SHORT-TERM WIND SPEED PREDICTION BASED ON TIMEMIXER AND MVMD OPTIMIZED BY IMPROVED SPARROW SEARCH ALGORITHM

Xiong Xiaofeng; Jia Shihui; Chi Xiaoni; Li Gaoxi

doi:10.19912/j.0254-0096.tynxb.2025-0308

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (7) : 58-69. DOI: 10.19912/j.0254-0096.tynxb.2025-0308

SHORT-TERM WIND SPEED PREDICTION BASED ON TIMEMIXER AND MVMD OPTIMIZED BY IMPROVED SPARROW SEARCH ALGORITHM

Xiong Xiaofeng^1,2, Jia Shihui^1,2, Chi Xiaoni³, Li Gaoxi⁴

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Abstract

To address the problem of low wind speed prediction accuracy caused by the inherent randomness and volatility of wind speed, this study proposes a hybrid prediction method based on multivariate variational mode decomposition (MVMD） with parameters optimized by an improved sparrow search algorithm (ISSA） and the Timemixer model. First, data variables are reduced in dimensionality using the maximum information coefficient (MIC） and the random forest algorithm. Then, the ISSA, which incorporates Logistic-Tent chaotic mapping and a golden sine strategy, is utilized to automatically optimize the number of modes and the penalty coefficient in MVMD. The optimized MVMD decomposes the data into multiple intrinsic mode functions (IMFs）, and each IMF is individually predicted using the TimeMixer model. Finally, the IMF predictions are aggregated to produce the final wind speed forecast. Experimental results on two datasets demonstrate that, compared with other prediction models, the proposed model achieves higher prediction accuracy and exhibits good performance in wind speed forecasting.

Key words

wind speed / wind power generation / feature selection / prediction model / multivariate variational mode decomposition / improved sparrow search algorithm / Timemixer

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Xiong Xiaofeng, Jia Shihui, Chi Xiaoni, Li Gaoxi. SHORT-TERM WIND SPEED PREDICTION BASED ON TIMEMIXER AND MVMD OPTIMIZED BY IMPROVED SPARROW SEARCH ALGORITHM[J]. Acta Energiae Solaris Sinica. 2026, 47(7): 58-69 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0308

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