SA-STGCN: A Spectral-Attentive Spatio-Temporal Graph Convolutional Network for Wind Power Forecasting with Wavelet-Enhanced Multi-Scale Learning

Yakai Yang, Zhenqing Liu () and Zhongze Yu
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Yakai Yang: China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China
Zhenqing Liu: China-EU Institute for Clean and Renewable Energy, Huazhong University of Science and Technology, Wuhan 430074, China
Zhongze Yu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2025, vol. 18, issue 19, 1-20

Abstract: Wind power forecasting remains a major challenge for renewable energy integration, as conventional models often perform poorly when confronted with complex atmospheric dynamics. This study addresses the problem by developing a Spectral-Attentive Spatio-Temporal Graph Convolutional Network (SA-STGCN) designed to capture the intricate temporal and spatial dependencies of wind systems. The approach first applies wavelet transform decomposition to separate volatile wind signals into distinct frequency components, enabling more interpretable representation of rapidly changing conditions. A dynamic temporal attention mechanism is then employed to adaptively identify historical patterns that are most relevant for prediction, moving beyond the fixed temporal windows used in many existing methods. In addition, spectral graph convolution is conducted in the frequency domain to capture farm-wide spatial correlations, thereby modeling long-range atmospheric interactions that conventional localized methods overlook. Although this design increases computational complexity, it proves critical for representing wind variability. Evaluation on real-world datasets demonstrates that SA-STGCN achieves substantial accuracy improvements, with a mean absolute error of 1.52 and a root mean square error of 2.31. These results suggest that embracing more expressive architectures can yield reliable forecasting performance, supporting the stable integration of wind power into modern energy systems.

Keywords: wind power forecasting; spatio-temporal learning; wavelet transform; spectral graph convolution; temporal attention (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: 2025
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