Many studies have conducted in the past that related to the domestic energy sector and households' appliances. These previous studies have explained the energy trends in the United Kingdom. In addition to this, the past studies have also provided wealth of information. However, all of these studies had some limitations. In addition, there were many gaps in the past studies regarding to the timing of usage the household's appliances and their daily contribution to the daily and peak demand. In this study, the researcher intended to overcome the limitations and gaps regarding the appliances time of use in the UK. In the present study, the data collected from Hillingdon Borough of London to ensure the study use the most reliable and valid data. Most importantly, suitable sampling and data collection technique applied in this study, which helped to obtain the appropriate data and outcome. All respondents were from the domestic sectors of the United Kingdom. Apart from this, to measure energy consumption in a more accurate manner, home appliances were categorised into several categories based on their functionality. Moreover, the household's appliances were categorised into time categories based on the time of use the appliances in order to determine the contribution of individual appliances at a certain time slot of the day to the total household consumption. Finally, the recommendations that have suggested in this study based on the current study as well as past studies. This means that the recommendations are a combination of all the major studies conducted. Additionally, based on the time a category of the household's appliances, a model was introduced that helped to determine how much of electrical appliances energy consuming in the UK households. Based on this model, the appliances consumption can managed and controlled. Thus, the model will help in mitigating the chances of the energy peak demand and will contribute towards energy and cost savings. Further, this study provides a valuable contribution to the field of smart homes as through the developed model, people can design a more efficient smart home. This specific method of determining energy demand has made the study more appropriate to forecast the 24h electricity demand and electricity price.

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京都大学 / 0048 / 新制・課程博士 / 博士(エネルギー科学) / 甲第17910号 / エネ博第282号 / 新制||エネ||59(附属図書館) / 30730 / 京都大学大学院エネルギー科学研究科エネルギー社会・環境科学専攻 / (主査)教授 手塚 哲央, 教授 東野 達, 教授 宇根﨑 博信 / 学位規則第4条第1項該当 / Doctor of Energy Science / Kyoto University / DFAM

Thesis (M. Sc. (Statistics)) --University of Limpopo, 2014 / analysis of price indices of electrical appliances in South Africa is performed using monthly data from Statistics South Africa for the period January 1998 to December 2010, with 2005 as a base year. Time series analysis (exponential smoothing and ARIMA) and neural networks are employed in developing forecasting models. The results for single, double and triple exponential smoothing are compared and triple exponential smoothing is found to be the best model amongst the three to forecast the electrical price indices in South Africa. ARCH models were also employed for the variable that failed to pass the requirements from ARIMA. Comparing neural networks, ARIMA and triple exponential smoothing results, neural networks is found to be the best model for forecasting price indices of electrical appliances. Regression analysis was then applied to the lighting equipment variable to check for a monthly effect after its plot depicted some seasonality pattern. Only the month of February did not have an impact or an effect on time since it was found not to be significantly different from zero. Multivariate time series is also applied in checking the correlation between the variables. Keywords: Time series analysis, ARIMA, ARCH, multiple linear regression, exponential smoothing, neural networks, electrical price indices.

Submitted thesis occupies by heat gains and heat balance of buildings. It includes the most common heats of people, lighting and electrical appliances. A second part deal with design air-conditioning part of Hospital Blansko include ventilation system, heating and cooling.

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented.

Various forecasting tools, based on historical data, exist for planners of national networks that are very effective in planning national interventions to ensure energy security, and meet carbon obligations over the long term. However, at a local community level, where energy demand patterns may significantly differ from the national picture, planners would be unable to justify local and more appropriate intervention due to the lack of appropriate planning tools. In this research, a new methodology is presented that initially creates a virtual community of households in a small community based on a survey of a similar community, and then predicts the energy behaviour of each household, and hence of the community. It is based on a combination of the statistical data, and a questionnaire survey. The methodology therefore enables realistic predictions and can help local planners decide on measures such as embedding renewable energy and demand management. Using the methodology developed, a study has been carried out in order to understand the patterns of electricity consumption within UK households. The methodology developed in this study has been used to investigate the incentives currently available to consumers to see if it would be possible to shift some of the load from peak hours. Furthermore, the possibility of using renewable energy (RE) at community level is also studied and the results presented. Real time pricing information was identified as a barrier to understanding the effectiveness of various incentives and interventions. A new pricing criteria has therefore been developed to help developers and planners of local communities to understand the cost of intervention. Conclusions have been drawn from the work. Finally, suggestions for future work have been presented. / Libyan government

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.

[pt] O objetivo desta dissertação é desenvolver modelos matemáticos que permitam estimar o tempo médio dos banhos com a utilização de chuveiros elétricos e a curva de carga desses aparelhos, considerando as informações das Pesquisas de Posses e Hábitos de Consumo (PPH) e medições realizadas com o auxílio de medidores eletrônicos com memória de massa, em residências com chuveiros elétricos. A motivação do estudo advém de uma exigência da ANEEL que determina que as distribuidoras de energia elétrica realizem a cada 2 (dois) ciclos de revisão tarifária a PPH em suas unidades consumidoras. Os métodos empregados foram: estatística descritiva (para a obtenção do tempo médio de banho); aplicação da regressão linear e de redes neurais (para corrigir a curva de carga horária obtida com a PPH, com base nos dados das medições). Os resultados foram promissores, pois o tempo médio de banho se encontra próximo às estimativas do PROCEL (que são de 8 (oito) a 10 (dez) minutos) e a curva de carga estimada se encontra próxima à da medição, sendo esta última o consumo real. Conclui-se que a abordagem desta dissertação resultou em melhorias na estimativa dos coeficientes de ajustes e que o método de redes neurais foi relativamente melhor que o método de regressão linear simples. / [en] The aim of this dissertation is to develop mathematical models that would allow the estimation of the average time of baths using electric showers and the load shape curves for these devices, obtained from two sources: the information of Electrical Appliances Ownership Survey and measurements of electric shower usage in households carried out with electronic meters with storage capacity. The motivation stems from a requirement of ANEEL that determines that the electric energy distributors periodically should hold a PPH in their consumer units. Concerning the average time of shower baths, the last PPH survey conducted by PROCEL in 2005 estimated this time between 8 (eight) and 10 (ten) minutes. The methods employed in this work were: descriptive statistics (for obtaining the average bath time); application of linear regression and neural networks (to estimate the correction factors to approximate the load shape curves obtained by PPH to those obtained by measurements). The obtained results are rather promising due to the following reasons: the average time of bath is next to the estimates of PROCEL and the corrected load shape curve estimated is quite close to the measured curve, the latter being the actual consumption. This approach has resulted in improvements in the estimation of the coefficients of adjustments and the method of neural networks was relatively better than the simple linear regression method.

Le domaine lié à ce travail est appelé « désagrégation d’énergie », où la principale préoccupation est de décomposer, ou désagréger, la consommation globale d’énergie électrique (par exemple, la consommation de tout un ménage) en une consommation détaillée donnée comme information de consommation par usage (par exemple, par appareil). Cette dernière permet d’avoir un retour sur la consommation pour les consommateurs ainsi que pour les fournisseurs et est utile pour permettre des économies d’énergie. Dans ce domaine de désagrégation d’énergie, il existe trois grandes questions auxquelles il faut répondre : qui consomme ? quand ? et combien ? Les recherches menées dans cette thèse se concentrent sur l’identification des appareils électriques, c’est-à-dire la réponse à la première question, en considérant particulièrement des appareils ménagers. À cet effet, nous utilisons le courant transitoire de mise en marche que nous modélisons en utilisant un nouveau modèle que nous avons proposé. De plus, nous utilisons les paramètres estimés de ce dernier pour la tâche d’identification. / The related field to this work is called “energy disaggregation" where the main concern is to break down, or disaggregate, the global electrical energy consumption (e.g. wholehouse consumption) into a detailed consumption given as end-use (e.g. appliance-level) consumption information. This latter gives consumption feedback to consumers and electricity providers and is helpful for energy savings. Three main questions have to be answered in the energy disaggregation field : who is consuming ? when ? and how much ? The research conducted in this thesis focuses on electrical appliances identification, i.e. the who question, considering particularly home appliances. For this purpose, we use the turn-on transient current signal which we model using a new model we proposed and use its estimated model parameters for the identification task.