Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning

被引：0

作者：

Li H. ^{[1
,3
]}

Zhu J. ^{[1
]}

Fu X. ^{[2
]}

Fang C. ^{[2
]}

Liang D. ^{[1
]}

Zhou Y. ^{[3
]}

机构：

[1] School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou

[2] State Grid Shanghai Municipal Electric Power Company, Shanghai

[3] Key Laboratory of Power Transmission and Power Conversion Control of the Ministry of Education, Shanghai Jiao Tong University, Shanghai

来源：

Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2022年 / 56卷 / 08期

关键词：

charging load; economy; electric vehicle charging station; ensemble learning; ultra-short-term forecasting;

D O I：

10.16183/j.cnki.jsjtu.2021.486

中图分类号：

学科分类号：

摘要：

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. © 2022 Shanghai Jiao Tong University. All rights reserved.

引用

页码：1004 / 1013

页数：9

共 23 条

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