Abstract
Wind power generation data exhibits non-periodic and non-stationary characteristics coupled with significant noise levels, posing challenges for conventional forecasting models. Existing time series prediction techniques struggle to handle the instability, high sampling frequencies, and inherent noise present in wind power data. To address these issues, we propose a novel Multiscale Cross Interaction Graph Neural Network with a Frequency-Enhanced Channel Attention Mechanism (CrossGFA). The CrossGFA effectively captures wind power trends across multiple scales via cross-scale GNN modules while reducing noise. Simultaneously, the cross-variable GNN component leverages both homogeneity and heterogeneity among variables, enhancing the detection of potential associations between different wind power characteristics. Furthermore, the frequency-enhanced channel attention mechanism complements the GNN framework by mitigating frequency domain noise. Extensive evaluations on four real-world wind power station datasets demonstrate that CrossGFA outperforms state-of-the-art time series forecasting methods, validating its effectiveness in handling the complexities of wind power data.
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The data and code used in this article are stored at the following website: https://github.com/ZUEL-hyzhang/CrossGFA.
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Funding
This work was supported by the National Natural Science Foundation of China (52405572), and by the Hubei Province Emergency Capacity and Safety Production Special Fund (SJZX20230906) and by the Base Platform Construction Project of Zhongnan University of Economics and Law (2722024EJ038). The authors are grateful to other participants of the project for their cooperation.
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conceptualization, H.Z.; methodology, H.Z.; formal analysis, D.W.; data curation, D.W.; supervision, X.J.; writing—original draft preparation, H.Z.; writing—review and editing, X.J. All authors have read and agreed to the published version of the manuscript.
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Zhang, H., Wang, D. & Jiang, X. CrossGFA: wind power prediction with a multi-scale cross-graph network via a Frequency-Enhanced Channel attention mechanism. Appl Intell 55, 48 (2025). https://doi.org/10.1007/s10489-024-05863-7
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DOI: https://doi.org/10.1007/s10489-024-05863-7