Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning
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 o...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2022-08-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-8-1004.shtml |
| _version_ | 1811283122795315200 |
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| author | LI Hengjie, ZHU Jianghao, FU Xiaofei, FANG Chen, LIANG Daming, ZHOU Yun |
| author_facet | LI Hengjie, ZHU Jianghao, FU Xiaofei, FANG Chen, LIANG Daming, ZHOU Yun |
| author_sort | LI Hengjie, ZHU Jianghao, FU Xiaofei, FANG Chen, LIANG Daming, ZHOU Yun |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T02:06:05Z |
| format | Article |
| id | doaj.art-753f0348905f47349c52594a3714d00e |
| institution | Directory Open Access Journal |
| issn | 1006-2467 |
| language | zho |
| last_indexed | 2024-04-13T02:06:05Z |
| publishDate | 2022-08-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj.art-753f0348905f47349c52594a3714d00e2022-12-22T03:07:28ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672022-08-015681004101310.16183/j.cnki.jsjtu.2021.486Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble LearningLI Hengjie, ZHU Jianghao, FU Xiaofei, FANG Chen, LIANG Daming, ZHOU Yun01. School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China;2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China;3. Key Laboratory of Power Transmission and Power Conversion Control of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, ChinaAccurate 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.http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-8-1004.shtmlelectric vehicle charging stationcharging loadultra-short-term forecastingensemble learningeconomy |
| spellingShingle | LI Hengjie, ZHU Jianghao, FU Xiaofei, FANG Chen, LIANG Daming, ZHOU Yun Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning Shanghai Jiaotong Daxue xuebao electric vehicle charging station charging load ultra-short-term forecasting ensemble learning economy |
| title | Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning |
| title_full | Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning |
| title_fullStr | Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning |
| title_full_unstemmed | Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning |
| title_short | Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning |
| title_sort | ultra short term load forecasting of electric vehicle charging stations based on ensemble learning |
| topic | electric vehicle charging station charging load ultra-short-term forecasting ensemble learning economy |
| url | http://xuebao.sjtu.edu.cn/article/2022/1006-2467/1006-2467-56-8-1004.shtml |
| work_keys_str_mv | AT lihengjiezhujianghaofuxiaofeifangchenliangdamingzhouyun ultrashorttermloadforecastingofelectricvehiclechargingstationsbasedonensemblelearning |