## 5 Conclusions  
In conclusion, the study successfully developed machine learning models for estimating soiling losses in PV systems. The models, particularly LightGBM for local predictions and the ANN for generalization across locations, performed well in their respective contexts. Whilst local models exhibited high accuracy in their original locations, the Global Model was able to generalise better, leading to a significant performance boost in terms of RMSE and \(R^{2}\) compared to the extrapolated models. The Global Model showed strong performance because it was trained on a more diverse dataset that included a broader range of soiling conditions. In contrast, local models, although highly accurate within their own datasets, struggled to estimate soiling values that were uncommon in their training data. For example, soiling losses below 1% were rarely present in the CIESOL dataset, making it difficult for CIESOL-trained models to perform well on CIEMAT data, where such values are more frequent. This demonstrates the Global Model's improved ability to generalise across varying environmental contexts. Additionally, the LightGBM model demonstrated strong performance due to its ability to handle non-linear relationships and perform well with relatively small datasets, making it a robust option for soiling loss estimation. Future work should focus on enhancing both the performance and general applicability of the proposed models, particularly the Global Model. One of the most immediate steps is to test the current Global Model in a wider range of geographic locations with different environmental conditions. This would help evaluate its ability to generalise and identify potential areas where performance may decrease due to factors that are not present in the original training data. At the same time, the development of a new version of the Global Model that incorporates training data from a broader variety of locations is recommended. Including regions with diverse climates and a wider spectrum of soiling levels would allow the model to better represent the variability of environmental influences on soiling losses. This expansion is expected to improve its accuracy and make it more robust for global applications. In addition, the integration of Geographic Information System (GIS) tools and satellite data represents a promising direction for enhancing the capabilities of soiling loss prediction models. These external data sources can provide real-time, location-specific information on key environmental variables such as atmospheric temperature, wind speed, relative humidity, and particulate matter. Incorporating this information into the models would enable more dynamic and geographically adaptable predictions. Furthermore, since the current models are based on accessible meteorological variables, they are well suited for integration with satellite and remote sensing platforms, which can help scale their application across broader regions without the need for extensive on-site measurements. Further exploration of LightGBM is also encouraged. This model demonstrated strong results with local data and is well suited to handling large datasets and complex interactions between variables. Additional tuning and testing with larger and more varied datasets could improve its performance even further. **Author Contributions:** Conceptualization, [PERSON] and [PERSON]-[PERSON]; methodology, [PERSON]-[PERSON], [PERSON] and [PERSON]-M.; software, [PERSON]-[PERSON] and [PERSON]-M.; validation, [PERSON]-[PERSON], [PERSON] and [PERSON]-M.; formal analysis, [PERSON]-[PERSON] and J.A.-M.; investigation, [PERSON]-[PERSON], J.P. and [PERSON]-M.; resources, [PERSON]-[PERSON], J.P. and [PERSON]-M.; data curation, [PERSON]-[PERSON], [PERSON] and [PERSON]-M.; writing--original draft preparation, [PERSON]-[PERSON], [PERSON] and [PERSON]-M.; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON], [PERSON] and [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, [PERSON] and [PERSON].