DBANN: Dual-Branch Attention Neural Networks with hierarchical spatiotemporal-perception for multi-node offshore wind power forecasting

Dan Hu, Fengquan He, Wei Fan and Wenlin Feng

Energy, 2025, vol. 334, issue C

Abstract: Precise forecasting of offshore wind power is the basic guarantee for ensuring power grid operation stability and optimizing energy management. Nevertheless, existing approaches struggle to balance multi-scale temporal characteristics, unstructured spatial dependencies, and intertwined spatiotemporal interactions. This study proposed the novel Dual-Branch Attention Neural Networks (DBANN) framework, including a Spatial Branch for capturing spatial correlations, a Temporal Branch for extracting hierarchical features, and a Spatiotemporal Fusion Module to integrate them. Experiments on real-world offshore wind farm data provided evidence for the proposed DBANN model's enhanced performance relative to SOTA models, achieving a 48.67 % reduction in RMSE, 40.30 % in MAE, and 52.85 % in MAPE. Ablation studies validate the contributions of multi-resolution convolutional kernels, dual-graph structures, and feature fusion mechanisms. Moreover, the parallel architecture outperforms cross-configurations, underscoring the significance of independent yet complementary extraction of the spatiotemporal feature.

Keywords: Offshore wind power forecasting; Deep learning; Spatiotemporal features; Graph convolutional networks; Multiresolution convolutional neural networks (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:334:y:2025:i:c:s0360544225031639

DOI: 10.1016/j.energy.2025.137521

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