power demand and occupants use of electrical appliances. Using a sample of 315 UK homes, the influence of socio-economic, technical and appliance related characteristics on the probability of a household being a high electrical energy consumer was investigated (Odds ratio analysis). Detailed appliance monitoring data was collected from 27 UK homes to establish the contributions of appliance ownership, power demand and use to high electrical energy demand (Appliance Electricity Use Survey). The current research found similar skewed electricity distributions towards high electricity consumers for both the 315 and 27 home cohorts. Conflicting results were however obtained from the two household samples with regard to whether high electricity consumers are increasing electrical energy demand over time. The results of the odds ratio analysis and Appliance Electricity Use Survey suggest that high electricity consumption in domestic buildings is related to a combination of the socio-economic characteristics of the building occupants, technical characteristics of the dwelling and the ownership, power demand and use of electrical appliances.

M. Tech. (Power Engineering, Faculty of Engineering and Technology), Vaal University of Technology / In view of the energy consumption problems, an impact study, extensive laboratory tests and an investigation towards comparable energy saving light sources was carried out on the light sources identified in the survey conducted at the Vaal University of Technology (VUT), Vanderbijlpark campus. Three types of identified incandescent light sources were purchased and extensively tested in order to obtain viable statistical data on the life span, luminance delivered per unit, power consumption and economic effect, as well as identifying relevant energy efficient light sources for replacement purposes. A suitable computerised maintenance program has been developed to be introduced to the VUT that currently does not have a lighting system maintenance program. The case study was located within the empirical-analytical paradigm, using quantitative data. The identified aims and goals place the empirical part of the study in the category of implementation evaluation research that provides an overview of a maintenance plan.

Nous traitons dans cette thèse le problème de la prédiction d’un processus stochastique à valeurs fonctionnelles. Nous commençons par étudier le modèle proposé par Antoniadis et al. (2006) dans le cadre d’une application pratique -la demande d’énergie électrique en France- où l’hypothèse de stationnarité semble ne pas se vérifier. L’écart du cadre stationnaire est double: d’une part, le niveau moyen de la série semble changer dans le temps, d’autre part il existe groupes dans les données qui peuvent être vus comme des classes de stationnarité.Nous explorons corrections qui améliorent la performance de prédiction. Les corrections visent à prendre en compte la présence de ces caractéristiques non stationnaires. En particulier, pour traiter l’existence de groupes, nous avons contraint le modèle de prévision à n’utiliser que les données qui appartiennent au même groupe que celui de la dernière observation disponible. Si le regroupement est connu, un simple post-traitement suffit pour obtenir des meilleures performances de prédiction.Si le regroupement en blocs est inconnu, nous proposons de découvrir le regroupement en utilisant des algorithmes d’analyse de classification non supervisée. La dimension infinie des trajectoires, pas nécessairement stationnaires, doit être prise en compte par l’algorithme. Nous proposons deux stratégies pour ce faire, toutes les deux basées sur les transformées en ondelettes. La première se base dans l’extraction d’attributs associés à la transformée en ondelettes discrète. L’extraction est suivie par une sélection des caractéristiques le plus significatives pour l’algorithme de classification. La seconde stratégie classifie directement les trajectoires à l’aide d’une mesure de dissimilarité sur les spectres en ondelettes. La troisième partie de la thèse est consacrée à explorer un modèle de prédiction alternatif qui intègre de l’information exogène. A cet effet, nous utilisons le cadre des processus Autorégressifs Hilbertiens. Nous proposons une nouvelle classe de processus que nous appelons processus Conditionnels Autorégressifs Hilbertiens (CARH). Nous développons l’équivalent des estimateurs par projection et par résolvant pour prédire de tels processus. / This thesis addresses the problem of predicting a functional valued stochastic process. We first explore the model proposed by Antoniadis et al. (2006) in the context of a practical application -the french electrical power demand- where the hypothesis of stationarity may fail. The departure from stationarity is twofold: an evolving mean level and the existence of groupsthat may be seen as classes of stationarity.We explore some corrections that enhance the prediction performance. The corrections aim to take into account the presence of these nonstationary features. In particular, to handle the existence of groups, we constraint the model to use only the data that belongs to the same group of the last available data. If one knows the grouping, a simple post-treatment suffices to obtain better prediction performances.If the grouping is unknown, we propose it from data using clustering analysis. The infinite dimension of the not necessarily stationary trajectories have to be taken into account by the clustering algorithm. We propose two strategies for this, both based on wavelet transforms. The first one uses a feature extraction approach through the Discrete Wavelet Transform combined with a feature selection algorithm to select the significant features to be used in a classical clustering algorithm. The second approach clusters directly the functions by means of a dissimilarity measure of the Continuous Wavelet spectra.The third part of thesis is dedicated to explore an alternative prediction model that incorporates exogenous information. For this purpose we use the framework given by the Autoregressive Hilbertian processes. We propose a new class of processes that we call Conditional Autoregressive Hilbertian (carh) and develop the equivalent of projection and resolvent classes of estimators to predict such processes.

This Engineering Doctorate aims to understand the factors that generate variability in small power consumption in commercial office buildings in order to generate more representative, building specific estimates of energy consumption. Current energy modelling practices in England are heavily focussed on simplified calculations for compliance with Building Regulations, which exclude numerous sources of energy use such as small power. When considered, estimates of small power consumption are often based on historic benchmarks, which fail to capture the significant variability of this end-use, as well as the dynamic nature of office environments. Six interrelated studies are presented in this thesis resulting in three contributions to existing theory and practice. The first contribution consists of new monitored data of energy consumption and power demand profiles for individual small power equipment in use in contemporary office buildings. These were used to inform a critical review of existing benchmarks widely used by designers in the UK. In addition, monthly and annual small power consumption data for different tenants occupying similar buildings demonstrated variations of up to 73%. The second contribution consists of a cross-disciplinary investigation into the factors influencing small power consumption. A study based on the Theory of Planned Behaviour demonstrated that perceived behavioural control may account for 17% of the variation in electricity use by different tenants. A subsequent monitoring study at the equipment level identified that user attitudes and actions may have a greater impact on variations in energy consumption than job requirements or computer specification alone. The third contribution consists of two predictive models for estimating small power demand and energy consumption in office buildings. Outputs from both models were validated and demonstrated a good correlation between predictions and monitored data. This research also led to the development and publication of industry guidance on how to stimate operational energy use at the design stage.

長久以來，電力消費與尖峰負載預測之方法不外乎使用迴歸模式及時間序列模式。其選用之自變數往往很多，如國民生產毛額(GNP)、價格、冷氣機台數、冷氣度(Cooling Degree Days)、冷氣時…等，因而使得整個預測模式相當複雜。吾人嘗試簡單以「溫度」單一變數來解釋電力消費與尖峰負載，作迴歸模式與時間序列模式並比較各模式的優劣，尋最適模式適配於『電力消費與溫度』、『尖峰負載與溫度』，並且以台灣地區為例，作實證分析研究，研究結果顯示溫度因素在電力消費上確實佔重要地位。文中溫度因素之單位，選用最具代表性之冷氣度(CDD)來表示，方便吾人探討。

Achieving sustainable consumption of energy is an important issue due to the increasing demand for energy and its environmental impact. One of the biggest consumers of the global energy production is the residential sector. Factors determining pattern of energy consumption in this sector are firstly, characteristics of the buildings and equipment and appliances which are used inside them and secondly, people who are using the buildings. The former could be approached by using efficiency strategies; i.e. designing and using materials and utilities which are low energy demanding or reducing consumption of energy. The latter could be reached by adoption of demand side management strategies which could improve pattern of energy consumption by the end users. Combining these strategies bring out energy-smart buildings with energy-smart people as the users. This project aims at introducing potential approaches to strategies of promoting sustainable behavior regarding energy consumption in individuals, with the focus on the students of Linköping University living in the properties of housing company of the city, Studentbostäder. For fulfilling this purpose, literature review has been done for finding influencing factors on and strategies for shaping of pro-environmental behavior. In the next step, two projects with focus on demand side management for changing energy consumption of individuals have been studied. Afterward, a questionnaire based on the results of the literature review was prepared and used to gain an understanding of first: attitude, values, knowledge, and awareness of students of Linköping University regarding environmental issues, and second: point of view of the students toward the strategies for shaping pro-environmental behavior. Results of the above mentioned methods were used for identifying characteristics of a demand side management project based on provision of feedback on energy consumption for the users. It has been proposed that designing and implementing such project has the potential of affecting pattern of energy consumption by people and lead to its reduction, especially among students accommodating at housing company of city of Linköping, Studentbostäder. More studies are needed for finding feasibility of implementing such project.

Šiame darba nagrinėjamos gyvenamųjų namų elektros apkrovos, elektros energijos suvartojimas. Pateikiami elektros apkrovų grafikai. Nagrinėjama kaip projektinė skaičiuojamoji galia keičiasi 47 metų laikotarpyje. Siūlomi būdai kaip nustatyti buto, namo skaičiuojamąsias apkrovas. Tiriama kiek realios namo skaičiuojamosios apkrovos skiriasi nuo projektinių. / This thesis addresses the residential electric load power consumption. Schedule of electrical loads. The present computational power of design changes 47-year period. Suggest ways to identify flat house computational loads. Investigated as far as the actual building computational load is different from the design.

Orientador: Prof. Dr. João Manoel Losada Moreira / Dissertação (mestrado) - Universidade Federal do ABC. Programa de Pós-Graduação em Energia, 2015. / O consumo residencial de energia elétrica no Brasil aumentou 64% nos últimos dez anos enquanto o consumo total de energia elétrica no País aumentou 51%. A intensidade elétrica dos domicílios definida, como a razão entre consumo de eletricidade domiciliar e o consumo efetivo das famílias, diminuiu 12% no período estudado, como também diminuiu a tarifa de eletricidade num 18%. Neste trabalho estuda-se o comportamento do consumo de eletricidade residencial em função dos fatores consumo efetivo das famílias, número de domicílios e tarifa de eletricidade. Duas técnicas foram utilizadas para a analise do consumo de eletricidade: a) análise de séries temporais para obter regressões do consumo de eletricidade em função de variáveis explicativas. A validade da regressão foi verificada por meio de testes de raiz unitária e de cointegração; b) técnica de decomposição LMDI ("logarithmic mean weight divisia method"). Os resultados da regressão linear forneceram elasticidades que permitiram avaliar e projetar no longo prazo o consumo de eletricidade. Os valores obtidos para as elasticidades para o período 1985-2013 foram 0,97 para o número de domicílios, 0,35 para consumo efetivo das famílias e - 0,32 para a tarifa. Os resultados mostram que o consumo de eletricidade apresenta mais sensibilidade às variações na variável numero de domicílios, isto é, o crescente aumento do número de residências no país é o responsável principal pelo aumento do consumo de eletricidade residencial. As variáveis explicativas consumo efetivo das famílias e tarifa de eletricidade variaram mais no período analisado que o número de domicílios, que apresenta um crescimento mais uniforme. Confirmou-se que a tarifa é uma possível variável controladora do consumo de eletricidade residencial por afetar indiretamente as preferências e hábitos das famílias. Para ser efetiva na redução de consumo de energia residencial ela deve ter uma taxa de variação maior que a taxa de variação do consumo efetivo das famílias, pois suas elasticidades são muito próximas, mas de sinais contrários. A partir da decomposição pela técnica LMDI, obteve-se a contribuição de cada variável explicativa no consumo de eletricidade, confirmando que a técnica é útil para conhecer e analisar os fatores em que a eletricidade decompõe-se, e não como uma técnica de projeção do consumo de eletricidade. / The residential electricity consumption in Brazil increased 64 % between 2003 and 2013 while the total electricity consumption in the country increased 51 %. The electric intensity of households, defined as the ratio of household consumption of electricity and the final consumption of households fell 12% during the study period, and the electricity tariff fell 18 %. In this work we study the residential electricity consumption behavior in terms of actual final consumption of household, number of households and electricity tariff. Two techniques were used for the analysis of electricity consumption: a) time-series analysis for regressions of electricity consumption in terms of explanatory variables. The validity of the regression was verified by unit root test and cointegration test; b) LMDI decomposition technique ("logarithmic mean weight dividing method"). The results of linear regression provided elasticities that allow us to evaluate and manage the long-term consumption of electricity. The values obtained for the elasticities for the period 1985-2013 were 0.97 for the number of households, 0.35 to actual final consumption of household and -0.32 for the electricity tariff. The results show that electricity consumption has more sensitivity to changes in the variable number of households, that is, the increasing number of households in the country is primarily responsible for the increase in residential electricity consumption. The explanatory variables consumption of household and electricity tariff varied over the analyzed period while the number of households presented a uniform growth. The electricity tariff may be used to manage the residential electricity demand. For reducing residential electricity consumption, its growth rate should be higher than that of the consumer spending because their elasticity¿s are very close, but of opposite signs. From the decomposition by LMDI technique, it obtained the contribution of each explanatory variable in electricity consumption, confirming this technique useful to know and analyze the factors on which electricity decomposes, and not as projection technique electricity consumption.