Journal of Applied Science and Engineering

Published by Tamkang University Press

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HMGNN: Charging Demand Prediction with Hypergraph- Connected Multimodal Temporal-Spatial Data Mining

 2026

 Volume 34

Haixing Guo1, Xiaolong Ren1, Hengbin Si1, Mengting Wang1, Qing Wu1, Xi Chen1, Shuang Tian1, and Xuan Zhen2

1State Grid Shaanxi Information and Telecommunication Company, Xi’an, Shaanxi, China

2School of Informatics, Xiamen University, Xiamen, P.R. China

Received: April 20, 2026
Accepted: June 13, 2026
Publication Date: August 17, 2026

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Framework of HMGNN

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Electric vehicles (EVs) are undergoing rapid expansion, leading to an explosive increase in ownership. Thissurge, however, poses unprecedented challenges to power grid operation and charging infrastructure planning. In particular, accurately forecasting charging demand has become increasingly difficult. Conventional graph-based or single-modal models often fail to capture the complex and nonlinear nature of charging behaviors because (1) transportation networks exhibit pronounced spatiotemporal heterogeneity, and (2) charging stations are characterized by intricate, multi-scale interdependencies. To overcome these limitations, this study introduces a multimodal hypergraph-based charging demand forecasting framework. The proposed approach constructs multidimensional spatiotemporal hypergraphs from heterogeneous multimodal data, thereby uncovering higher-order correlations among charging stations across different spatial and temporal scales. By dynamically modeling these evolving inter-station relationships, the framework enhances the accuracy of charging demand prediction and provides a robust analytical foundation for the optimal planning and management of EV charging infrastructure. Building upon the proposed methodology, this study validates its effectiveness using a real-world EV charging demand dataset collected from an urban area and further develops an intelligent charging station operation system. Extensive experiments and ablation analyses demonstrate that the proposed multimodal hypergraph based framework consistently outperforms unimodal and conventional graph-based baselines, confirming its superior capability in capturing complex spatiotemporal dependencies and improving
forecasting accuracy.

Keywords: Charging Demand Forecasting, Multimodal, Hypergraph, Spatial-Temporal Data Mining

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