{"id":9324,"date":"2026-07-25T19:43:38","date_gmt":"2026-07-25T11:43:38","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9324"},"modified":"2026-07-25T22:29:54","modified_gmt":"2026-07-25T14:29:54","slug":"jase-202610-33-059","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202610-33-059","title":{"rendered":"An AI-Driven Multi-Scale Graph Neural Network for Maintenance-Oriented Charging Demand Forecasting in Cyber-Physical Systems"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 33<\/a><\/p>\n<\/div>\n\n\n\n

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Haixing Guo<\/i><\/a><\/p>\n\n\n\n

State Grid Shaanxi Information and Telecommunication Company, Xi\u2019an, Shaanxi, China.<\/p>\n<\/div>\n\n\n\n

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Received: June 9, 2026
Accepted: June 26, 2026
Publication Date: July 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Charging station node distance matrix. <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202610_33.059<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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As a core part of Cyber-Physical Systems (CPS), EV charging networks face limitations in traditional spatiotemporal models due to static spatial representation and poor heterogeneous data fusion. This paper proposes a Multi-Scale Subgraph Spatiotemporal Graph Convolutional Network (MSG-STGCN) for predictive load management. By integrating multi-modal data- including POI semantics and traffic conditions- into a dynamic heterogeneous graph, the model utilizes a condition-triggered mechanism to adaptively capture multi-scale spatial contexts. Experiments on real-world datasets show MSG-STGCN achieves MAE of 0.22 and MAPE of 9.75%, outperforming the baseline AGCRN by 0.07 in MAE and 0.81% in MAPE. Ablation studies further verify the effectiveness of multi-scale subgraph embedding and spatiotemporal attention.<\/p>\n\n\n\n

Keywords: Cyber-Physical Systems, AI-Driven Predictive Maintenance, Spatiotemporal Graph Neural Networks<\/em><\/p>\n\n\n\n

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