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Two-stage ultra-short-term wind power forecasting based on multi-scale wind process extraction and fluctuation continuation analysis

Author

Listed:
  • Liu, Xiaoyan
  • Zhen, Zhao
  • Mi, Zengqiang
  • Hao, Ling
  • Xu, Fei
  • Wang, Fei

Abstract

Wind power fluctuations are influenced by multi-scale turbulence interactions, imparting complex dynamics. The non-stationarity of such fluctuations poses challenges in regional wind power forecasting (RWPF), particularly in extracting multi-scale temporal features with clear physical interpretations to effectively model dynamic spatiotemporal relationships among wind farms. In addition, the continuation scale of wind power fluctuations changes over time, resulting in varying forecasting error patterns. Error correction strategies based on fluctuation continuation analysis remain underdeveloped, which further limits the accuracy of RWPF. Therefore, this paper proposes a two-stage ultra-short-term RWPF model that integrates multi-scale wind process fluctuation feature extraction and fluctuation continuation analysis. Firstly, a Gaussian-Holt model is proposed to fit wind power fluctuations by accounting for multiple disturbance/baseline wind processes, enabling the extraction of temporal features at multiple levels. Based on the extracted features, temporal correlations among wind farms are analyzed, and spatial topology is leveraged to construct dynamic spatiotemporal graph structures. The constructed graph structures are fed into STGformer to produce the initial wind power forecasts. Secondly, representative continuation scenarios of wind power fluctuations are identified through a quantitative analysis of how long the current fluctuation state is likely to persist, which supports the design of tailored error correction strategies. Finally, the forecasting error corrections from different scenarios are weighted and combined with the initial forecasts to produce the final wind power forecasts. Validation on real-world data from nine wind farms in Xinjiang demonstrates that the proposed model significantly improves forecasting accuracy and error correction effectiveness compared to existing approaches.

Suggested Citation

  • Liu, Xiaoyan & Zhen, Zhao & Mi, Zengqiang & Hao, Ling & Xu, Fei & Wang, Fei, 2026. "Two-stage ultra-short-term wind power forecasting based on multi-scale wind process extraction and fluctuation continuation analysis," Energy, Elsevier, vol. 342(C).
    • Handle: RePEc:eee:energy:v:342:y:2026:i:c:s0360544225051485
    DOI: 10.1016/j.energy.2025.139506
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