Improving the prediction accuracy of wind power is of great significance to ensure the safety of large-scale wind power integration and the efficient operation of energy systems. However, the inherent non-stationarity and strong randomness of wind power series bring great challenges to prediction, and the existing models are still insufficient in the depth and breadth of feature extraction. Therefore, this paper proposes a combined prediction model based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with empirical wavelet transform (EWT) decomposition data, bidirectional time series convolution network (BiTCN) and bidirectional gated recurrent unit (BiGRU) prediction. The hybrid exploration selection strategy and spiral ascent factor are used to improve the global and local search performance of the dream optimization algorithm (DOA), and to search the optimal hyperparameters of ICEEMDAN and the combined model. Aiming at the noise residual problem of high-frequency components in ICEEMDAN, EWT is used to perform secondary decomposition denoising on the high-frequency components with the heaviest noise. Subsequently, the processed components are input into the combined prediction network composed of BiTCN-BiGRU to synergistically capture the multi-scale local features and long-term bidirectional dependencies of the sequence. Finally, the prediction results of each component are aggregated to obtain the final prediction value. Through the verification of the data set in this paper, the ICEEMDAN-EWT and BiTCN-BiGRU models based on improved dream optimization algorithm (IDOA） optimization are reduced to 0.73 in the average absolute percentage error, and the average absolute error and root mean square error are reduced to 2.5 and 1.46, respectively, with high prediction accuracy.