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Open Access

ComPact: Edge Collaborative Spatiotemporal Graph Learning for Wind Speed Forecasting

Zaigang Gong1, Siyu Chen1, Qiangsheng Dai2, Ying Feng1, Jinghui Zhang3( )
State Grid Yangzhou Power Supply Company, Yangzhou 225009, China
State Grid Jiangsu Electric Power Co. Ltd., Nanjing 210000, China
School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
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Abstract

In edge-distributed environments, spatiotemporal graphs provide a promising solution for capturing the complex dependencies among nodes and edges necessary for accurate wind speed forecasting. These dependencies involve spatial and temporal interactions that are crucial for modeling dynamic weather patterns. However, challenges, such as effectively maintaining spatial dependency information across spatiotemporal subgraphs, can lead to reduced prediction accuracy. Additionally, managing high communication costs, associated with the need for frequent and intensive data exchanges required for real-time forecasting across distributed nodes, poses significant hurdles. To address these issues, we propose graph coarsening-based cross-subgraph message passing with edge collaboration training mechanism (namely ComPact), a novel approach that simplifies graph structures through graph coarsening while preserving essential spatiotemporal dependencies. This coarsening process minimizes communication overhead and enables effective cross-subgraph message passing, capturing both local and long-range dependencies. ComPact further leverages hierarchical graph learning and structured edge collaboration to integrate global information into local subgraphs, enhancing predictive performance. Experimental validation on large-scale datasets, primarily the WindPower dataset, demonstrates ComPact’s superiority in wind speed forecasting, with up to a 31.82% reduction in Mean Absolute Error (MAE) and 11.8% lower in Mean Absolute Percentage Error (MAPE) compared to federated learning baselines.

Keywords

edge computingcross-subgraph message passingwind speed predictionhierarchical graph learning

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Tsinghua Science and Technology
Volume 30 Issue 5,
October 2025
Pages 2320-2341
DOI: 10.26599/TST.2024.9010261
Cite this article:
Gong Z, Chen S, Dai Q, et al. ComPact: Edge Collaborative Spatiotemporal Graph Learning for Wind Speed Forecasting. Tsinghua Science and Technology, 2025, 30(5): 2320-2341. https://doi.org/10.26599/TST.2024.9010261

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Received: 02 December 2024
Revised: 15 December 2024
Accepted: 24 December 2024
Published: 29 April 2025
© The Author(s) 2025.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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