ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON SAMPLE ENTROPY AND CNN-MGM HYBRID MODEL

Zhang Nan; Zhu Yongqi; Zheng Chuang; Sun Na; Xue Xiaoming

doi:10.19912/j.0254-0096.tynxb.2024-1646

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (1) : 645-653. DOI: 10.19912/j.0254-0096.tynxb.2024-1646

ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON SAMPLE ENTROPY AND CNN-MGM HYBRID MODEL

Zhang Nan^1,2, Zhu Yongqi¹, Zheng Chuang¹, Sun Na^1,3, Xue Xiaoming¹

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Abstract

A hybrid wind speed prediction model is proposed to address the instability and intermittency of wind speed, which integrates convolutional neural networks （CNN）,minimum gated storage networks （MGM）, whale optimization algorithms （WOA）, and time-varying filter based empirical mode decomposition （TVFEMD）. TVFEMD is used decomposition to decompose the original wind speed data into multiple subsequences. Guided by sample entropy, use TVFEMD to decompose the sequence with the highest complexity twice. Then, the decomposed subsequences are input into the WOA algorithm optimized hybrid model for prediction, and the prediction results of each subsequence are obtained to produce the final result. The experimental results show that compared with other models, the average absolute error of the proposed model has decreased by 2.3%-8.6%, and the same effect has been achieved in different datasets, verifying the effectiveness of the mixed model in wind speed prediction.

Key words

wind speed prediction / minimum gated memory networle / variational mode decomposition / mixed prediction model / whale optimization algorithm

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Zhang Nan, Zhu Yongqi, Zheng Chuang, Sun Na, Xue Xiaoming. ULTRA-SHORT-TERM WIND SPEED PREDICTION BASED ON SAMPLE ENTROPY AND CNN-MGM HYBRID MODEL[J]. Acta Energiae Solaris Sinica. 2026, 47(1): 645-653 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1646

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