Forecasting Daily Electricity Load Curves

Short term electricity load forecasting is a well-known problem, and many neural computing approaches for solving it have been proposed in recent years. In this paper, we argue in favour of its decomposition into two subproblems, and propose a solution for one of them: the prediction, en bloc, of the daily load profile, or configuration, for the different hours of a particular future date. From this point of view, we propose a methodology where the shape of the load curve is inferred from two variables: one of them is nominal and serves to characterize the type of day, calendarwise; the other one consists of a pattern of temperature forecasts for that day. The approach includes fuzzy clustering of past temperature patterns, as well as fuzzy clustering of past load curves, and inference is based on the computation of empirical correlations among the three variables: the day class, the temperature pattern, and the load pattern. Key-Words: Load Forecasting, Neural Networks, Fuzzy Clustering, Pattern Recognition, Classification.