Medium and Long-Term Forecasting Method of China’s Power Load Based on SaDE-SVM Algorithm

Abstract

Medium and long-term power load forecasting is the basis for power system planning and construction. This paper builds a prediction model based on SaDE-SVM algorithm. In order to reduce its selection problem of excessive large-scale hyperplane parameters, improve global optimization ability of traditional SVM, and further improve the prediction accuracy of SVM, the SaDE-SVM optimization algorithm is proposed. This algorithm optimizes the training process of traditional SVM based on adaptive differential evolution algorithm. The results of the medium and long-term forecasting for China’s power load show that the improved SaDE-SVM algorithm has good adaptability, robustness, fast convergence rate, and high accuracy for multi-influencing factors prediction model with less data volume, and is applicable to relevant medium and long-term forecasts.