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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.

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This paper presents a novel method to include the uncertainties or the weather-related input variables in neural network-based electric load forecasting models. The new method consists of traditionally trained neural networks and a set of equations to calculate the mean value and confidence intervals of the forecasted load. This method was tested for daily peak load forecasts for one year by using modified data from a large power system. The tests indicate that in addition to the confidence interval, the new method provides a more accurate mean forecast than a multilayer perceptron networks alone.

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