## 4 Discussion  
This study used machine learning algorithms and statistical methods to investigate the effects of time, environmental, technical, and locational variables on the dependent variable for wind energy production. ANOVA analysis performed on raw data revealed that all independent variables significantly affected the dependent variable (\(p<0.05\)). In particular, wind speed (\(Ws\), \(F=129\),\(059\),\(67\)) and rotor speed (\(Rs\), \(F=2336\),\(09\)) showed the most substantial effects, emphasizing the importance of speed and rotation factors in wind energy production. In addition, the effects of \(Nd\) and \(t\) variables were also found to be significant but at relatively lower levels. The prediction data based on machine learning algorithms exhibited a similar distribution to the actual data. Descriptive statistics showed that all models produced close predictions for the dependent variable. While the mean values were around 0.385 MW for RF, GB, and Tree algorithms, the LR model's mean prediction value was slightly higher at 0.386 MW. However, the LR model's lower standard deviation and variance values reveal that the estimates show less variability but do not sufficiently reflect the effects of the independent variables. The performance measurement values of the algorithms showed that the RF and GB models had the lowest error rates (\(MSE=0.003\), \(RMSE=0.053\) for RF; \(MSE=0.003\), \(RMSE=0.058\) for GB). These two models also proved their estimation success with high R\({}^{2}\) values (\(RF\): \(R^{2}=0.988\), \(GB\): \(R^{2}=0.985\)). On the other hand, the kNN algorithm attracted attention with relatively higher error rates (\(MSE=0.007\), \(RMSE=0.082\)), while the LR model was distinguished from the other models with a low R\({}^{2}\) value (\(R^{2}=0.808\)). These results reveal that nonlinear models are more suitable for analyzing complex systems such as wind energy production. Pareto charts provided the opportunity to visually evaluate the effects of independent variables on the dependent variable. Was and Rs variables were consistently among the strongest effects in both actual and predicted data. While RF, GB, and Tree algorithms captured these effects correctly, it was observed that the LR model needed to be revised to reflect the impact of independent variables. In particular, the effect of the \(t\) variable was found to be relatively low in the GB model (\(p=0.050\)), indicating that the model was limited in evaluating the time factor of other variables. The reliability rates of machine learning and ANOVA tests, as well as the prioritization of the effect power of independent variables on dependent variables, are given in Table 10. The table compares the effects of different machine learning algorithms (RF, GB, kNN, Tree, and LR) on dependent variables with R\({}^{2}\) and ANOVA results. When looking at the R\({}^{2}\) values, the RF algorithm has the highest accuracy of 0.988, while the LR algorithm shows the lowest R\({}^{2}\) value of 0.808. This indicates that algorithms such as RF and GB predict the dependent variables of the model better, while the LR algorithm provides lower accuracy. The highest ANOVA R\({}^{2}\) value was obtained at 89.30% in the GB algorithm, while the lowest was 88.36% in the LR algorithm. These results reveal that machine learning algorithms show higher performance in model accuracy, while ANOVA plays a vital role in determining the effect power of independent variables. ANOVA tests performed with predicted data provided critical information to understand the effects of independent variables on the predicted dependent variable. RF and GB algorithms produced the closest results to the actual data in ANOVA test results and correctly modeled the effects of independent variables. However, kNN and Tree algorithms were found to be less sensitive to some variables. The Nd variable showed borderline significance with _p = 0.006_ for kNN and _p = 0.021_ for the Tree model.