## 3 Results and Discussion
Figure 7 illustrates that the SVR model exhibits a relatively low level of performance. There is a notable discrepancy between the predicted and actual data. As illustrated in the graph, the predicted values exhibit a tendency to remain within a narrow range, which limits their capacity to reflect the inherent variability of the data. This indicates that the model is unsuitable for this particular dataset. As illustrated in Figure 8, the Random Forest model demonstrates a high degree of accuracy in predicting and reproducing actual values. The model is based on decision trees and is capable of capturing complex structures within the dataset. It is more successful than other models, particularly in terms of capturing the fluctuations of the data. Nevertheless, discrepancies are still evident at certain points. The results demonstrate that the Random Forest model exhibits the optimal performance in energy prediction, whereas the SVR model produces the least accurate predictions. Notably, linear regression has demonstrated efficacy in short-term forecasts of photovoltaic power generation, exhibiting minimal error rates. The Ridge and Lasso models have demonstrated the capacity to produce efficacious forecasts of energy production by examining the impact of factors such as solar radiation and temperature on the forecast. The ElasticNet model has demonstrated the capacity to accurately model the effects of environmental variables by combining the advantages of both the Ridge and Lasso models. The SVR model has demonstrated a robust performance in forecasting time series data and a range of weather scenarios. The Random Forest model has also exhibited a high accuracy in solar energy forecasting, particularly when working with meteorological datasets. The capacity of these models to make accurate predictions in different weather conditions contributes to the more efficient use of solar energy and the increased stability of energy systems. Figure 6: Predictions of ElasticNet Regression modeling of clean and dirty monocrystalline and polycrystalline PV panels. As demonstrated in Table 2, a comparative analysis of the performance outcomes of six machine learning models (i.e., linear regression, Ridge, Lasso, ElasticNet, SVR, and Random Forest) was conducted for the purpose of predicting the energy production of monocrystalline and polycrystalline photovoltaic (PV) panels under both clean and polycrystalline products. Figure 8: Random Forest modeling predictions of clean and dirty monocrystalline and polycrystalline PV panels. Figure 7: Predictions for SVR modeling of clean and dirty monocrystalline and polycrystalline PV panels. polluted conditions. While all models demonstrate similar coefficients of determination (R\({}^{2}\)), the Random Forest model consistently achieves the lowest error values (MAE and MSE) across all scenarios, making it the most accurate and reliable model. For instance, it attains R\({}^{2}\) = 0.93696 for clean monocrystalline panels and R\({}^{2}\) = 0.94204 for clean polycrystalline panels, while maintaining a comparable performance under dirty conditions, with R\({}^{2}\) = 0.93353 for monocrystalline and R\({}^{2}\) = 0.93802 for polycrystalline panels. The Random Forest model's superior performance is further evidenced by its ability to minimize errors, showcasing its robustness in handling both linear and non-linear relationships in the data. The Random Forest model's capacity to produce analogous outcomes in both clean and dirty conditions can be attributed to its resilience and adaptability to noise. This demonstrates the model's capability to effectively learn the dynamics of energy production in both datasets. The following factors are identified as the primary contributors to this phenomenon:  
* Patterns in dirty panel data: The data obtained from dirty panels may not contain completely random noise, but may contain specific and consistent patterns that affect energy production.