STUDY OF SUPPORT VECTOR MACHINES FOR SHORT-TERM LOAD FORECASTING
Yuancheng Li · Proceedings of the CSEE · 2003
A new methodology based on SVM for the electric power system 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 than the BP neural network.