Short-term power prediction of hybrid wind power generation

LiMing Wei and Yuan Li

International Journal of Low-Carbon Technologies, 2025, vol. 20, 1855-1864

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.

Keywords: GM model; LSTM neural network model; short-term power prediction of wind power generation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:20:y:2025:i::p:1855-1864.

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