MULTI-WIND-FARM WIND SPEED PREDICTION BASED ON VMD FREQUENCY DIVISION AND GRU-INFORMER SYNERGISTIC MECHANISM

Liang Tao; Chen Rui; Mi Dabin; Jing Yanwei; Guo Yanxun; Zhang Erhui

doi:10.19912/j.0254-0096.tynxb.2025-0712

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (9) : 755-770. DOI: 10.19912/j.0254-0096.tynxb.2025-0712

MULTI-WIND-FARM WIND SPEED PREDICTION BASED ON VMD FREQUENCY DIVISION AND GRU-INFORMER SYNERGISTIC MECHANISM

Liang Tao¹, Chen Rui¹, Mi Dabin², Jing Yanwei³, Guo Yanxun⁴, Zhang Erhui⁴

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Abstract

To address the strong nonstationarity of wind speed time series, multi-scale variability, and cross-farm heterogeneity in centralized wind power control-center scenarios, this study proposes a hybrid wind speed forecasting model based on VMD-based frequency-band decomposition and a GRU-Informer synergistic mechanism. First, variational mode decomposition （VMD） is applied to wind speed and relevant meteorological variables from representative wind farms, and the decomposed modal components are reconstructed into low-frequency and high-frequency signals. A frequenly-band cooperative forecasting architecture is then developed. The gated recurrent unit （GRU） network is used to model low-frequency components with clear trend characteristics and long-term dependencies, while the Informer model is employed to capture complex fluctuations and long-range dependencies in high-frequency components. The forecasts from the two channels are subsequently reconstructed to obtain the baseline forecast for representative wind farms. Furthermore, to support multi-wind-farm forecasting under centralized management, a transfer learning strategy is introduced to transfer the learned source-domain models to target wind farms. A multi-objective coati optimization algorithm （MOCOA） is further used to adaptively fuse the forecasts of multiple source-domain expert models, thereby constructing a generalized forecasting model with improved adaptability and generalization capability. Experimental results show that the proposed hybrid model achieves high forecasting accuracy, strong environmental adaptability, and fast response in centralized wind power control applications, demonstrating its effectiveness and practical value.

Key words

wind speed / wind farm / variational mode decomposition / wind speed forecasting / frequency-band cooperative forecasting / gated recurrent unit / Informer / centralized wind power control center

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Liang Tao, Chen Rui, Mi Dabin, Jing Yanwei, Guo Yanxun, Zhang Erhui. MULTI-WIND-FARM WIND SPEED PREDICTION BASED ON VMD FREQUENCY DIVISION AND GRU-INFORMER SYNERGISTIC MECHANISM[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 755-770 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0712

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