## 4 Experimental Results and Analysis
### Validation of Wind Speed Prediction Algorithms
These models were tested on the same dataset, comparing the wind speed prediction metrics across different time periods and geographic locations. Table 2 lists the evaluation results of the abilities of the different models to predict wind speeds across various seasons. Table 3 compares the performances of the models in predicting wind speed intervals on different wind farms. The results show that the proposed model can stably predict wind speeds across different time periods and scenarios, demonstrating its reliability. In the following sections, we analyze the results for each scenario in detail. According to the vertical comparison of the wind speed prediction metrics on any day across different seasons in Table 2, the proposed model exhibits high accuracy. The R\({}^{2}\) values are 0.98, 0.99, 0.98, and 0.99, with errors consistently smaller than those of the other models. In the horizontal comparison of the metrics for a specific day across different seasons, all four models had good predictive performances. However, the proposed model had lower error metrics, with R\({}^{2}\) and NSE values closer to 1. The proposed model \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predictive Model** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** & **NSE** \\ \hline CEEMDAN-RIME-MHSA-BiLSTM & 0.33 & 0.17 & 4.12\% & 0.98 & 0.97 \\ CEEMDAN-BiLSTM & 0.72 & 0.57 & 8.33\% & 0.88 & 0.89 \\ RIME-BiLSTM & 0.48 & 0.38 & 5.72\% & 0.95 & 0.94 \\ BiLSTM & 0.92 & 0.74 & 11.97\% & 0.82 & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation indices obtained in ablation experiments. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **Predictive Model** & **RMSE** & **MAE** & **MAPE** & **R\({}^{2}\)** & **NSE** \\ \hline \multirow{4}{*}{March 20 th (Spring)} & CEEMDAN-RIME-MHSA-BiLSTM & 0.33 & 0.17 & 4.12\% & 0.98 & 0.97 \\  & EMD-BiLSTM & 0.69 & 0.48 & 6.74\% & 0.87 & 0.86 \\  & CNN-BiLSTM & 0.78 & 0.55 & 7.98\% & 0.85 & 0.83 \\  & GRU & 1.42 & 0.85 & 12.19\% & 0.70 & 0.61 \\ \hline \multirow{4}{*}{July 9 th (Summer)} & CEEMDAN-RIME-MHSA-BiLSTM & 0.19 & 0.12 & 2.10\% & 0.99 & 0.98 \\  & EMD-BiLSTM & 0.64 & 0.45 & 6.43\% & 0.90 & 0.88 \\  & CNN-BiLSTM & 0.74 & 0.50 & 7.12\% & 0.86 & 0.83 \\  & GRU & 1.21 & 0.78 & 11.12\% & 0.72 & 0.65 \\ \hline \multirow{4}{*}{November 15 th (Fall)} & CEEMDAN-RIME-MHSA-BiLSTM & 0.27 & 0.15 & 2.74\% & 0.98 & 0.97 \\  & EMD-BiLSTM & 0.71 & 0.51 & 6.92\% & 0.88 & 0.86 \\  & CNN-BiLSTM & 0.83 & 0.60 & 9.12\% & 0.84 & 0.82 \\  & GRU & 1.92 & 1.10 & 15.11\% & 0.60 & 0.55 \\ \hline \multirow{4}{*}{December 25 th (Winter)} & CEEMDAN-RIME-MHSA-BiLSTM & 0.21 & 0.14 & 2.71\% & 0.99 & 0.98 \\  & EMD-BiLSTM & 0.71 & 0.50 & 6.94\% & 0.88 & 0.86 \\ \cline{1-1}  & CNN-BiLSTM & 0.75 & 0.55 & 8.13\% & 0.85 & 0.83 \\ \cline{1-1}  & GRU & 1.33 & 0.94 & 12.58\% & 0.70 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation indicators for different models under the four seasons. consistently outperformed the others in all seasons, displaying a stronger generalization ability and stability, which confirms the superior predictive performance of the CEEMDAN-RIME-MHSA-BiLSTM model. The other wind speed prediction methods (EMD-BiLSTM, CNN-BiLSTM, and GRU), despite their better performances in certain linear time series predictions, often face the problems of weak model feature capture, low accuracy, and poor generalization when dealing with highly nonlinear and volatile time series data such as wind speed data. The model proposed in this paper effectively overcomes these problems through multiscale decomposition and the MHSA, which is capable of discovering potential patterns and dependencies in complex wind speed data and not only captures local features effectively but also focuses on global information at the same time.