EV Charging Prediction in Residential Area Based on SE-GRU-MA Model Consider Multi-Source Data Feature Mining

Abstract

The number of electric vehicle (EV) in residential areas is growing rapidly, resulting in large-scale charging of EVs connected to the distribution network. This poses a challenge to the safe and stable operation of the distribution network. In order to cope with this challenge, it is crucial to achieve accurate EV charging load prediction. However, current researches on EV charging load prediction suffer from insufficient data feature mining and lower prediction accuracy. To address this issue, this paper proposes a SE-GRU-MA residential EV charging load prediction method that incorporates multi-source data feature mining. The proposed method employs a multi-source data feature mining approach based on Pearson correlation analysis, which enhances the training efficiency and prediction accuracy of the prediction model. Additionally, this study develops a prediction model based on SE-GRU-MA hybrid network to achieve accurate EV charging load prediction. Simulation cases on actual history data validate that the proposed feature mining method can effectively promote prediction accuracy, and proposed SE-GRU-MA prediction model exhibits superior prediction capability in comparison to existing models. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.