## 4 Data and Assessment Criteria  
Data were gathered at five-minute intervals from the Utrecht University Photovoltaic Outdoor Test Facility (UPOT), located in Utrecht, The Netherlands (Latitude: 52.0907374, Longitude: 5.1214201), which evaluates the performance of several photovoltaic (PV) modules under real-world situations (Figure 3) [30]. Solar radiation measurements were obtained using a Kipp & Zonen CMP11 pyranometer (Delft, The Netherlands). The dataset contains 11 features; 10 serve as input variables, while one feature (Efficiency) functions as the output variable in the models. The characteristics and their associated values are displayed in Table 2.  
Figure 2: Usage of Explainable AI in the predictions [26].  
Each model was trained on the training dataset, with hyperparameters optimized via grid search and cross-validation methods. To ensure reproducibility, the random_state was set to 42 for all models. The number of estimators (n_estimators) was also consistently set to 500 across all models after optimization. Other hyperparameters were specific to each model and were determined through the grid search process. The evaluation of model performance was conducted utilizing metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination (R\({}^{2}\)). The metrics are computed utilizing the below formulae. The MAE (Equation (1)) measures the mean magnitude of the absolute discrepancies between expected and observed values. The MSE (Equation (2)) evaluates the mean squared differences between expected and actual values. The RMSE (Equation (3)), the square root of the MSE, quantifies the model's predictive performance by assessing the square root of the mean squared discrepancies between anticipated and actual values. The R\({}^{2}\) (Equation (4)) value indicates the extent to which the variance in the dependent variable is elucidated by the independent factors.  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\lvert y_{i}-\hat{y_{i}}\rvert \tag{1}\]  
\[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}\bigl{(}y_{i}-\hat{y_{i}}\bigr{)}^{2} \tag{2}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\bigl{(}y_{i}-\hat{y_{i}}\bigr{)}^{ 2}} \tag{3}\]  
Figure 3: The Utrecht University Photovoltaic Outdoor Test (UPOT) facility evaluates the real-world performance of different commercial and prototype PV modules [31].  
\begin{table}
\begin{tabular}{r l} \hline \hline \multicolumn{1}{c}{**Feature**} & \multicolumn{1}{c}{**Values Between**} \\ \hline Open Circuit Voltage (Voc) [Volt] & 30–35 \\ Short Circuit Current (Isc) [A] & 5–7 \\ Maximum Power (Pmpp) [W] & 0.07–1.25 \\ Solar Irradiation Spread [m\({}^{2}\)] & 0.000515–0.007286 \\ Maximum Voltage (Vmpp) [V] & 15.16–29.36 \\ Maximum Current (Impp) [A] & 0.12–0.50 \\ FillFactor (FF) [\%] & 45–70 \\ Parallel Resistance (Rp) [Ohm] & 0.17–1.19 \\ Series Resistance (Rs) [Ohm] & 0.17–0.96 \\ Module Temperature [\({}^{\circ}\)C] & 7.70–59.80 \\ Effective) [\%] & 6–18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Internal features [31].  
\[R^{2}=1-\frac{\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}{\sum_{i=1}^{n}(y_{i}-\overline {y})^{2}} \tag{4}\]