浙江电力2024，Vol.43Issue(4)：21-28,8.DOI:10.19585/j.zjdl.202404003
考虑多因素影响与误差修正的充电站负荷预测
Load forecasting for charging stations considering multiple influencing factors and error correction
摘要
Abstract
The rapid development of electric vehicles has led to a yearly increase in charging load levels,character-ized by strong randomness and unpredictability.Therefore,research on load forecasting for charging stations holds significant importance.Firstly,to address the inaccuracy of single-factor forecasting models that only consider load fluctuation trends,this paper analyzes the impact of multiple factors on the accuracy of charging station load fore-casting.A load forecasting model is established that takes into account multiple influencing factors and is based on CNN-LSTM(convolutional neural network,long short-term memory).Subsequently,given the impact of strong ran-domness of charging load on the model,an error correction method based on the random forest(RF)algorithm is proposed.Finally,the paper conducts simulation verification using real charging station load data as a case study.The research results indicate that the load prediction of the CNN-LSTM model,corrected by the RF algorithm,can accurately cover real values.Compared to the LSTM single model and the non-corrected CNN-LSTM model,it exhib-its higher forecasting accuracy and practical value.关键词
电动汽车/充电负荷/充电站/负荷预测/CNN-LSTMKey words
electric vehicle/charging load/charging station/load forecasting/CNN-LSTM引用本文复制引用
赵子鋆,彭清文,邓铭,李琳,邓亚芝,陈柏沅,吴东琳..考虑多因素影响与误差修正的充电站负荷预测[J].浙江电力,2024,43(4):21-28,8.基金项目
国网湖南省电力有限公司科技项目(5216A522001Z) （5216A522001Z）
