Support vector machines for short‐term electrical load forecasting

Short‐term electrical load forecasting plays a vital role in the electric power industries. It ensures the availability of supply of electricity, as well as providing the means of avoiding over‐ and under‐utilization of generating capacity and therefore optimizes energy prices. Several methods have been applied to short‐term load forecasting, including statistical, regression and neural networks methods. This paper introduces support vector machines, the latest neural network algorithm, to short‐term electrical load forecasting and compares its performance with the auto‐regression model. The results indicate that support vector machines compare favourably against the auto‐regressive model using the same data for building and testing both models based on the root‐mean‐square errors between the actual and the predicted data. Support vector machines allow the training data set to be increased beyond what is possible using the auto‐regressive model or other neural networks methods. Increasing the training data further improves the performance of support vector machines method. Copyright © 2002 John Wiley & Sons, Ltd.