Load Forecasting of Battery Electric Vehicle Charging Station based on GA-Prophet-LSTM

Abstract

Under the general trend of electric vehicles (EV), aiming at the problem of load forecasting accuracy of electric vehicle charging stations (EVCS), this paper proposes a prediction method combining the Prophet model and Long Short-Term Memory (LSTM) neural network. First, the Prophet model and LSTM neural network model are constructed independently; second, the optimal weight of each model is determined by a genetic algorithm (GA), and a new prediction model is obtained by combining the above two models; finally, experiments are carried out with the charging load data of EV in the real scene. According to the analysis of experimental results, it can be proved that the GA-Prophet-LSTM model presented here is more accurate compared to any single model.