RESEARCH ON SHORT-TERM WIND TURBINE POWER FORECASTING BASED ON VMD AND MULTI-TIME SCALE CLASSIFICATION FORECASTING

Xia Weiping; Deng Aidong; Xue Yuan; Bian Wenbin; Liu Yang; Liu Dongying

doi:10.19912/j.0254-0096.tynxb.2024-1466

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (12) : 554-563. DOI: 10.19912/j.0254-0096.tynxb.2024-1466

RESEARCH ON SHORT-TERM WIND TURBINE POWER FORECASTING BASED ON VMD AND MULTI-TIME SCALE CLASSIFICATION FORECASTING

Xia Weiping^1,2, Deng Aidong^1,2, Xue Yuan^1,2, Bian Wenbin^1,2, Liu Yang^1,2, Liu Dongying³

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Abstract

To enhance the accuracy of short-term wind turbine power forecasting, a forecasting model based on variational mode decomposition （VMD） is proposed. The model constructs a long-term trend forecasting module （LTTFM） and a short-term periodic forecasting module （STPFM） for multi-time scale classification forecasting. Firstly, considering the non-stationarity of the wind power sequence, the original power sequence is decomposed by VMD, and the intrinsic mode functions are categorized into low-frequency components, high-frequency components, and residuals based on their correlation. Then, according to the characteristics of each component, different forecasting modules are designed. Long-term trend forecasting module based on GRU is constructed for the forecasting of low-frequency components. Short-term periodic forecasting module containing dimensional transformation and 2D-CNN is constructed for the forecasting of high-frequency components and residuals. Finally, the final power is obtained by synthesizing the forecasting results of each component. The proposed model is verified using actual operation data from a wind turbine, and the results show that the mean absolute error （MAE） of the proposed model is reduced by 34.9%-44.1% compared to commonly used models, indicating that the proposed model has a higher forecasting accuracy.

Key words

wind power / forecasting / variational mode decomposition / recurrent neural network / convolutional neural network

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Xia Weiping, Deng Aidong, Xue Yuan, Bian Wenbin, Liu Yang, Liu Dongying. RESEARCH ON SHORT-TERM WIND TURBINE POWER FORECASTING BASED ON VMD AND MULTI-TIME SCALE CLASSIFICATION FORECASTING[J]. Acta Energiae Solaris Sinica. 2025, 46(12): 554-563 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1466

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