Short-term load forecasting based on support vector machines regression

A novel method based on SVM for the electric power system short-term load forecasting was presented. The proposed algorithm embodies the structural risk minimization (SRM) principle is more generalized performance and accurate as compared to artificial neural network which embodies the embodies risk minimization (ERM) principle. The theory of the SVM algorithm is based on statistical learning theory. Training of SVM leads to a quadratic programming problem. In order to improve forecast accuracy, the SVM interpolates among the load and temperature data in a training data set. Analysis of the experimental results proved that SVM could achieve greater accuracy and faster speed than the BP neural network.