Real-time Load Prediction Model of Electric Vehicle Charging Station Considering Environmental Factors

Abstract:

To mitigate the adverse effects of large-scale integration of electric vehicles into the grid， a method for the precise prediction of charging station load is proposed in this paper. The method employs a combination of LightGBM and XGBoost to construct offline-online ensemble models. Historical data including charging load， time， temperature， and weather are considered. Firstly， a charging load offline prediction model is established using LightGBM. Based on the XGBoost model， with the error between offline prediction model output load and actual load as the optimization target， and the real-time varying traffic flow as a covariate， an online prediction model is developed， and the error correction is performed on preliminary prediction results. Predictions from actual charging stations in a certain city indicate that compared to random forest （RF）， LightGBM， XGBoost， multilayer perceptron （MLP）， and LightGBM-RF ensemble models， the ensemble model demonstrates higher prediction accuracy while accurately forecasting real-time charging loads for different charging stations.

Key words:electric vehicles;charging load prediction;LightGBM;XGBoost;e-learning