中圖分類號： TM910.6；U469.72 文獻標識碼： A DOI：10.16157/j.issn.0258-7998.212316 中文引用格式： 吳丹,，雷珽,，李芝娟，等. 基于XGBoost與LightGBM集成的電動汽車充電負荷預測模型[J].電子技術應用,，2022,，48(9)：44-49. 英文引用格式： Wu Dan，Lei Yu,，Li Zhijuan,，et al. Electric vehicle charging load forecasting based on XGBoost and LightGBM integration model[J]. Application of Electronic Technique，2022,，48(9)：44-49.
Electric vehicle charging load forecasting based on XGBoost and LightGBM integration model
1.State Grid Shanghai Municipal Electric Power Company，Shanghai 200122,，China,； 2.State Grid Shanghai Pudong Electric Power Supply Company，Shanghai 200122,，China,； 3.Institute of Automobile，Tongji University,，Shanghai 201804,，China
Abstract： With the scale development of electric vehicles, the load of charging stations has a certain impact on the power grid. In order to ensure the power grid run steadily, an electric vehicle charging load forecasting model based on the integration of eXtreme Gradient Boosting(XGBoost) and Light Gradient Boosting Machine(LightGBM) is proposed. This method uses the strategy of stacking integrated learning. Firstly, the base models of load forecasting are constructed based on XGBoost and LightGBM respectively. And then Ridge Regression(RR) algorithm is used to fuse the output results of the base models, the fusion result is the load forecasting value. Based on a variety of different load forecasting models, comparative experiments are carried out with the order data of charging station located in Jiading District, Shanghai. The results show that the load forecasting model constructed by this method has higher forecasting accuracy than the model based on single algorithm, and has certain theoretical and practical value for the smooth operation of power grid.
Key words : electric vehicle；load forecasting；Stacking integrated learning
