## 3 Results and Discussion
The Random Forest model has been shown to successfully identify these patterns while maintaining a high level of predictive accuracy. * The Random Forest model demonstrates a strong overall performance, characterized by its capacity to produce accurate results despite the presence of noise and variability in the data. The structure of multiple decision trees enables Random Forest to perform robustly on datasets with noise and variability. The model demonstrates an equal aptitude in learning relationships and trends in both clean and dirty datasets. Similar energy production profiles were exhibited by the dataset. It was observed that despite the presence of contamination, the energy production profiles of dirty panel data were comparable to those of clean panels. This phenomenon led to a high degree of predictability. Conversely, the SVR model demonstrated the poorest performance across all scenarios, exhibiting elevated error values and R\({}^{2}\) values approaching zero. This suggests that the SVR model is unable to capture the underlying patterns of the dataset and is particularly sensitive to non-linear and noisy characteristics arising from varying environmental conditions. The performance of the linear models (linear regression, Ridge, Lasso, and ElasticNet) is comparable, with error metrics and R\({}^{2}\) values that are similar to each other. It is evident that the Lasso and ElasticNet models yield slightly superior outcomes in comparison to the linear regression and Ridge models, a phenomenon that can be attributed to their capacity for regularization and the mitigation of overlearning. The higher R\({}^{2}\) values observed in predictions made for dirty panels compared to clean panels indicate that these models are better able to adapt to noise-induced patterns in the dataset. Consequently, the Random Forest model emerges as the most effective and reliable option for energy production prediction in PV systems. The model's robust performance across both clean and dirty datasets underscores its generalization capability and its capacity to generate precise predictions irrespective of the data type. However, a detailed validation of these results and additional analyses using different models or alternative metrics would contribute to consolidating the findings in a broader context. Figure 9 depicts a comparative graph of six-month energy measurements of dirty and clean PV panels, along with predictions generated by machine regression. In the course of measuring the data for the period between 1 January and 30 June 2023, it was not possible to obtain 17 days' worth of data due to a system failure. The graphs of clean and dirty PV panels below demonstrate the critical importance of maintaining the cleanliness of PV panels for optimal energy production, as well as the detrimental impact of pollution on the efficacy of energy prediction models based on machine learning regression. Figure 10 illustrates the comparative rates of energy efficiency improvement for clean and dirty monocrystalline and polycrystalline photovoltaic (PV) panels. In accordance with the IEC 61724 standard [24], data collection and PV panel cleaning were performed in the course of this study. The increase in monocrystalline panels was observed to be 8.56%, while the corresponding figure for polycrystalline panels was 6.22%.