Short-term load combined forecasting method based on BPNN and LS-SVM

Load forecasting, especially short-term load forecasting is of great significance for the planning, scheduling, marketing of power system. In order to predict the daily load as accurate as possible,a combined prediction method based on Least Squares Support Vector Machine (LS-SVM) and BP Neural Network (BPNN) is proposed in this paper. The historical load of relational better six-day ahead and the day type are selected as input,and got 1-dimensional output variable. Two sets of different prediction results are obtained from LS-SVM method and BPNN method, which is combined by using the method of minimum variance to research the final prediction results. The load prediction results of northwest grid show that the combined forecasting method has better prediction accuracy than LS-SVM and BPNN method. Therefore, this method is efficient and practical for a short-term load forecasting of electric power system.