基于优化随机森林算法的电动汽车充电负荷预测

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doi：10.3969/J.ISSN.1672-7274.2025.06.009

中图分类号：TM910.6;TP31;U469.72 文献标志码：B 文章编码：1672-7274（2025）06-0026-03

Electric Vehicle ChargingLoad Forecasting Based on Optimized Random Forest Algorithm

ZHANG Meng

（Shanxi Vocational College of Finance and Trade， Taiyuan O3oo31， China）

Abstract： With the rapid development ofthe electric vehicle （EV） market，the demand forcharging is increasing， posing unprecedented challnges to the existing power system. Accurately predicting the charging load of electric vehiclescan guidethe management and scheduling of the power system，improve the eficiencyofenergyutilization， and help alleviate charging pressure.This article proposes an electric vehicle charging load prediction method based on optimized random forest algorithm.The experimentalresults show that compared with the SVM method，this model has significantly improved accuracyand stability，and can provide more reliable basis for power system management and scheduling， effectively coping with the pressure of charging peak.

Keywords： charging load forecasting; random forest; grid search

0 引言

随着全球对可持续发展和环保的重视，电动汽车（EV）作为一种低碳出行方式，近年来得到了快速发展。（剩余3522字）