## 1 Introduction
This study investigates the estimation of photovoltaic system efficiency utilizing eleven internal parameters: Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Solar Irradiation Spread (SIS), Maximum Voltage (Vmpp), Maximum Current (Impp), Fill Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), and Module Temperature (Tm). We utilize advanced machine learning regression models, including XGBoost, CatBoost, LightGBM, AdaBoost, and Histogram-Based Gradient Boosting, to forecast efficiency based on these attributes. These models are selected for their capacity to manage non-linear relationships and interactions among variables, together with their shown efficacy in regression tasks. To improve the interpretability of our machine learning models, we utilize explainable artificial intelligence techniques, notably SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations). SHAP offers a cohesive framework for elucidating forecasts by calculating the contribution of each feature to the final result, grounded in game theory principles [4]. LIME elucidates individual predictions by locally approximating the intricate model with a comprehensible one [5]. The utilization of these XAI approaches in photovoltaic efficiency assessment is innovative and bridges the gap in comprehending the influence of internal variables on model predictions. This research is significant due to its potential to enhance the efficiency of photovoltaic systems. By comprehending the internal aspects that affect efficiency, engineers and practitioners can concentrate on essential parameters for system enhancements. Furthermore, the interpretability afforded by XAI approaches can augment the reliability and adoption of ML models within the renewable energy sector. The principal objective of this study is to enhance the assessment of photovoltaic system efficiency by employing internal module characteristics alongside sophisticated machine learning models and explainable artificial intelligence approaches. We want to develop precise regression models for estimating photovoltaic efficiency utilizing internal features. * Analyze the predictions of these models to comprehend the impact of each feature. * Deliver practical insights that can guide the design and optimization of PV systems. This study's originality resides in the use of XAI techniques, SHAP and LIME, to elucidate the efficiency forecasts of machine learning models in the realm of photovoltaic systems. Although machine learning has been utilized in renewable energy forecasting and optimization, the application of explainable artificial intelligence techniques to elucidate the impact of internal photovoltaic properties on efficiency estimation is unprecedented. This study is, to our knowledge, the inaugural application of XAI approaches to clarify the internal dynamics of PV systems within efficiency prediction models, thus addressing a significant gap in existing research. We seek to integrate powerful machine learning techniques with explainable artificial intelligence to develop models that are both precise and interpretable. This method corresponds with the increasing desire for transparency in AI applications, particularly in areas where decision-making affects economic and environmental results. Table 1 presents an overview of the research organized by technique and application domains derived from this literature review.