## 3 Results and Discussion  
All the above models were applied to a dataset after the preprocessing with different feature compositions. To train and test the models listed above, the preprocessed dataset was divided into two parts: a training set and a testing set. The shares of these parts were selected using a commonly used ratio, 80/20. To select hyperparameters, 30% of the training set was taken for validation. Since the goal was not to achieve maximum possible accuracy, but to study the influence of features, the simplest method for adjusting hyperparameters, random search, was used. Tables 3 and 4 show the RMSE (\(kW\)) and adjusted \(R^{2}\) obtained at the test set. Values of \(R^{2}\) are very close to the values of adjusted \(R^{2}\) because the number of features is very small relative to the test dataset size (according to Equation (6)). For comparison, the RMSE and adjusted \(R^{2}\), when using the theoretical curve, are 262.22 kW and 0.95962, respectively. The best results were obtained using CatBoost; therefore, for this model, the influence of features on the RMSE and the significance (importance) of features are shown in Figures 8 and 9. Experiments have demonstrated the advantages of ensemble models over others, highlighting the complexity of the dependencies between wind turbine power and various features. It should be noted that among the ensemble models, those using gradient boosting (GB, XGB, and CB) performed better than those using adaptive boosting (AB) or bagging (RF and ET). Removing the theoretical power as a feature has little impact on the best models. However, if this feature is present, the model will utilize it, as evident in Figure 10. As expected, wind speed is the most significant feature, but using wind speed alone significantly reduces accuracy. The hypothesis that incorporating binary features to account for seasonality would have an effect was not confirmed, as the significance of these features is very low. Conversely, the hypothesis about the benefits of using the actual power value \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**All Features**} & **All Except** & **All Except** & **All Except Features** & **All Except** & **Wind Speed** \\  & & **Theoretical** & **Previous Real** & **Based on Date and** & **Seasonal** & **Only** \\  & & **Power** & **Power** & **Time** & **Binary Features** & **Only** \\ \hline LR & 0.98016 & 0.96901 & 0.96251 & 0.98004 & 0.98007 & 0.87589 \\ SVR & 0.95486 & 0.94779 & 0.93428 & 0.97817 & 0.95307 & 0.96190 \\ kNN & 0.98955 & 0.98402 & 0.98997 & 0.98899 & 0.98970 & 0.96026 \\ DT & 0.98924 & 0.98883 & 0.97777 & 0.98684 & 0.98775 & 0.96130 \\ RF & 0.99135 & 0.99131 & 0.98307 & 0.98974 & 0.99074 & 0.96514 \\ ET & 0.99081 & 0.98881 & 0.98037 & 0.98936 & 0.99035 & 0.96592 \\ AB & 0.99081 & 0.99107 & 0.98919 & 0.98866 & 0.99080 & 0.78041 \\ GB & 0.99255 & 0.99238 & 0.98774 & 0.98931 & 0.99198 & 0.96253 \\ XGB & 0.99231 & 0.99248 & 0.98989 & 0.98869 & 0.99236 & 0.96616 \\ CB & 0.99246 & 0.99223 & 0.99099 & 0.98963 & 0.99200 & 0.96628 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Adjusted R\({}^{2}\) obtained at the test set. Figure 9: RMSE of CatBoost using different sets of features.