Mid-term load forecasting based on ADE-SVM and fuzzy theory

Based on mid-term load forecasting with support vector machine (SVM) for power system,since SVM parameters are hard to be determined,the differential evolution (DE) algorithm is introduced.And to reduce the optimal time of DE and improve the global search ability,the method based on noise estimation of training sample is applied to determine the scope of SVM parameters as the scope of DE optimal,to guide optimal of DE.Meanwhile,the adaptive operators are introduced,and self-adaptive DE (ADE) algorithm is applied to optimize SVM parameters.Due to the influence of the temperature to load is vague,the membership function is used to fuzz the temperature to further improve the prediction accuracy.This method is used to mid-term load forecasting of European intelligence technology network (EUNITE) data,the results show that the method can accurately predict load changes,having higher prediction precision than other algorithms,which provides an important method for the power system load forecasting.