基于K-means聚类的BP-DTR的电动汽车短期充电负荷预测
Short-term charging load prediction of electric vehicles based on BP-DTR with K-means clustering
陈启凡 1丁云飞 1田锟 1孙钱承1
作者信息
- 1. 上海电机学院电气学院,上海 201306
- 折叠
摘要
随着电动汽车的普及率越来越高,电动汽车充电行为对电网稳定运行的影响愈发显著,对充电负荷的预测愈发重要,提出了一种基于K-means聚类的BP-DTR电动汽车短期充电负荷组合预测模型.首先,利用K-means聚类方法将充电负荷聚类;其次,使用反向传播神经(BP)和决策树回归(DTR)分别对聚类后的每一类数据进行预测;最后,采用最优化方法加权组合得到每一类的预测结果并求和.实验以真实数据为基础,采用蒙特卡洛方法获得电动汽车充电负荷.实验结果表明:该预测模型能考虑到充电负荷影响因素,有效提高电动汽车充电负荷预测的准确性,为电网的优化运行和规划提供了参考.
Abstract
With the rapid growth of electric vehicle adoption,electric vehicle charging behavior has a significant impact on the stability of power grids and the charging load prediction.A short-term electric vehicle charging load combination prediction model is proposed based on K-means clustering and a backpropagation neural network with a decision tree regressor(BP-DTR).First,the K-means clustering method is used to cluster the charging load.Second,BP and DTR are employed to predict each group of the clustered data separately.Finally,the optimized method is utilized to combine the weighted prediction results of each group,which are then summed up.The experiments are based on real data,and the Monte Carlo method is used to obtain the electric vehicle charging load.The results show that this prediction model can consider the influencing factors of the charging load,effectively improving the accuracy of electric vehicle charging load predictions,and providing a reference for the optimal operation and planning of the power grid.
关键词
充电负荷预测/K-means聚类/反向传播神经(BP)/决策树回归(DTR)Key words
charging load prediction/K-means clustering/BP neural network/decision tree regression引用本文复制引用
基金项目
航空科学基金资助项目(20200001012015)
出版年
2024
NETL
NSTL
万方数据