To improve wind power prediction accuracy, a combination model based on SSA-optimized Transformer-BiGRU is proposed. First, CEEMDAN decomposes the original sequence into multiple modal components and a residual component, reducing data complexity and instability. Then, a high-efficiency combined model is constructed by integrating the self-attention mechanism of the Transformer with the bidirectional time-series modeling capability of BiGRU. To address the challenge of hyperparameter optimization for the Transformer-BiGRU model, the SSA algorithm is introduced to optimize the hyperparameters, further enhancing prediction accuracy. Finally, using the Longyuan Electric Power wind power prediction dataset, comparative and ablation experiments are conducted to show that the proposed model outperforms other traditional models and demonstrates the effectiveness of each component. The experimental results indicate that the method achieves an R2 of 0.981 0.