Electricity peak load forecasting with self-organizing map and support vector regression

This paper aims to study the short-term peak load forecasting (PLF) by using Kohonen self-organizing maps (SOM) and support vector regression (SVR). We first adopt a SOM network to cluster the input data set into several subsets in an unsupervised learning strategy. Then, several SVRs for the next day's peak load are used to fit the training data of each subset in the second stage. In the numerical experiments, data of electricity demand from the New York Independent System Operator (ISO) are used to verify the effectiveness of the prediction for the proposed method. The simulation results show that the proposed model can predict the next day's peak load with a considerably high accuracy compared with the ISO forecasts. (c) 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.