## 3 Results and Discussion  
In this experimental study, the daily peak energy data obtained from photovoltaic (PV) panels between 1 January 2023 and 30 June 2023, and the value of the environmental factors in that period, were analyzed with machine learning regression models. Six distinct regression models were subjected to a comparative analysis, and error functions were identified. The following regression models were employed for the analysis: linear regression, Ridge regression, Lasso regression, ElasticNet regression, Support Vector Regression (SVR), and Random Forest models. The graphs for each model are provided below, and the MAE, MSE, and R\({}^{2}\) values are presented in Table 2 for comparison. Figure 3 illustrates a comparable trend between the predicted values (orange) and the actual values (blue) of clean and dirty PV panels. However, it is evident that the model's predictions exhibit discrepancies in certain abrupt changes. The outcomes for both monocrystalline and polycrystalline clean PV panels are notably favorable. As illustrated in Figure 4, the regression analysis employing the Ridge method yielded results that are nearly identical to those obtained through linear regression. The predictions are, for the most part, in close alignment with the true values; however, some discrepancies are still evident at the upper and lower limits of the dataset. This variant of linear regression employs an additional penalty term to prevent overfitting. Figure 5 illustrates the performance of Lasso regression, which exhibits a comparable outcome to that of linear regression. It is, however, noteworthy that the estimated curve deviates to a great extent from the actual data in certain areas. Lasso regression is comparable to Ridge in that it prevents overfitting by selecting variables and reducing some coefficients to zero. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Monocrystalline Clean**} & \multicolumn{3}{c}{**Monocrystalline Dirty**} & \multicolumn{3}{c}{**Polycrystalline Clean**} & \multicolumn{3}{c}{**Polycrystalline Dirty**} \\ \cline{2-13}  & **MSE** & **MAE** & **R\({}^{2}\)** & **MSE** & **MAE** & **R\({}^{2}\)** & **MSE** & **MAE** & **R\({}^{2}\)** & **MSE** & **MAE** & **R\({}^{2}\)** \\ \hline Linear Regression & 21.460364 & 2.446629 & 0.946 & 29.862416 & 2.913831 & 0.93157 & 31.382007 & 3.225071 & 0.9371 & 39.496208 & 3.563459 & 0.9269 \\ \hline Ridge & 21.459944 & 2.446566 & 0.946 & 29.861922 & 2.913753 & 0.93157 & 31.381909 & 3.225063 & 0.93711 & 39.496099 & 3.563451 & 0.9269 \\ \hline Lasso & 21.248957 & 2.405298 & 0.94653 & 29.625856 & 2.883666 & 0.93211 & 32.314242 & 3.214784 & 0.93741 & 39.362826 & 3.5576 & 0.92715 \\ \hline ElasticNet & 21.325286 & 2.4213202 & 0.94634 & 29.707916 & 2.893632 & 0.93192 & 31.277662 & 3.217466 & 0.93731 & 39.415989 & 3.558854 & 0.92705 \\ \hline SVR & 368.06379 & 15.83324 & 0.07381 & 401.98623 & 16.62928 & 0.07879 & 459.72607 & 17.90789 & 0.07862 & 496.830077 & 18.58907 & 0.08044 \\ \hline Random Forest & 25.051308 & 2.416703 & 0.93696 & 29.005226 & 2.58343 & 0.93353 & 28.921422 & 2.865306 & 0.94204 & 33.485835 & 2.628908 & 0.93802 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of energy production prediction models for clean and dirty monocrystalline and polycrystalline photovoltaic (PV) panels. Figure 3: Linear regression modeling predictions of clean and dirty monocrystalline and polycrystalline PV panels. As illustrated in Figure 6, the ElasticNet model exhibits a performance that is nearly identical to that of the Lasso and Ridge models. Although a comparable pattern is evident between the actual and predicted values, discrepancies are apparent at certain points. Figure 4: Ridge regression modeling predictions of clean and dirty monocrystalline and polycrystalline PV panels. Figure 5: Predictions of Lasso regression modeling of clean and dirty monocrystalline and polycrystalline PV panels.