@MASTERSTHESIS\{IMM2011-06084,
    author       = "P. d. Saint-Aubain",
    title        = "Adaptive Load Forecasting",
    year         = "2011",
    school       = "Technical University of Denmark, {DTU} Informatics, {E-}mail: reception@imm.dtu.dk",
    address      = "Asmussens Alle, Building 305, {DK-}2800 Kgs. Lyngby, Denmark",
    type         = "",
    note         = "Supervised by Professor Henrik Madsen, hm@imm.dtu.dk, {DTU} Informatics",
    url          = "http://www.imm.dtu.dk/English.aspx",
    abstract     = "The purpose of this thesis is to contribute to the research in forecasting energy consumption in residential houses. The work is motivated by the Danish iPower project, which deals with investigation of possibilities for replacing fossil fuel with renewable energy. Renewable energy in Denmark is mostly based on wind power which is a highly 
fluctuating energy source and it is difficult to conserve. Energy consumption is also varying but independent of supply to the power plant. The fact that energy supply and energy consumption is not synchronized could be handled with a methodology that facilitates using the energy when present. The present work provides an adaptive method to get detailed knowledge of the energy consumption in residential houses. The method will be a contribution to forecasting energy consumption and to the development of Smart Grid technology. The approach taken is to reveal the details in the heating consumption in residential houses by developing mathematical models for the heat load. Based on district heating consumption data from four houses in a small area in Denmark and data from a nearby meteorological station, models are developed for separating the heating signals into different components. One of the models is able to split the overall consumption into heating consumption and hot water consumption. The heating consumption is further separated into parts explained by diurnal variation and variation explained by changes in outdoor temperature and the amount of solar radiation present. The method is adaptive to changes in the consumption due to variation in the daily routine of the inhabitants. The results are obtained by using mathematical modeling, statistics and time series analysis. For separating the hot water consumption and heating Low Pass Filters and advanced Kernel Smoothing techniques are used. The Kernel Smoother is extended to contain robust estimation and polynomial shape kernels. The further separation of the heating consumption is done with Kalman Filter techniques for signal separation."
}