Study models load shapes in residential energy usage, suggesting improved data accuracy.
A method is presented for modeling load shapes in the residential sector by using hourly whole-house metered data and temperature. Individual household-level load data are analyzed to achieve data smoothing (noise rejection) and compression (in the ratio of approximately 150:1), and to disaggregate the weather-dependent and weather-independent components of the load. The weather-independent (lifestyle) component is modeled as a weighted sum of orthogonal functions (primarily sinusoids and boxcars), while the weather-dependent component is modeled as a nonlinear dynamic system based on thermodynamic principles. Numerical examples show efficient representation of data and good model fit.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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Schi̇ck et al. (1988) studied this question.
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