Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism
The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/3/687 |
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| author | Yang Chen Zeyang Tang Yibo Cui Wei Rao Yiwen Li |
| author_facet | Yang Chen Zeyang Tang Yibo Cui Wei Rao Yiwen Li |
| author_sort | Yang Chen |
| collection | DOAJ |
| description | The accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). First, this method delves into the correlations between various regions within the target city, establishing intricate coupling relationships among them. Subsequently, the FastDTW algorithm is employed to construct an adjacency matrix, capturing the spatiotemporal correlation among different urban regions. Finally, the ASTGCN model is applied to predict the power load of each region, which can accurately capture the spatiotemporal characteristics of the power load. The experimental results indicate that the proposed model has a more powerful comprehensive ability to capture spatiotemporal relationships and improve accuracy and stability in different prediction steps. |
| format | Article |
| id | doaj-art-cc0a4086a5bf482c84c4a4b3e05aa3b2 |
| institution | OA Journals |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-cc0a4086a5bf482c84c4a4b3e05aa3b22025-08-20T02:12:30ZengMDPI AGEnergies1996-10732025-02-0118368710.3390/en18030687Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention MechanismYang Chen0Zeyang Tang1Yibo Cui2Wei Rao3Yiwen Li4College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaCollege of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, ChinaState Grid Hubei Electric Power Research Institute, Wuhan 430077, ChinaState Grid Hubei Electric Power Research Institute, Wuhan 430077, ChinaState Grid Hubei Electric Power Research Institute, Wuhan 430077, ChinaThe accurate estimation and prediction of charging demand are crucial for the planning of charging infrastructure, grid layout, and the efficient operation of charging networks. To address the shortcomings of existing methods in utilizing the spatial interdependencies among urban regions, this paper proposes a forecasting approach that integrates dynamic time warping (DTW) with a spatial–temporal attention graph convolutional neural network (ASTGCN). First, this method delves into the correlations between various regions within the target city, establishing intricate coupling relationships among them. Subsequently, the FastDTW algorithm is employed to construct an adjacency matrix, capturing the spatiotemporal correlation among different urban regions. Finally, the ASTGCN model is applied to predict the power load of each region, which can accurately capture the spatiotemporal characteristics of the power load. The experimental results indicate that the proposed model has a more powerful comprehensive ability to capture spatiotemporal relationships and improve accuracy and stability in different prediction steps.https://www.mdpi.com/1996-1073/18/3/687electric vehiclecharging demandspatiotemporal distributionFastDTWload forecasting |
| spellingShingle | Yang Chen Zeyang Tang Yibo Cui Wei Rao Yiwen Li Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism Energies electric vehicle charging demand spatiotemporal distribution FastDTW load forecasting |
| title | Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism |
| title_full | Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism |
| title_fullStr | Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism |
| title_full_unstemmed | Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism |
| title_short | Electric Vehicle Charging Demand Prediction Model Based on Spatiotemporal Attention Mechanism |
| title_sort | electric vehicle charging demand prediction model based on spatiotemporal attention mechanism |
| topic | electric vehicle charging demand spatiotemporal distribution FastDTW load forecasting |
| url | https://www.mdpi.com/1996-1073/18/3/687 |
| work_keys_str_mv | AT yangchen electricvehiclechargingdemandpredictionmodelbasedonspatiotemporalattentionmechanism AT zeyangtang electricvehiclechargingdemandpredictionmodelbasedonspatiotemporalattentionmechanism AT yibocui electricvehiclechargingdemandpredictionmodelbasedonspatiotemporalattentionmechanism AT weirao electricvehiclechargingdemandpredictionmodelbasedonspatiotemporalattentionmechanism AT yiwenli electricvehiclechargingdemandpredictionmodelbasedonspatiotemporalattentionmechanism |