Pangu-Weather Driven Dual-Path Network with Dynamic Gated Attention for Ultra-Short-Term Wind Power Forecasting

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ID: 326252
2026
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Abstract
Abstract Conventional numerical weather prediction is limited by high computational cost and delayed availability, while single-path models cannot effectively integrate multi-scale historical power information with future meteorological drivers. To address these issues, this work proposes a dual-path ultra-short-term wind power forecasting method based on Pangu-Weather and dynamic gated attention fusion. The raw wind power series is decomposed using complete ensemble empirical mode decomposition with adaptive noise, and meteorological forecasts are generated by Pangu-Weather. In the proposed network, the power path uses extreme gradient boosting to model short-window historical power features. The meteorological path comprises a temporal convolutional network branch and a meteorological feature branch, which respectively extract long-range temporal features and future meteorological driving features. A dynamic gated attention fusion module is then designed to fuse the outputs of the two paths through step-wise adaptive weighting, inter-step dependency modeling, and residual correction. Experiments on a real-world 387.45 MW wind farm demonstrate that the proposed model achieves an root mean square error of 21.28 MW, outperforming the best baseline by 13.97% and surpassing several baseline models overall. Ablation studies further validate the necessity of the dual-path design and the dynamic fusion mechanism. These results demonstrate that AI-weather-driven forecasting and dual-path dynamic fusion can provide a promising low-latency meteorological-input strategy for ultra-short-term wind power forecasting.
Reference Key
openalex_W7204012957 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lejia Zhu, Yujia Zhang, Qiang Wang, Shuai Wang, Xinyu Gu, Kun Luo, Jianren Fan
Journal journal of modern power systems and clean energy
Year 2026
DOI
10.1093/ce/zkag053
URL
https://doi.org/10.1093/ce/zkag053
Keywords Keywords not found

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