同济大学学报（自然科学版）2024，Vol.52Issue(6)：962-969,8.DOI:10.11908/j.issn.0253-374x.22362
考虑环境因素的电动汽车充电站实时负荷预测模型
Real-time Load Prediction Model of Electric Vehicle Charging Station Considering Environmental Factors
摘要
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.关键词
电动汽车/充电负荷预测/LightGBM(light gradient boosting machine)/XGBoost(eXtreme gradient boosting)/在线学习Key words
electric vehicles/charging load prediction/LightGBM/XGBoost/e-learning分类
交通工程引用本文复制引用
李波,王宁,吕叶林,陈宇..考虑环境因素的电动汽车充电站实时负荷预测模型[J].同济大学学报（自然科学版）,2024,52(6):962-969,8.基金项目
国家电网总部科技项目(5108-202119040A-0-0-00) （5108-202119040A-0-0-00）
