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Short-term power prediction of hybrid wind power generation

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  • LiMing Wei
  • Yuan Li

Abstract

Accurate wind power prediction is crucial for maintaining the stability of power systems during large-scale wind power grid integration. In this paper, the generalization ability and nonlinear prediction advantages of Long Short-Term Memory networks are combined with the data-smoothing characteristics of Grey Models for short-term wind power prediction. Experimental tests show that the improved method achieves an average R2 value of 82.47% in 24-h, 72-h, and 5-day wind power prediction tasks, effectively improving prediction accuracy and reducing prediction errors.

Suggested Citation

  • LiMing Wei & Yuan Li, 2025. "Short-term power prediction of hybrid wind power generation," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 1855-1864.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:1855-1864.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf121
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