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

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 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. •GSCSO algorithm is proposed to optimize VMD decomposition parameters.•DEE is proposed to fill the gap of evaluation indicators for time-series decomposition processing.•CBTCN model is proposed to extract potential deep coupling temporal features from wind power data. Cascaded GRU network is used to further mine temporal correlation of extracted information.•The proposed hybrid model outperforms other state-of-the-art methods.