# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 5 Detailed Analysis of Predictability Performance and Model Validation
### Prediction Results Analysis  
Figure 17 presents the wind speed prediction results at the K1005 station across various models. MRIformer, incorporating wind speed data from meteorological stations and a predictability analysis, delivers the most accurate predictions, with the smallest RMSE and \(nMAE\) values compared to all other models. As shown in Table 6, the RMSE values for wind speeds at the K1005, K1066, K1245, and K1256 stations are 0.1815, 0.1847, 0.1859, and 0.1838, respectively. The basic model, which lacks any additional processing, yields the poorest results. This is primarily due to the limited information contained within the single historical wind speed sequence, which restricts the model's performance. However, when the wind speeds of surrounding stations are included, the prediction accuracy improves. Likewise, incorporating a predictability analysis and processing previously unrelated data further enhance prediction accuracy.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **Parameter** & **Value** \\ \hline MRIformer & Number of MRI block & 3 \\  & Number of layers of the tree structure & 2 \\  & Learning rate & 0.0002 \\  & Batch size & 32 \\  & Dropout & 0.3 \\  & Training epochs & 100 \\  & Optimizer & Adam \\ \hline \end{tabular}
\end{table}
Table 5: _Cont._The predictability curve in Figure 18 further demonstrates the effectiveness of the proposed method in improving wind speed prediction. Compared to its incomplete version, the proposed method shows a substantial increase in \(PR\) index across various input durations. Additionally, the duration of forecast input data influences the predictability performance. The predictability metric helps identify the optimal forecast input duration. Specifically, when predicting wind speed at the four stations, the best prediction accuracy is achieved by using forecast inputs of 8 h, 40 h, 14 h, and 54 h, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pre-Operation**} & \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**K1005**} & \multicolumn{2}{c}{**K1066**} & \multicolumn{2}{c}{**K1245**} & \multicolumn{2}{c}{**K1256**} \\  & & _RMSE_ & _nMAE_ & _RMSE_ & _nMAE_ & _RMSE_ & _nMAE_ & _RMSE_ & _nMAE_ \\ \hline \multirow{4}{*}{None} & TCN & 0.4528 & 0.3595 & 0.5449 & 0.4147 & 0.5130 & 0.4090 & 0.4805 & 0.3895 \\  & LSTM & 0.4209 & 0.3382 & 0.4977 & 0.3719 & 0.4960 & 0.3860 & 0.4642 & 0.3632 \\  & Informer & 0.3747 & 0.2950 & 0.4439 & 0.3488 & 0.4536 & 0.3625 & 0.4144 & 0.3273 \\  & MRIformer & 0.3683 & 0.2914 & 0.4379 & 0.3373 & 0.4327 & 0.3436 & 0.3943 & 0.3144 \\ \hline \multirow{4}{*}{Join WS wind speed} & TCN & 0.4266 & 0.3356 & 0.4817 & 0.3730 & 0.4323 & 0.3415 & 0.4136 & 0.3254 \\  & LSTM & 0.3975 & 0.3095 & 0.4689 & 0.3598 & 0.4233 & 0.3250 & 0.4037 & 0.3135 \\  & Informer & 0.3567 & 0.2609 & 0.3942 & 0.2994 & 0.4008 & 0.3047 & 0.3678 & 0.2803 \\  & MRIformer & 0.3423 & 0.2575 & 0.3921 & 0.2974 & 0.3972 & 0.2941 & 0.3560 & 0.2751 \\ \hline \multirow{4}{*}{Join WS wind speed} & TCN & 0.2930 & 0.2391 & 0.3136 & 0.2687 & 0.3423 & 0.2645 & 0.3037 & 0.2657 \\  & LSTM & 0.2614 & 0.2186 & 0.2816 & 0.2343 & 0.3184 & 0.2549 & 0.3039 & 0.2699 \\ +Predictability Analysis & Informer & 0.2204 & 0.1697 & 0.2395 & 0.1740 & 0.2359 & 0.1850 & 0.2288 & 0.1698 \\  & MRIformer & 0.1815 & 0.1123 & 0.1847 & 0.1275 & 0.1859 & 0.1303 & 0.1838 & 0.1278 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of prediction results.  
Figure 17: Wind speed forecast results of K1005.