Research on Prediction Method of Distribution Network Activity in Green Electric Power through PSO-WNN

Abstract

The convergence and prediction accuracy of the artificial neural network prediction model are affected by the initial parameter settings of the model, and the wavelet neural network (WNN) modeling algorithm can effectively overcome this defect and achieve better prediction results. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the initial weights, and the optimal value of the particle swarm algorithm is assigned to the WNN to replace the initial random assignment of the WNN. The WNN is trained according to the backpropagation (BP) algorithm until convergence. The particle swarm optimization wavelet neural network (PSO-WNN) model is used to predict the cost of a single grid activity based on the cost driver parameters of the power grid. The prediction error analysis shows that the model significantly improves the accuracy of the activity cost prediction, which is beneficial to the grid enterprise to effectively manage the cost drivers and track the specific effects of the grid activity cost which produced by the driver parameters.