Electric vehicle load forecasting based on improved neural network based on differential evolution algorithm

Abstract

Under the environment of energy crisis and green development, the development of electric vehicles has become inevitable. However, the charging load of electric vehicles has great uncertainty, which will have a certain impact on the security and stability of the power grid. Accurately predicting the charging load of electric vehicles is an effective method to avoid this problem. Considering the spatiotemporal characteristics of electric vehicle charging load, an electric vehicle load prediction method based on differential evolution algorithm improved BP neural network is proposed to complete the search of the weight space and network structure space of the neural network at the same time. The optimal network structure. The algorithm adopts the (1+1)-ES binary evolution strategy, uses a new network structure crossover and mutation method, and speeds up the search of the neural network model and the algorithm through the co-evolution of dual population structure and adaptive mutation rate strategies. Convergence improves the learning ability of the network and reduces the prediction error of the BP neural network model. The model and the BP neural network model are respectively used to predict the load of electric vehicles, and the comparison results prove the superiority of the proposed model.