Identification of seasonal short-term load forecasting models using statistical decision functions

A hierarchical classification algorithm is applied to hourly temperature readings to divide the historical database into seasonal subsets. These subsets are used to statistically identify and fit a response function for each season. These functional models constitute a library of models useful to the power scheduler. For a particular day, the appropriate model is selected by performing discriminant analysis. This approach is illustrated using data from a summer peaking utility. This application demonstrates that an entire procedure for specifying forecasting models can be formed with currently available statistical software. Furthermore, the models can be implemented on a microcomputer spreadsheet.<>