## 4 Result and Discussion  
In order to comprehensively observe the solar PV power generation process based on meteorological data, a matrix consisting of correlation coefficient pairs was constructed. This enabled the identification of collinearity between the existing features and the power output. The data used in this study were obtained from a research project numbered KBU-21-DS-018 and titled \"IoT-Based Condition Monitoring and Fault Analysis of Solar Panels\". Figure 5 provides the correlation between the existing features and solar PV power output. From the correlation graph, it can be observed that the features with the highest correlation to power output are hour and humidity. Additionally, a similar strong correlation between humidity and temperature is also evident. Following the identification of the relationship between meteorological data and solar PV power output, the dataset was  
Figure 5: Correlation between current features and solar PV power output. subjected to machine learning models. The graph in Figure 6 presents the predicted values against the actual test data for solar PV power output estimations performed using linear regression, SVM, decision trees, random forest, and KNN algorithms. After obtaining the graph depicting predicted values corresponding to real test data, the performance of the models was quantified numerically. At this stage, metrics such as MAE, MSE, RMSE, and R\({}^{2}\) were employed as performance evaluation criteria. The comparison of model performances based on these evaluation criteria is presented in Table 2.  
According to the performance evaluation criteria presented in Table 2, it can be deduced that the most successful prediction model across all domains is the random forest, while the least performing prediction model is linear regression. Based on the MAE evaluation criterion, the random forest algorithm exhibits approximately 13% less error than the decision trees algorithm, which provides the closest result. The KNN algorithm is the third one, and it has 41% more error than the random forest algorithm. The error difference between the SVM, linear regression algorithms, and the random forest algorithm is 1.81 times, and 2.24 times, respectively. In terms of the MSE evaluation criterion, the random forest algorithm outperforms the decision trees algorithm, which provides the closest result, by approximately 45%. The KNN algorithm is the third one, and it has 57% more error than the random forest \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **MAE** & **MSE** & **RMSE** & **R\({}^{2}\)** \\ \hline Linear Regression & 3.41 & 23.53 & 4.85 & 0.64 \\ Linear Regression-index & 2.24 & 2.74 & 1.66 & 1.35 \\ SVM & 2.76 & 21.25 & 4.60 & 0.67 \\ SVM-index & 1.81 & 2.47 & 1.57 & 1.29 \\ Decision Trees & 1.72 & 12.48 & 3.53 & 0.81 \\ Decision Trees-index & 1.13 & 1.45 & 1.20 & 1.07 \\ Random Forest & 1.52 & 8.57 & 2.92 & 0.87 \\ Random Forest-index & 1.00 & 1.00 & 1.00 & 1.00 \\ KNN & 2.15 & 13.48 & 3.67 & 0.79 \\ KNN-index & 1.41 & 1.57 & 1.25 & 1.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of prediction models based on MAE, MSE, RMSE, and R\({}^{2}\) evaluation criteria. Figure 6: Predicted values against real test data. algorithm. The error difference between the SVM, linear regression algorithms, and the random forest algorithm is 2.47 times, and 2.74 times, respectively. Considering the RMSE evaluation criterion, the random forest algorithm possesses about 20% less error than the decision trees algorithm, which provides the closest result. The error difference between the KNN, SVM, and linear regression algorithms, and the random forest algorithm is 25%, 57%, and 66%, respectively. With regards to the R\({}^{2}\) evaluation criterion, the random forest algorithm performs about 7% better than the decision trees algorithm, which provides the closest result. The difference in performance between the KNN, SVM, and linear regression algorithms, and the random forest algorithm is 10%, 29%, and 35%, respectively.