## 5 Conclusions  
Aimed at addressing the obvious discrepancies between the predicted and actual results when using existing wind speed prediction methods, in this study, we propose the MHSA-BiLSTM method, which incorporates multiscale decomposition and RIME optimization for short-term wind speed prediction. The MHSA-BiLSTM method aims to improve the power generation efficiency of wind farms as well as reduce the impact of the wind power output on the grid-connected stability. Through experimental analysis, the method proposed in this paper showed superior prediction performance on several experimental datasets. The main findings are summarized as follows:  
1. MHSA-BiLSTM, as the core of CEEMDAN-RIME-MHSA-BiLSTM, is good at extracting key information in long series and capturing different time-frequency scale information and complex dependencies in wind speed series, which greatly improves the prediction accuracy of the model. The experimental results show that the KRMSE, KMAE, and KMAPE values of the CEEMDAN-RIME-MHSA-BiLSTM are 0.33, 0.17, and 4.12%, respectively, which are 36% lower in the RMSE, 31% lower in the MAE, and 2.62% lower in the MAPE compared with the EMD-BiLSTM model. Compared with the CNN-BiLSTM model, the RMSE is reduced by 45%, the MAE by 38%, and the MAPE by 3.86%. Compared with the single-model GRU, the R\({}^{2}\) and NSE are improved by 28% and 36%, respectively. The combination of the RIME algorithm and CEEMDAN with MHSA-BiLSTM effectively reduces the complexity and improves the performance of long-time wind speed series prediction, which demonstrates the superiority of the combined model in short-term wind speed prediction;
2. The original wind speed sequence is decomposed using CEEMDAN, which decomposes the complex wind speed signal into multiple modal components with different frequency characteristics. When the prediction performance of CEEMDAN-BiLSTM is compared with that of the single-model BiLSTM, the \(R^{2}\) and \(NSE\) values are improved by 6% and 8%, and the CEEMDAN-BiLSTM exhibits better accuracy. This indicates that the decomposition strategy can improve the prediction accuracy in a limited way, which reduces the noise and nonlinear perturbations in the original data and improves the robustness of the model;
3. The RIME algorithm optimizes the pairwise hidden-layer nodes, the number of training times, and the learning rate of the long short-term memory network, which solves the limitations of the traditional BiLSTM model in the parameter selection and significantly improves the prediction performance of the model. According to the experimental results, the optimized BiLSTM model of the RIME algorithm is better than the single prediction model in terms of the RMSE and MAE, which verifies the hypothesis that the optimization algorithm can improve the prediction accuracy of the model;
4. CEEMDAN-RIME-MHSA-BiLSTM maintains a stable and highly accurate performance at different wind speeds in different geographic locations and at different time periods. The predictive performances of the KRMSE, KMAE, and KMAPE for the Iowa wind farm in the U.S. are 0.12, 0.08, and 1.09%, respectively, and the R\({}^{2}\) and NSE reach 0.99. The predictive performance of the model is greater than those of the other models for any one day in different seasons. As a result, the model proposed in this paper is highly applicable to wind speed prediction in scenarios with different geographical locations and time periods. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Training Set Length** & _RMSE_ & _MAE_ & _MAPE_ & \(R^{2}\) & _NSE_ \\ \hline
50\% & 0.37 & 0.23 & 4.72\% & 0.97 & 0.96 \\
60\% & 0.43 & 0.34 & 6.57\% & 0.94 & 0.94 \\
70\% & 0.33 & 0.17 & 4.12\% & 0.98 & 0.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Prediction results of the model under different training sets. In conclusion, the CEEMDAN-RIME-BiLSTM model proposed in this study showed superior predictive performance in the analysis and processing of wind farm data and achieved the objectives of this study.