基于集成学习的电动汽车充电站超短期负荷预测
收稿日期: 2021-11-30
网络出版日期: 2022-08-26
基金资助
国家自然科学基金项目(52167014);教育部产学合作协同育人项目(202002010028);上海市科委技术标准项目(21DZ2204800);国家电网有限公司科技项目(52094021000F)
Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning
Received date: 2021-11-30
Online published: 2022-08-26
精确的电动汽车充电站充电负荷预测是提高充电站安全经济运行的重要措施,也是支撑充电基础设施新建、扩容规划决策的重要基础.为提高电动汽车充电站超短期充电负荷预测的精度,提出一种基于集成学习的充电站超短期充电负荷预测方法.首先,以预测精确度与响应速度为主要目标,使用轻量级梯度提升框架构建基础回归器模型;其次,通过自适应提升方法对基础回归器群进行集成;最后,通过超参数调整与优化,建立基于能量集成轻量梯度提升框架(EEB-LGBM)的双层充电站超短期充电负荷预测模型.算例结果表明,相较于反向传播神经网络、卷积神经长短期记忆网络、差分自回归移动平均模型等预测模型,所提出的基于EEB-LGBM的超短期充电负荷预测模型具有更高的精确度,同时可以大幅度缩短训练时间和降低计算资源需求.
李恒杰, 朱江皓, 傅晓飞, 方陈, 梁达明, 周云 . 基于集成学习的电动汽车充电站超短期负荷预测[J]. 上海交通大学学报, 2022 , 56(8) : 1004 -1013 . DOI: 10.16183/j.cnki.jsjtu.2021.486
Accurate electric vehicle load forecasting is the basis for maintaining the safe and economical operation of charging stations, and for supporting the planning and decision-making of new and expanded charging infrastructure. In order to improve the accuracy of the ultra-short-term load forecasting of charging stations, an ultra-short-term load forecasting method based on ensemble learning is proposed. First, aimed at the prediction accuracy and the response speed, the light gradient boosting machine (LightGBM) framework is utilized to build several basic regressors. Next, the basic regressors are integrated by using the adaptive boosting (Adaboost) method. Finally, by using hyperparameter adjustment and optimization, a dual-system for ultra-short-term load forecasting of charging stations named energy ensemble boosting-light gradient boosting machine (EEB-LGBM) is generated. The analysis of the numerical examples shows that the proposed model has a higher accuracy than the back propagation neural network (BPNN), convolutional neural networks-long short term memory (CNN-LSTM), autoregressive integrated moving average (ARIMA), and other load forecasting methods, which can greatly reduce the training time and the computing power requirements of the training platform.
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