HYBIRD MODEL BASED ON SSA-VMD PREPROCESSING OF TCN-INFORMER SHORT-TERM WIND SPEED PREDICTION

Kong Xianzheng; Huang Guoyong; Deng Weiquan; Liu Fabing

doi:10.19912/j.0254-0096.tynxb.2024-1806

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Acta Energiae Solaris Sinica ›› 2026, Vol. 47 ›› Issue (2) : 527-538. DOI: 10.19912/j.0254-0096.tynxb.2024-1806

HYBIRD MODEL BASED ON SSA-VMD PREPROCESSING OF TCN-INFORMER SHORT-TERM WIND SPEED PREDICTION

Kong Xianzheng¹, Huang Guoyong¹, Deng Weiquan¹, Liu Fabing²

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Abstract

To address the limited accuracy of multi-step wind speed predictions using conventional methods, this paper proposes a hybrid model that integrates Singular Spectrum Analysis （SSA） and Variational Mode Decomposition （VMD） with a Time Convolutional Network （TCN）-Informer architecture. Firstly, SSA is used to suppress noise in the original wind speed data and reduce its instability. Next, VMD is employed to reduce the complexity of the wind speed sequence, with each component then input into TCN's feature extraction module to capture temporal features and enhance local information representation. Finally, by fusing temporal and spatial features from each modal component and inputting them into Informer's self-attention model, long-term dependence relationships are modeled to obtain multi-step wind speed predictions. The proposed model was validated using measured wind speed data from a meteorological tower at a wind farm in Yunnan province, China. The results show that the MAPE for 6-step and 12-step predictions were only 1.63% and 2.25%, respectively, demonstrating significantly accuracy in short-term multi-step wind speed prediction.

Key words

wind power / prediction / deep learning / singular spectrum analysis / temporal convolutional networks / variational modal decomposition

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Kong Xianzheng, Huang Guoyong, Deng Weiquan, Liu Fabing. HYBIRD MODEL BASED ON SSA-VMD PREPROCESSING OF TCN-INFORMER SHORT-TERM WIND SPEED PREDICTION[J]. Acta Energiae Solaris Sinica. 2026, 47(2): 527-538 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1806

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