Forecasting Demand for Electric Power

We are developing a forecaster for daily extremes of demand for electric power encountered in the service area of a large midwestern utility and using this application as a testbed for approaches to input dimension reduction and decomposition of network training. Projection pursuit regression representations and the ability of algorithms like SIR to quickly find reasonable weighting vectors enable us to confront the vexing architecture selection problem by reducing high-dimensional gradient searchs to fitting single-input single-output (SISO) subnets. We introduce dimension reduction algorithms, to select features or relevant subsets of a set of many variables, based on minimizing an index of level-set dispersions (closely related to a projection index and to SIR), and combine them with backfitting to implement a neural network version of projection pursuit. The performance achieved by our approach, when trained on 1989, 1990 data and tested on 1991 data, is comparable to that achieved in our earlier study of backpropagation trained networks.