SHORT-TERM WIND POWER FORECASTING BASED ON VMD-MBWO-KELM

Chang Zhencheng; You Guodong; Xiao Ziyue; Lu Yuran; Liu Ruijun; Xi Zhongqi

doi:10.19912/j.0254-0096.tynxb.2024-0229

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (6) : 623-631. DOI: 10.19912/j.0254-0096.tynxb.2024-0229

SHORT-TERM WIND POWER FORECASTING BASED ON VMD-MBWO-KELM

Chang Zhencheng, You Guodong, Xiao Ziyue, Lu Yuran, Liu Ruijun, Xi Zhongqi

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Abstract

Aiming at the problem of strong intermittency and volatility and low prediction accuracy of wind power, this paper proposes a short-term wind power prediction model based on Beluga whale optimization algorithm based on multi-strategy fusion （MBWO）, Variational mode decomposition （VMD）, and Kernel extreme learning machine （KELM） prediction model. Firstly, the original wind power sequence is smoothed by VMD and the MBWO-KELM model is constructed. secondly, the decomposed subsequences are input into the MBWO-KELM model for prediction. Finaly, the different subsequences are reconstructed to obtain the final wind power prediction. The results show that the prediction accuracy and stability of the model are significantly better than other models under different seasons, and the MAPE values are all controlled below 6%, which can improve the utilization efficiency of wind power energy.

Key words

wind power / variational modal decomposition / forecasting / adaptive algorithms / kernel extreme learning machine

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Chang Zhencheng, You Guodong, Xiao Ziyue, Lu Yuran, Liu Ruijun, Xi Zhongqi. SHORT-TERM WIND POWER FORECASTING BASED ON VMD-MBWO-KELM[J]. Acta Energiae Solaris Sinica. 2025, 46(6): 623-631 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0229

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