## 4 Case Study
### Wind Power Forecasting Results  
#### 4.3.1 Comparison of Power Forecasting Results of Different Models  
In this study, LSTM, BP, Random Forest, Transformer, and Ns-Transformer are used for wind power forecasting, with separate training and forecasting for data from the cold-wave  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Random Forest & 0.9863 & 0.8069 & 0.8540 & 0.8300 \\ SVM & 0.9823 & 0.8100 & 0.7153 & 0.7600 \\ KNN & 0.9899 & 0.8500 & 0.9051 & 0.8700 \\ MLP & 0.9926 & 0.8936 & 0.9198 & 0.9000 \\ LSTM & 0.9951 & 0.9412 & 0.9344 & 0.9300 \\ U-Net & 0.9971 & 0.9568 & 0.9731 & 0.9600 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation of classification results of generated data.  
and non-cold-wave seasons. The evaluation metrics for the forecasting results are presented in Table 6. In the non-cold-wave season, the Ns-Transformer model proposed in this paper achieves reductions of 3.82%, 4.60%, 7.37%, and 0.39% in RMSE compared to the LSTM, BP, Random Forest, and Transformer models. During the non-cold-wave season, due to relatively small weather fluctuations, both the Transformer and Ns-Transformer models effectively predict the power change trend based on the input features. The forecasting results of the five models during the cold-wave-free season are shown in Figure 13, where the differences in model performance can be visually observed.  
However, for the forecasting results in the cold wave season, the performance of the LSTM, Random Forest, Transformer, and the proposed model decreases. The RMSE in the cold wave season is 3.98%, 3.33%, 2.40%, and 2.35% higher than that in the non-cold-wave season, respectively. The occurrence of cold wave events not only brings drastic fluctuations to wind power but also reduces the correlation between wind speed and other factors, thereby decreasing forecasting accuracy. Through comparative analysis, it is evident that the proposed method maintains good forecasting accuracy under cold-wave conditions, with its RMSE increase being significantly lower than that of other methods, making it more suitable for wind power forecasting during cold wave events. In the cold wave season, LSTM, BP, and Random Forest models fail to accurately capture power changes during cold wave events, leading to forecasting power values that are generally higher than the actual power during the cold wave. The cold wave period marked in Figure 14 shows the forecasting of different models for the cold wave. In contrast, the Transformer model achieves better forecasting accuracy for relatively stable power changes but cannot identify the fluctuation trend during severe wind power fluctuations in cold wave events. The  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Cold Wave Season**} & \multicolumn{2}{c}{**Non-Cold Wave Season**} \\ \cline{2-5}
**Model** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline LSTM & 0.1642 & 0.1144 & 0.1244 & 0.0935 \\ Random Forest & 0.1635 & 0.1189 & 0.1302 & 0.1042 \\ BP & 0.1558 & 0.1100 & 0.1579 & 0.1229 \\ Transformer & 0.1121 & 0.0854 & 0.0881 & 0.0785 \\ Proposed Method & 0.1077 & 0.0849 & 0.0842 & 0.0692 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation metrics for the prediction results of different models.  
Figure 13: Forecasting results for the non-cold-wave season.  
Ns-Transformer model, which introduces de-stationary attention based on the Transformer, provides excellent forecasting results even under large fluctuations in wind power.