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.