Application of radial basis function neural network model for short-term load forecasting

Abstract

A description and original application of a type of neural network, called the radial basis function network (RBFN), to the short-term system load forecasting (SLF) problem is presented. The predictive capability of the RBFN models and their ability to produce accurate measures that can be used to estimate confidence intervals for the short-term forecasts are illustrated, and an indication of the reliability of the calculations is given. Performance results are given for daily peak and total load forecasts for one year using data from a large-scale power system. A comparison between results from the RBFN model and the back-propagation neural network are also presented.