A spatiotemporal demand forecasting study of new energy vehicle charging stations
Research Article
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A spatiotemporal demand forecasting study of new energy vehicle charging stations

Yuwen Lin 1* Chenyang Liu 2, Qianchun Wu 3, Yanxi Pu 4
1 Anhui University of Finance and Economics
2 Chengdu University of Technology
3 Anhui University of Finance and Economics
4 Anhui University of Finance and Economics
*Corresponding author: 2454299024@qq.com
Published on 1 July 2025
Journal Cover
AEI Vol.16 Issue 6
ISSN (Print): 2977-3911
ISSN (Online): 2977-3903
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Abstract

Against the backdrop of Electric Vehicle (EV) proliferation, accurately predicting the spatiotemporal distribution of charging demand is crucial. However, traditional methods face considerable challenges. This study aims to uncover the dynamic patterns of charging demand and construct a predictive framework. Methodologically, the ARIMA time series model is employed to analyze temporal features, Moran’s I index is used to assess spatial autocorrelation, and K-means clustering identifies spatial patterns. Pearson correlation and spatial regression models are integrated to quantify the influence of geographic dependency and socioeconomic factors. The results reveal a "dual-peak" temporal distribution and a "multi-core" spatial aggregation pattern of charging demand. Traffic flow and regional functional types are identified as key influencing factors.

Keywords:

charging demand forecasting, spatiotemporal distribution, ARIMA model, Moran’s I index, K-means clustering, spatial regression model

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Lin,Y.;Liu,C.;Wu,Q.;Pu,Y. (2025). A spatiotemporal demand forecasting study of new energy vehicle charging stations. Advances in Engineering Innovation,16(6),52-64.

References

[1]. Zhang, M. X., Xu, L. C., Yang, X., Wu, Z. J., & Zhang, Q. Q. (2023). A study on charging station planning based on the spatiotemporal distribution characteristics of electric vehicle charging demand.Power System Technology, 47(1), 256–268.

[2]. Zhou, C. Q., Xiang, Y., Tong, H., Rao, P., Qing, Y. F., & Liu, Y. B. (2022). Estimation of electric vehicle charging demand and V2G controllable capacity driven by trajectory data.Automation of Electric Power Systems, 46(12), 46–55.

[3]. Wang, H. X., Yuan, J. H., Chen, Z., Ma, Y. M., Dong, H. N., Yuan, S., & Yang, J. Y. (2022). A review and prospect of key technologies for integrated vehicle–station–grid operation in smart cities.Transactions of China Electrotechnical Society, 37(1), 112–132.

[4]. Yuan, X. X., Pan, M. Y., Duan, D. P., Li, X. L., & Chen, H. Y. (2021). Electric vehicle charging load forecasting method based on grid division.Journal of Electric Power Science and Technology, 36(3), 19–26.

[5]. Wang, R., Gao, X., Li, J. L., Xu, J. H., Ai, G. Q., & Jing, X. (2020). Electric vehicle charging load forecasting method based on cluster analysis.Power System Protection and Control, 48(16), 37–44.

References

[1]. Zhang, M. X., Xu, L. C., Yang, X., Wu, Z. J., & Zhang, Q. Q. (2023). A study on charging station planning based on the spatiotemporal distribution characteristics of electric vehicle charging demand.Power System Technology, 47(1), 256–268.

[2]. Zhou, C. Q., Xiang, Y., Tong, H., Rao, P., Qing, Y. F., & Liu, Y. B. (2022). Estimation of electric vehicle charging demand and V2G controllable capacity driven by trajectory data.Automation of Electric Power Systems, 46(12), 46–55.

[3]. Wang, H. X., Yuan, J. H., Chen, Z., Ma, Y. M., Dong, H. N., Yuan, S., & Yang, J. Y. (2022). A review and prospect of key technologies for integrated vehicle–station–grid operation in smart cities.Transactions of China Electrotechnical Society, 37(1), 112–132.

[4]. Yuan, X. X., Pan, M. Y., Duan, D. P., Li, X. L., & Chen, H. Y. (2021). Electric vehicle charging load forecasting method based on grid division.Journal of Electric Power Science and Technology, 36(3), 19–26.

[5]. Wang, R., Gao, X., Li, J. L., Xu, J. H., Ai, G. Q., & Jing, X. (2020). Electric vehicle charging load forecasting method based on cluster analysis.Power System Protection and Control, 48(16), 37–44.

Cite this article

Lin,Y.;Liu,C.;Wu,Q.;Pu,Y. (2025). A spatiotemporal demand forecasting study of new energy vehicle charging stations. Advances in Engineering Innovation,16(6),52-64.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Journal: Advances in Engineering Innovation

Volume number: Vol.16
Issue number: Issue 6
ISSN: 2977-3903(Print) / 2977-3911(Online)

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