Predicting electric vehicle charging demand using a heterogeneous spatio-temporal graph convolutional network

Short-term Electric Vehicle (EV) charging demand prediction is an essential task in the fields of smart grid and intelligent transportation systems, as understanding the spatiotemporal distribution of charging demand over the next few hours could help operators of charging stations and the grid to take measures (e.g., dynamic pricing) in response to varying charging demand. This study proposed a heterogeneous spatial–temporal graph convolutional network to predict the EV charging demand at different spatial and temporal resolutions. Specifically, the authors first learned the spatial correlations between charging regions by constructing heterogeneous graphs, i.e., a geographic graph and a demand graph. Then, the authors used graph convolutional layers and gated recurrent units to extract spatio-temporal features in the observations. Further, the authors designed a region-specific prediction module that grouped regions based on graph embedding and point of interest (POI) data for prediction. The authors used a large real-world GPS dataset which contained over 76,000 private EVs in Beijing for model training and validation. The results showed that, compared with recently popular spatio-temporal prediction methods, the proposed model had superior prediction accuracy and steady performance at different scales of regions. In addition, the authors conducted ablation studies and hyperparameter sensitivity tests. The results suggested that incorporating the demand graph and geographic graph could help improve model performance.

• Record URL:
  https://doi.org/10.1016/j.trc.2023.104205
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S0968090X23001948
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
• Supplemental Notes:
  • © 2023 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Wang, Shengyou
  • Chen, Anthony
  • 0000-0003-4363-5041
  • Wang, Pinxi
  • Zhuge, Chengxiang
• Publication Date: 2023-8

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 104205
• Serial:
  • Transportation Research Part C: Emerging Technologies
  • Volume: 153
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 0968-090X
  • Serial URL: http://www.sciencedirect.com/science/journal/0968090X

Subject/Index Terms

• TRT Terms: Demand; Electric vehicle charging; Electric vehicles; Smart growth; Spatial analysis; Sustainable transportation
• Geographic Terms: Beijing (China)
• Subject Areas: Data and Information Technology; Energy; Environment; Highways; Operations and Traffic Management; Vehicles and Equipment;

Filing Info

• Accession Number: 01894403
• Record Type: Publication
• Files: TRIS
• Created Date: Sep 25 2023 2:46PM