## 4 Conclusions  
In this study, a comparative analysis of machine learning regression models was conducted to predict the energy output power of photovoltaic (PV) panels under varying environmental conditions. The results demonstrate that clean monocrystalline and polycrystalline panels generally yield higher and more stable energy production, which improves the accuracy of prediction models and reduces the difference between predicted and actual values. Conversely, dirty panels exhibit a reduced energy output and increased variability, making predictions less precise.  
Figure 10: Energy efficiency graph of dirty and clean PV panels.  
Figure 9: Graph of six-month energy production estimates of clean and dirty PV panels.  
Among the evaluated models, the Random Forest model consistently outperformed other regression models across all scenarios. It achieved the lowest MSE and MAE values and the highest R\({}^{2}\) scores for both monocrystalline and polycrystalline panels, under both clean and dirty conditions. For instance, in monocrystalline clean panels, the Random Forest model achieved an MSE of 25.051, an MAE of 2.417, and an R\({}^{2}\) value of 0.93696, while for dirty panels, it maintained an MSE of 29.005, an MAE of 2.583, and an R\({}^{2}\) value of 0.93353. Similarly, in polycrystalline panels, the Random Forest model delivered the best performance, with MSEs of 28.921 and 33.485 and R\({}^{2}\) values of 0.94204 and 0.93802 for clean and dirty panels, respectively.  
Although the R\({}^{2}\) values across the models are relatively similar, indicating comparable explanatory powers, the Random Forest model distinguishes itself with significantly lower MSE and MAE values. This indicates that while all models achieve a similar level of general fit to the data, Random Forest provides more precise predictions with smaller errors. This superiority can be attributed to its ability to handle non-linear relationships and noise more effectively, which is particularly evident in the performance differences observed in MSE and MAE metrics.  
These results highlight the Random Forest model's robustness and ability to provide accurate predictions across different panel types and surface conditions. The model's ability to consistently achieve low error rates and a high explanatory power makes it a reliable choice for predicting the energy output under diverse environmental conditions.  
Future work should focus on validating these findings with additional datasets and exploring the use of other advanced models to ensure the generalizability of the results. Moreover, further investigations into feature importance and model interpretability could provide deeper insights into the factors influencing energy production predictions.  
Conceptualization, S.K. and E.A. ([PERSON]); methodology, S.K. and E.A. ([PERSON]); software, S.K. and E.A. (Ertugrul Adguzel); validation, S.K., E.A. (Ertugrul Adguzel) and E.A. ([PERSON]); formal analysis, S.K. and E.A. ([PERSON]); investigation, E.A. ([PERSON]); resources, S.K. and E.A. ([PERSON]); data curation, S.K. and E.A. ([PERSON]); writing--original draft preparation, S.K. and E.A. ([PERSON]); writing--review and editing, E.A. ([PERSON]) and E.A. ([PERSON]); visualization, E.A. ([PERSON]); supervision, E.A. ([PERSON]); project administration, E.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality and ethical restrictions.  
Many thanks to [PERSON] for her help. The authors declare no conflict of interest.