Electric Vehicle Charging Load Prediction Considering Spatio-Temporal Node Importance Information

Sizu Hou, Xinyu Zhang () and Haiqing Yu
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Sizu Hou: College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Xinyu Zhang: College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Haiqing Yu: College of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2024, vol. 17, issue 19, 1-14

Abstract: The rapid development of electric vehicles (EVs) has brought great challenges to the power grid, so improving the EV load prediction accuracy is crucial to the safe operation of the power grid. Aiming at the problem of insufficient consideration of spatial dimension information in the current EV charging load forecasting research, this study proposes a forecasting method that considers spatio-temporal node importance information. The improved PageRank algorithm is used to carry out the importance degree calculation of the load nodes based on the historical load information and the geographic location information of the charging station nodes, and the spatio-temporal features are initially extracted. In addition, the attention mechanism and convolutional network techniques are also utilized to further mine the spatio-temporal feature information to improve the prediction accuracy. The results on a charging station load dataset within a city in the Hebei South Network show that the model in this study can effectively handle the task of forecasting large fluctuations and long time series of charging loads and improve the forecasting accuracy.

Keywords: electric vehicle; spatio-temporal feature; node importance; attention mechanism; spatio-temporal convolution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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