## 3 Results  
The main findings of this study are presented within the framework of five basic steps. First, ANOVA analysis was applied to the raw data to evaluate the effect of independent variables on the dependent variable. This analysis was the starting point for understanding the general structure and relationships of the data. Then, the raw dataset was divided into training and test datasets, and the dependent variable was estimated using five different machine learning algorithms (RF, kNN, GB, LR, and Tree). This stage is essential to evaluate the prediction performance of independent variables on the dependent variable. In the following stages, descriptive statistics were calculated for the predictions obtained from the machine learning algorithms, and the characteristics of the prediction data were analyzed in detail. Then, the performance measurement metrics of the algorithmswere evaluated, and critical indicators such as accuracy, precision, and error rates of each algorithm were compared. Finally, ANOVA analysis was applied to the prediction data obtained from the machine learning algorithms, and the relationships between the algorithms and the independent variables were examined. This multi-faceted analysis approach not only evaluates the robustness and reliability of the prediction results but also contributes to a better understanding of the effect of independent variables on the prediction processes. According to Table 5, which includes ANOVA analysis, the effects of independent variables on the dependent variable are statistically significant (_p_-value < 0.05). All variables _Wd_, _Ws_, _Nd_, _Rs_, \(t\), _Wt_, and _Wf_ significantly affected the dependent variable. The highest F-value was observed in the Ws variable with 129,059.67, which reveals that wind speed is the most substantial influencing factor on the dependent variable. The variables _Rs_ and _Wf_ also show a highly significant effect with F-values of 2336.09 and 1640.98, respectively. On the other hand, although the F-value of the \(t\) variable (9.19) is lower than other variables, it is statistically significant. It is accepted to be effective on the dependent variable. In addition, the effects of the variables _Wt_ and _Wd_ are more moderate but significant, with F-values of 25.96 and 25.37, respectively. These results highlight the role of each independent variable in modeling the dependent variable, with particular emphasis on wind speed, rotor speed, and wind farm variables. This study used machine learning algorithms (RF, kNN, GB, LR, and Tree) to obtain prediction data. Training and testing stages for the prediction data obtained from machine learning algorithms were considered 80% and 20%, respectively. The forecast data based on the algorithms and the data of the actual observations selected for forming the forecast data are compared in Figure 2. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **DF** & **Adj SS** & **Adj MS** & **F-Value** & _p-Value_ \\ \hline _Wd_ (\({}^{\circ}\)) & 1.00 & 0.8000 & 0.83000 & 25.370000 & 0.0001 \\ _Ws_ (m/s) & 1.00 & 4228.3 & 4228.27 & 129,059.67 & 0.0002 \\ _Nd_ (\({}^{\circ}\)) & 1.00 & 2.9000 & 2.91000 & 88.820000 & 0.0001 \\ _Rs_ (s\({}^{-1}\)) & 1.00 & 76.500 & 76.5400 & 2336.0900 & 0.0001 \\ \(t\) (UTC) & 6238 & 1877.7 & 0.30000 & 9.1900000 & 0.0002 \\ _Wt_ & 9.00 & 7.7000 & 0.85000 & 25.960000 & 0.0001 \\ _Wf_ & 5.00 & 268.80 & 53.7600 & 1640.9800 & 0.0001 \\ \hline \end{tabular} Abbreviations: DF, degree of freedom; Adj SS, the adjusted sum of squares; Ad] MS, the adjusted mean of squares.  
\end{table} 
Table 5: ANOVA data of independent and dependent variables. Figure 2: Comparison of estimated and actual data of the selected data for the testing phase.