## 5 Conclusions  
This study presents a two-stage method to evaluate the performance of machine learning algorithms in wind energy production estimation and to increase the reliability of the estimation data. In the first stage, the dependent variable, energy production, was estimated using RF, GB, kNN, Tree, and LR algorithms. The ANOVA test analyzed the second stage's relationship between the calculated values and the independent variables. The RF algorithm had the lowest error rates (_MSE: 0.003, RMSE: 0.053_) and the highest R\({}^{2}\) value (0.988). The GB algorithm also showed similarly high performance (_MSE: 0.003, RMSE: 0.058, R\({}^{2}\): 0.985_), achieving better results than linear models. These findings show that nonlinear algorithms are more effective in complex processes such as wind energy production.  
This study focused on the ANOVA test to statistically evaluate the effects of independent variables on the dependent variable. Analyses of actual and estimated data revealed that _Ws_ and _Rs_ had the most substantial impact on energy production (_p_ < 0.050). In addition, factors such as \(t\) and _Nd_ were also found to be significant, but their effects were observed to be more limited than other variables. The data estimated by the RF and GB algorithms produced the values closest to the actual data in the ANOVA results, which showed that the accuracy and reliability of the estimates were high.  
A significant contribution of this study is that it verified the robustness of the estimation models with a statistical control mechanism. In this context, the ANOVA test was used as an effective tool to determine the accuracy of the dependent variable and the importance of the order of the independent variables. In the analyses conducted with the RF algorithm, the R\({}^{2}\) value was 88.83%, and the ANOVA analysis result showed that the effects between the independent variables were significant. This finding increases the model's reliability and offers a new methodological approach to energy production estimates.  
Future studies should include the expansion of this method by including a more significant number and variety of independent variables in the analysis. In addition, different periods and seasonal effects should be considered in temporal analyses to further increase forecast models' accuracy. In addition to machine learning algorithms, deep learning approaches should be integrated into such studies to improve the accuracy and robustness of forecasts. Such developments will contribute to developing more efficient and integrated strategies for renewable energy production.  
Conceptualization, A.A. and Y.A.A.; methodology, A.A.; software, Y.A.A.; validation, A.A. and Y.A.A.; formal analysis, Y.A.A.; investigation, A.A.; resources, A.A.; data curation, Y.A.A.; writing--original draft preparation, A.A.; writing--review and editing, Y.A.A.; visualization, A.A. and Y.A.A.; supervision, Y.A.A.; project administration, A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The authors declare no conflicts of interest.