RESEARCH ON ULTRA-SHORT-TERM WIND POWER FORECAST BASED ON AVMD-CNN-GRU-Attention

Ren Dongfang; Ma Jiaqing; He Zhiqin; Wu Qinmu

doi:10.19912/j.0254-0096.tynxb.2023-0146

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (6) : 436-443. DOI: 10.19912/j.0254-0096.tynxb.2023-0146

RESEARCH ON ULTRA-SHORT-TERM WIND POWER FORECAST BASED ON AVMD-CNN-GRU-Attention

Ren Dongfang, Ma Jiaqing, He Zhiqin, Wu Qinmu

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Abstract

In order to improve the forecast accuracy of ultra-short-term wind power, an improved ultra-short-term wind power forecast model based on variational mode decomposition convolutional neural network （AVMD-CNN）, gated recurrent unit （GRU） and attention mechanism （Attention） is proposed. Firstly, the wind power sequence is decomposed into K sub-modes by using the improved VMD. Then, each sub-mode is classified by sample entropy （SE） and center frequency. According to the classification results, each sub-mode is given a normalization method, and input into GRU-Attention and CNN-GRU-Attention models for training and forecasting according to SE values. Finally, the final results are obtained by superimposing the forecast results of each sub-mode, so as to complete the ultra-short-term wind power forecast. Using the determination coefficient（R²）, mean absolute error （MAE）, root mean square error （RMSE）, and mean absolute percentage error （MAPE） as the accuracy assessment indexes, the actual arithmetic examples show that the R² of the proposed model is improved by 12.06% on average compared with other methods, and the MAE, RMSE, and MAPE are reduced by 59.36%, 62.49%, and 48.34% respectively, with high prediction accuracy.

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wind power / forecasting / variational mode decomposition / convolutional neural network / attention mechanism / sample entropy

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Ren Dongfang, Ma Jiaqing, He Zhiqin, Wu Qinmu. RESEARCH ON ULTRA-SHORT-TERM WIND POWER FORECAST BASED ON AVMD-CNN-GRU-Attention[J]. Acta Energiae Solaris Sinica. 2024, 45(6): 436-443 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0146

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