## 4 Experimental Results and Analysis
### Validation of Wind Speed Prediction Algorithms  
After the CEEMDAN and reconstruction of the wind speed data, the BiLSTM model was used to predict the processed wind speed series. The specific configuration and validation process of the BiLSTM model are described below. The BiLSTM model uses a hidden-layer structure containing 25 neurons with a ReLU activation layer and a regression layer. The model was compiled with the mean square error (MSE) as the loss function, and the initial learning rate was set to 0.005. The number of training times (epoch) was set to 20, and an L2 regularization coefficient of 0.01 was used to prevent overfitting. During the training process, the RIME algorithm was used to adjust the learning rate to optimize the model performance. The RIME optimization curve is shown in Figure 9, which finds the optimal value at two iterations and converges rapidly. The trend of the RIME algorithm in different parameters is shown in Figure 10; all the parameters were stabilized after three iterations. To more deeply verify the improvement in the model accuracy via the multi-head self-attention mechanism and the RIME optimization ability on the model, ablation experiments were performed. The model prediction results are shown in Figure 11. The predictions of the hybrid models are better than those of the single BiLSTM model. Among the hybrid models, the prediction curve of the CEEMDAN-RIME-MHSA-BiLSTM model is closer to the real value, with a higher fit to the real value and a better prediction effect, demonstrating  
Figure 10: Variation in RIME hyperparameters with the number of iterations. Figure 9: RIME adaptation curve. that the MHSA-BiLSTM model combines the time decomposition capability of CEEMDAN with RIME optimization to effectively capture the dynamic properties and dependencies of the wind speed. A comparison of the prediction results was performed using evaluation indices, and the evaluation indices for each module of the proposed model are shown in Table 1. From Table 1, it can be seen that BiLSTM was selected as the underlying single model because of the improvement in the prediction performance by adding decomposition. After CEEMDAN, the evaluation indices of the CEEMDAN-BiLSTM model (\(K_{RMSE}\)) were reduced by 20%, the \(K_{MAE}\) was reduced by 17%, and the KMAPE was reduced by 3.64%. After the RIME optimization, the evaluation indices of the RIME-BiLSTM model (\(K_{RMSE}\)) decreased by 44%, the \(K_{MAE}\) decreased by 36%, and the KMAPE decreased by 6.25%. To minimize the error in the hybrid model, decomposition, optimization, and multi-head self-attention mechanisms were incorporated into the BiLSTM model. The optimization results show that the R\({}^{2}\) value of the proposed model improved by 10% and 3% compared to the other two hybrid models. Additionally, the prediction curve of this model is closer  
Figure 11: (**a**) BiLSTM prediction model; (**b**) CEEMDAN-BiLSTM prediction model; (**c**) RIME-BiLSTM prediction model; (**d**) CEEMDAN-RIME-MHSA-BiLSTM prediction model. to the true values, with the NSE increasing by 8% and 3%. Based on these metrics, it can be seen that the proposed combined model has the smallest error in the \(K_{RMSE}\), \(K_{MAE}\), and \(K_{MAPE}\) evaluation metrics and provides accurate and effective prediction results. The results show that RIME optimization and CEEMDAN have a significant effect on the prediction performance of the model. The best prediction results can only be obtained if both are introduced into the model at the same time. CEEMDAN reduces the complexity of the data through multiscale decomposition, while RIME optimization improves the accuracy of the model parameter selection. Both are essential for improving the accuracy of the model over a wide range of predictions. To further verify the prediction capability and robustness of the proposed CEEMDAN-RIME-BiLSTM model under diverse conditions, several hybrid models, including EMD-BiLSTM, CNN-LSTM, and the standalone GRU model, were constructed.