Short-term electrical load forecasting using least squares support vector machines

Abstract

This paper presents a least squares support vector machines (LS-SVM) approach to short-term electric load forecasting (STLF). The proposed algorithm is more robust and reliable as compared to the traditional approach when actual loads are forecasted and used as input variables. In order to provide the forecasted load, the LS-SVM interpolates among the load and temperature data in a training data set. Analysis of the experimental results proved that this approach can achieve greater forecasting accuracy than the traditional model.