## 3 Experimental Results  
The hyperparameters employed in the fine tree, medium tree, coarse tree, bagged tree, and boosted tree models were provided, along with corresponding parameters and details regarding training time and memory usage. Figure 5 displays the current prediction data and the corresponding outcomes for the verification estimate, based on the information provided.  
The temperature, pressure, and humidity parameters within the dataset have an impact on the production quantity. Each parameter in this effect carries a distinct weight. These values have a direct impact on the level of production. Figure 6 illustrates the quantity of energy generated carrying the influence of these independent variables.  
The findings are evaluated based on the information provided in each row of the training validation dataset. The study was performed in MATLAB Toolboxes were used. The evaluation metrics findings are provided in Table 4. Based on these findings, bagged tree shows superior performance compared to the other examined decision trees while fine tree ranked last.  
The box plot obtained according to the data of error indices is shown in Figure 7. Using the data provided, we achieved results by independently using decision tree models in the study. The prediction of solar energy production is derived from factors that are reliant on temperature, humidity, and pressure. Decision trees are the most interpretable regression models that can be derived from this type of data. The decision trees, namelythe fine tree, medium tree, and coarse tree, exhibit similarities but also possess varying levels of complexity. The accuracy of the outcomes derived using these decision trees has been proven to be inferior to that of the ensemble decision tree.  
Empirical evidence suggests that higher levels of complexity are positively correlated with improved outcomes. Studies employing econometric methods for energy prediction often report significant limitations, such as reduced accuracy when dealing with non-linear and high-dimensional datasets. For example, linear regression models typically achieve \(R^{2}\) values between 0.3 and 0.5 in similar applications, whereas the bagged trees model in this study achieved an \(R^{2}\) of 0.5650, demonstrating its superior performance. This comparison underscores the potential of ML approaches to address the inherent complexities of renewable energy forecasting.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Tree & Fine Tree & Tested & 1.9696 & 3.8793 & 0.4264 & 1.1718 & 1.1978 & 4.0324 & 2.0081 & 0.4007 \\ \hline Tree & Medium Tree & Tested & 1.8213 & 3.3172 & 0.5095 & 1.1198 & 1.1256 & 3.3111 & 1.8196 & 0.5079 \\ \hline Tree & Coarse Tree & Tested & 1.7658 & 3.1180 & 0.5390 & 1.1104 & 1.1253 & 3.1815 & 1.7837 & 0.5272 \\ \hline Ensemble & Boosted Trees & Tested & 1.7566 & 3.0857 & 0.5437 & 1.1462 & 1.1657 & 3.1479 & 1.7742 & 0.5322 \\ \hline
**Ensemble** & **Bagged Trees** & **Tested** & **1.7152** & **2.9420** & **0.5650** & **1.0666** & **1.0781** & **2.9928** & **1.7300** & **0.5552** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of statistical indices from decision trees.  
Figure 5: Tests, verification, and responses obtained after training.  
Figure 6: Temperature, pressure, and humidity effect graphs in energy production with datasets.