Long-term load forecasting based on cyclic neural network and nonlinear relationship

Long-term load forecasting is the premise to ensure the stable operation of the power system. Therefore, long-term load forecasting based on the cyclic neural network and the nonlinear relationship is proposed. The connection structure of the cyclic neural network is constructed, and the long-term load forecasting data is output. According to the output results, the power data is modified with the help of a nonlinear relationship, and the preprocessing results of long-term load forecasting data are obtained. Thus, long-term load forecasting can be realized. The experimental results show that the relative error of the prediction results of the proposed method is small. The minimum is only 0.01%. The root mean square error is relatively small, ranging from 0.01% to 0.02%, and the accuracy of the prediction results is high.