Electric load forecasting using an artificial neural network

Abstract

This paper presents an artificial neural network(ANN) approach to electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the fore-casted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the one-hour and 24-hour ahead fore-casts in our test on actual utility data are shown to be 1.40 % and 2.06%, respectively. This compares with an average error of 4.22 % for 24hour ahead forecasts with a currently used forecasting technique applied to the same data.