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|Title:||The Impact of Demographics on Residential Electricity Usage|
|Department:||Mathematics and Statistics|
|Abstract:||The electricity consumption efficiency in the residential sector is commonly discussed in previous studies. Over the previous studies, different factors influencing electricity consumption have been covered, including economic factors, lifestyle and demographic factors, climate and environmental factors and technological development. With respect to estimation methodologies in these studies, there are three methods existing—conditional demand analysis, neural network and engineering method. A significant amount of information for my thesis is drawn from the collaborative project involving McMaster University and Hydro One. My thesis mainly focuses on residential electricity consumption efficiency and the relationship between the total electricity consumption and a number of variables, including dwelling information, time-of-use prices, weather data and demographic factors. I am particularly interested in the influence of demographics. The data sources of variables include four categories---dwelling and household information, consumption data, weather data and price data in 2013. In my regression estimation, I include four systems components—heating system, water heating system, cooling system and other appliances system. Each system has its own error term. I discuss and estimate models where the error terms are correlated and uncorrelated. Seven versions of models are discussed with different combinations of variables in the model and variables in the variance model of the errors. I choose a final model after conducting Wald hypothesis tests. Finally, I list a table of illustrative examples explaining the influence of demographic factors---education distribution level, age distribution level and number of residents on electricity usage. From the results, I can conclude that education distribution level exerts a very significant impact on total electricity consumption.|
|Appears in Collections:||Open Access Dissertations and Theses|
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|Zhou_Tianyao_201609_degree.pdf||Master Thesis||5.81 MB||Adobe PDF||View/Open|
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