Electric Vehicle Charging Load Prediction Based on Weight Fusion Spatial–Temporal Graph Convolutional Network

• Published in: Energies (Basel) Vol. 17; no. 19; p. 4798
• Main Authors: Zhang, Jun, Cong, Huiluan, Zhou, Hui, Wang, Zhiqiang, Wen, Ziyi, Zhang, Xian
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Accuracy; Battery chargers; charging load prediction; Deep learning; Electric vehicles; Neural networks; Queuing theory; spatial–temporal network; Time series; Traffic flow; User behavior; Wavelet transforms; weight fusion
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en17194798

The rapid increase in electric vehicles (EVs) poses significant impacts on multi-energy system (MES) operation and energy management. Accurately assessing EV charging demand becomes crucial for maintaining MES stability, making it an urgent issue to be studied. Therefore, this paper proposes a novel deep learning-based EV charging load prediction framework to assess the impact of EVs on the MES. First, to model the EV traffic flow, a modified weight fusion spatial–temporal graph convolutional network (WSTGCN) is proposed to capture the inherent spatial–temporal characteristics of traffic flow. Specifically, to enhance the WSTGCN performance, the modified residual modules and weight fusion mechanism are integrated into the WSTGCN. Then, based on the predicted traffic flow, an improved queuing theory model is introduced to predict the charging load. In this improved queuing theory model, special consideration is given to subjective EV user behaviors, such as refusing to join queues and leaving impatiently, making the queuing model more realistic. Additionally, it should be noted that the proposed charging load predicting method relies on traffic flow data rather than historical charging data, which successfully addresses the data insufficiency problem of newly established charging stations, thereby offering significant practical value. Experimental results demonstrate that the proposed WSTGCN model exhibits superior accuracy in predicting traffic flow compared to other benchmark models, and the improved queuing theory model further enhances the accuracy of the results.