Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning
This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/7/3/133 |
| _version_ | 1797581231902162944 |
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| author | Ze-Yang Tang Qi-Biao Hu Yi-Bo Cui Lei Hu Yi-Wen Li Yu-Jie Li |
| author_facet | Ze-Yang Tang Qi-Biao Hu Yi-Bo Cui Lei Hu Yi-Wen Li Yu-Jie Li |
| author_sort | Ze-Yang Tang |
| collection | DOAJ |
| description | This paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome this limitation, this paper proposes an evaluation method based on natural language models for assessing the operation of charging stations. By utilizing the proposed SimCSEBERT model, this study analyzes the operational data, user charging data, and basic information of charging stations to predict the operational status and identify influential factors. Additionally, this study compared the evaluation accuracy and impact factor analysis accuracy of the baseline and the proposed model. The experimental results demonstrate that our model achieves a higher evaluation accuracy (operation evaluation accuracy = 0.9464; impact factor analysis accuracy = 0.9492) and effectively assesses the operation of EVCSs. Compared with traditional evaluation methods, this approach exhibits improved universality and a higher level of intelligence. It provides insights into the operation of EVCSs and user demands, allowing for the resolution of supply–demand contradictions that are caused by power supply constraints and the uneven distribution of charging demands. Furthermore, it offers guidance for more efficient and targeted strategies for the operation of charging stations. |
| first_indexed | 2024-03-10T23:02:26Z |
| format | Article |
| id | doaj.art-60be90f9cf98475aafe8da6a28ff0de2 |
| institution | Directory Open Access Journal |
| issn | 2504-2289 |
| language | English |
| last_indexed | 2024-03-10T23:02:26Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj.art-60be90f9cf98475aafe8da6a28ff0de22023-11-19T09:34:12ZengMDPI AGBig Data and Cognitive Computing2504-22892023-07-017313310.3390/bdcc7030133Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive LearningZe-Yang Tang0Qi-Biao Hu1Yi-Bo Cui2Lei Hu3Yi-Wen Li4Yu-Jie Li5State Grid Hubei Electric Power Research Institute, Wuhan 430077, ChinaSchool of Information Management, Wuhan University, Wuhan 430072, ChinaState Grid Hubei Electric Power Research Institute, Wuhan 430077, ChinaSchool of Microelectronics, Hubei University, Wuhan 430062, ChinaState Grid Hubei Electric Power Research Institute, Wuhan 430077, ChinaSchool of Information Management, Wuhan University, Wuhan 430072, ChinaThis paper aims to address the issue of evaluating the operation of electric vehicle charging stations (EVCSs). Previous studies have commonly employed the method of constructing comprehensive evaluation systems, which greatly relies on manual experience for index selection and weight allocation. To overcome this limitation, this paper proposes an evaluation method based on natural language models for assessing the operation of charging stations. By utilizing the proposed SimCSEBERT model, this study analyzes the operational data, user charging data, and basic information of charging stations to predict the operational status and identify influential factors. Additionally, this study compared the evaluation accuracy and impact factor analysis accuracy of the baseline and the proposed model. The experimental results demonstrate that our model achieves a higher evaluation accuracy (operation evaluation accuracy = 0.9464; impact factor analysis accuracy = 0.9492) and effectively assesses the operation of EVCSs. Compared with traditional evaluation methods, this approach exhibits improved universality and a higher level of intelligence. It provides insights into the operation of EVCSs and user demands, allowing for the resolution of supply–demand contradictions that are caused by power supply constraints and the uneven distribution of charging demands. Furthermore, it offers guidance for more efficient and targeted strategies for the operation of charging stations.https://www.mdpi.com/2504-2289/7/3/133electric vehiclecharging stationevaluation methodcontrastive learningnatural language models |
| spellingShingle | Ze-Yang Tang Qi-Biao Hu Yi-Bo Cui Lei Hu Yi-Wen Li Yu-Jie Li Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning Big Data and Cognitive Computing electric vehicle charging station evaluation method contrastive learning natural language models |
| title | Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning |
| title_full | Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning |
| title_fullStr | Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning |
| title_full_unstemmed | Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning |
| title_short | Evaluation Method of Electric Vehicle Charging Station Operation Based on Contrastive Learning |
| title_sort | evaluation method of electric vehicle charging station operation based on contrastive learning |
| topic | electric vehicle charging station evaluation method contrastive learning natural language models |
| url | https://www.mdpi.com/2504-2289/7/3/133 |
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