Support vector machine approach for peak load forecasting

A new algorithm with high forecasting accuracy and global optimal property for peak load forecasting is proposed based on the support vector machine (SVM) method, where the cross-validation is introduced into hyper-parameter estimation in SVM to outperform the common cut and try method. In addition to the load variables, the temperature information, weekday and vacation information are taken into account in the input samples to improve the forecasting accuracy. The practical examples show that the accuracy of the SVM is 0.4%-0.8% higher than artificial neural network under the same load and weather conditions.