Dynamic structural analysis and forecasting of residential electricity consumption

Abstract

This paper studies the dynamic relationships between electricity consumption and several potentially relevant variables, such as weather, price, and consumer income. Monthly data from January 1969 to December 1990 for all-electric residences in the southeast United States are used for this study. Because of the nature of the annual weather cycle, several of these time series are highly seasonal. Multiple-input transfer function models are employed to analyze the data for their dynamic structure and to evaluate future levels of electricity consumption. The linear transfer function (LTF) method is employed in the identification of transfer function models for structural analysis and forecasting. A major finding is that price plays a major role in explaining conservation behavior by electricity consumers. This result has important implications for forecasting the consumption of electric energy. This paper also demonstrates the appropriate construction of models for economic time series with strong seasonality.