An adaptive variational mode decomposition for wind power prediction using convolutional block attention deep learning network

Anbo Meng, Zhifeng Xie, Jianqiang Luo, Ying Zeng, Xuancong Xu, Yidian Li, Zhenbo Wu, Zhan Zhang, Jianbin Zhu, Zikang Xian, Chen Li, Baiping Yan and Hao Yin

Energy, 2023, vol. 282, issue C

Abstract: Random intermittency and high fluctuation limit the wind power prediction accuracy. Although current studies offer various solutions, the prediction accuracy and data fitting performance are not satisfactory during violently fluctuating periods. To address the issue, a novel hybrid model is proposed in this paper, which combines adaptive variational mode decomposition (VMD), temporal convolution network with convolutional block attention module (CBTCN), and gated recurrent unit (GRU). First, VMD is used for data decomposition. Due to the high fluctuation of wind power data, it is difficult to optimize parameters for VMD. Thus grey wolf crossover optimization algorithm (GSCSO) is proposed, which combines four optimization algorithms. Then, an important index, i.e., dynamic error entropy (DEE) is proposed as the fitness function to ensure decomposition integrity, mode complexity, and predictability of sub-sequences for the first time. Thereafter, by extracting deep temporal features with CBTCN, GRU is cascaded to further mine the temporal correlation of these features and predict the wind power. Multiple experiments are conducted, and the results demonstrate that the proposed hybrid model can track the peaks and troughs satisfactorily, especially during violently fluctuating periods. For instance, in three-step prediction, the RMSE reduces by over 40% compared with other advanced models.

Keywords: Wind power prediction; Convolutional block attention module; Dynamic error entropy; Temporal convolutional network; Variational mode decomposition (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:282:y:2023:i:c:s0360544223023393

DOI: 10.1016/j.energy.2023.128945

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