## 6 Discussion  
The comparative study results indicate that CatBoost surpassed other regression models in forecasting the efficiency of photovoltaic (PV) systems, underscoring its proficiency in managing intricate non-linear connections and categorical variables. The enhanced accuracy across all evaluation criteria is due to CatBoost's distinctive features, including ordered boosting and effective management of categorical input, which alleviate overfitting concerns and enhance generalization. This skill is essential in photovoltaic efficiency estimates, where understanding the interplay between internal factors like maximum power (Pmpp) and fill factor (FF) is crucial for enhancing prediction accuracy. XGBoost and LightGBM demonstrated commendable outcomes, with performance measures closely aligning with those of CatBoost. XGBoost's integration of regularization approaches and LightGBM's application of Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB) improve their efficacy in managing intricate datasets. Nonetheless, CatBoost's proficiency in modeling intricate non-linear interactions rendered it the most effective model in this investigation. The marginally inferior performance of Histogram-Based Gradient Boosting, despite its computational ease, underscores the trade-off between computational efficiency and model efficacy. The discretization of continuous features may result in information loss, hence accounting for the observed rise in prediction error. AdaBoost's comparatively elevated error metrics highlight its constraints in this situation. The focus on misclassified instances during training renders it vulnerable to noise and outliers, which likely impaired its capacity to accurately simulate the intricacies of PV system efficiency. This discovery underscores the significance of model selection contingent upon the dataset's complexity and the prediction objective. The incorporation of Explainable Artificial Intelligence (XAI) methodologies, particularly SHAP and LIME, enhances understanding of the model's predictions by pinpointing the significant elements for efficiency assessment. Global explanations utilizing SHAP elucidate the most significant elements within the dataset, including solar irradiation distribution, maximum power, and fill factor, thereby offering a comprehensive comprehension of their contributions. MPP and fill factor are influenced by extrinsic factors such as sun irradiation. [PERSON], [PERSON], and [PERSON] [19] similarly proved that when irradiance increases, the output current and maximum power point (MPP) increase, however the fill factor falls. This underscores the substantial impact of solar irradiance, an external variable, on these parameters. Simultaneously, local explanations via LIME clarify specific forecasts, offering a more detailed picture. This dual methodology for model interpretation boosts both the transparency and reliability  
Figure 7: LIME local annotation (continued). of the model while offering actionable insights to inform enhancements in photovoltaic module design and operating efficiency. The present study is limited by its evaluation of model performance on a single dataset from the Utrecht University Photovoltaic Outdoor Test Facility (UPOT). While this dataset provides valuable insights into the performance of PV systems under real-world conditions, it is important to acknowledge that the results may not generalize perfectly to other datasets with different characteristics (e.g., different PV technologies, climatic conditions, or data collection methodologies). Future research should explore the performance of these machine learning models on multiple, diverse datasets to assess their robustness and generalizability. This would provide a more comprehensive understanding of the strengths and weaknesses of each method and inform the selection of the most appropriate model for different PV system applications.