The IVMD-CNN-GRU-Attention Model for Wind Power Prediction With Sample Entropy Fusion
Dongfang Ren, Jiaqing Ma, Hongjv Liu, Yongjie Li, Changsheng Chen, Tao Qin, Zhiqin He, Qinmu Wu
Abstract
To enhance the accuracy of wind power prediction, we introduce a novel prediction method: the IVMD-CNN-GRU-Attention model integrated with sample entropy fusion. This approach initially decomposes the raw wind power series using tailored indicators <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Gamma $ </tex-math></inline-formula>, subsequently classifying and inputting the decomposed sub-modes, based on their central frequencies and sample entropies, into a hybrid model comprising CNN, GRU, and an attention mechanism. This innovative model was rigorously tested on SCADA data from a Chinese wind farm, achieving remarkable improvements over existing methods. Specifically, average enhancements of 12.06% in R2 score, 59.43% reduction in MAE, 52.04% reduction in RMSE, and 48.40% reduction in MAPE were observed. These substantial outcomes demonstrate that our method significantly enhances wind power prediction accuracy, thereby contributing to the advancement of the wind energy industry and ensuring stable power grid operation.