Load Prediction of Charging Stations in Fast and Slow Charging Modes
[Purposes] The existing charging station load prediction mainly uses traditional statistical simulation or situation analyzation of electric vehicles (EVs) for forecasting, but the applicability and accuracy are not high enough. In response to this situation, it is proposed to predict the load of ch...
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| Format: | Article |
| Language: | English |
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Editorial Office of Journal of Taiyuan University of Technology
2025-05-01
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| Series: | Taiyuan Ligong Daxue xuebao |
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| Online Access: | https://tyutjournal.tyut.edu.cn/englishpaper/show-2417.html |
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| _version_ | 1849471898091520000 |
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| author | HAN Luyang ZHANG Wenhui SONG Zize HAO Xiaoyan |
| author_facet | HAN Luyang ZHANG Wenhui SONG Zize HAO Xiaoyan |
| author_sort | HAN Luyang |
| collection | DOAJ |
| description | [Purposes] The existing charging station load prediction mainly uses traditional statistical simulation or situation analyzation of electric vehicles (EVs) for forecasting, but the applicability and accuracy are not high enough. In response to this situation, it is proposed to predict the load of charging stations according to fast and slow charging modes. [Methods] First, the factors that affect the number of various types of EV in the charging station was analyzed, and BP neural network was used to predict the number of EVs in the charging station. Second, according to the different social functions of each type of EV, the proportion of fast and slow charging demands for each type of EV at each time was analyzed. Finally, LSTM and attention mechanism were used to predict the load of the charging station. [Results] The experimental results show that the accuracy of this method is higher than that of other existing methods. |
| format | Article |
| id | doaj-art-4ecf7cf92e7c4cfba91d99191a65d550 |
| institution | Kabale University |
| issn | 1007-9432 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Editorial Office of Journal of Taiyuan University of Technology |
| record_format | Article |
| series | Taiyuan Ligong Daxue xuebao |
| spelling | doaj-art-4ecf7cf92e7c4cfba91d99191a65d5502025-08-20T03:24:40ZengEditorial Office of Journal of Taiyuan University of TechnologyTaiyuan Ligong Daxue xuebao1007-94322025-05-0156342743510.16355/j.tyut.1007-9432.202402161007-9432(2025)03-0427-09Load Prediction of Charging Stations in Fast and Slow Charging ModesHAN Luyang0ZHANG Wenhui1SONG Zize2HAO Xiaoyan3Shanxi Jinyun Interconnection Technology Co., Lti, Taiyuan, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong, ChinaCollege of Artificial Intelligence, Taiyuan University of Technology, Jinzhong, ChinaCollege of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Jinzhong, China[Purposes] The existing charging station load prediction mainly uses traditional statistical simulation or situation analyzation of electric vehicles (EVs) for forecasting, but the applicability and accuracy are not high enough. In response to this situation, it is proposed to predict the load of charging stations according to fast and slow charging modes. [Methods] First, the factors that affect the number of various types of EV in the charging station was analyzed, and BP neural network was used to predict the number of EVs in the charging station. Second, according to the different social functions of each type of EV, the proportion of fast and slow charging demands for each type of EV at each time was analyzed. Finally, LSTM and attention mechanism were used to predict the load of the charging station. [Results] The experimental results show that the accuracy of this method is higher than that of other existing methods.https://tyutjournal.tyut.edu.cn/englishpaper/show-2417.htmlcharging stationelectric vehiclefast and slow charging modesload forecasting |
| spellingShingle | HAN Luyang ZHANG Wenhui SONG Zize HAO Xiaoyan Load Prediction of Charging Stations in Fast and Slow Charging Modes Taiyuan Ligong Daxue xuebao charging station electric vehicle fast and slow charging modes load forecasting |
| title | Load Prediction of Charging Stations in Fast and Slow Charging Modes |
| title_full | Load Prediction of Charging Stations in Fast and Slow Charging Modes |
| title_fullStr | Load Prediction of Charging Stations in Fast and Slow Charging Modes |
| title_full_unstemmed | Load Prediction of Charging Stations in Fast and Slow Charging Modes |
| title_short | Load Prediction of Charging Stations in Fast and Slow Charging Modes |
| title_sort | load prediction of charging stations in fast and slow charging modes |
| topic | charging station electric vehicle fast and slow charging modes load forecasting |
| url | https://tyutjournal.tyut.edu.cn/englishpaper/show-2417.html |
| work_keys_str_mv | AT hanluyang loadpredictionofchargingstationsinfastandslowchargingmodes AT zhangwenhui loadpredictionofchargingstationsinfastandslowchargingmodes AT songzize loadpredictionofchargingstationsinfastandslowchargingmodes AT haoxiaoyan loadpredictionofchargingstationsinfastandslowchargingmodes |