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
赵子鋆 1彭清文 1邓铭 1李琳 1邓亚芝 1陈柏沅 1吴东琳1
作者信息
- 1. 国网湖南省电力有限公司长沙供电分公司,长沙 410015
- 折叠
摘要
电动汽车的快速发展导致充电负荷水平逐年升高,且具有强随机性、难预测的特点,因此关于充电站负荷预测的研究具有重要意义.首先,针对仅考虑负荷波动趋势的单因素模型预测精度不足问题,分析多重因素对充电站负荷预测精度的影响,建立考虑多重影响因素并基于CNN-LSTM(卷积神经网络-长短期记忆)混合网络结构的负荷预测模型;然后,考虑充电负荷的强随机性对模型的影响,提出基于RF(随机森林)算法的误差修正方法;最后,以真实充电站负荷数据为算例进行仿真验证.研究结果表明,经RF算法修正的CNN-LSTM模型的负荷预测结果能较为精准地覆盖真实值,相较于LSTM单模型和未经修正的CNN-LSTM模型,具有更高的预测精度和实用价值.
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
基金项目
国家电网湖南省电力公司科技项目(5216A522001Z)
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据