## 1 Introduction  
Solar photovoltaic (PV) systems have become a crucial technology in the worldwide shift to sustainable and renewable energy sources. The increasing apprehensions regarding climate change, the exhaustion of fossil resources, and the ecological consequences of traditional energy production have driven the global implementation of photovoltaic systems. Wind and solar photovoltaic energy will exceed nuclear power generation in 2025 and 2026, respectively. By 2028, renewable energy sources will represent over 42% of worldwide electricity generation, with the proportion of wind and solar photovoltaic energy doubling to 25% [1]. The efficiency of these systems is a crucial determinant that directly affects their overall performance, energy output, and economic feasibility. Efficient photovoltaic systems enhance energy production while reducing expenses, which is crucial for the competitiveness of solar energy in the global energy market. Precise assessment of photovoltaic efficiency facilitates enhanced system design, optimization, and maintenance planning. It enables accurate energy yield forecasts, aiding in grid integration and energy strategizing. Furthermore, comprehending efficiency dynamics assists producers and engineers in enhancing PV module designs and materials, resulting in technological progress in the sector. Historically, the evaluation of photovoltaic performance has mostly concentrated on external environmental variables, including solar irradiation, ambient temperature, shade, and meteorological conditions. These parameters clearly influence the performance of photovoltaic systems; nonetheless, they do not present a comprehensive overview. The internal features of photovoltaic modules, including electrical parameters, material properties, and thermal behavior, provide vital insights that can be utilized for more accurate efficiency calculation. Parameters such as Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Fill Factor (FF), and resistances (parallel, Rp and series, Rs) are inherent to photovoltaic modules and substantially influence their operating efficiency. Recent breakthroughs in machine learning (ML) have yielded sophisticated tools adopt at predicting the intricate non-linear interactions characteristic of photovoltaic (PV) systems [2]. Machine learning algorithms can manage extensive datasets with numerous variables, identifying complex patterns and relationships that conventional statistical methods would miss. The incorporation of machine learning in photovoltaic efficiency assessment offers a chance to improve forecast accuracy by concurrently accounting for both internal and external elements. Nonetheless, a considerable obstacle in utilizing ML models is their \"black-box\" characteristic. Although they provide excellent predicted accuracy, comprehension of how input features affect the output is frequently constrained. The absence of openness might impede confidence and acceptability among stakeholders that necessitate justifications for model projections, especially in vital applications such as energy systems. Interpretability is crucial for both validating model behavior and understanding the system's physical processes. To tackle this difficulty, Explainable Artificial Intelligence (XAI) methodologies have been created to elucidate and clarify machine learning model predictions [3]. XAI methodologies seek to elucidate the decision-making processes of machine learning models, offering insights into the contributions of input features to the resultant output. In the realm of photovoltaic systems, the application of explainable artificial intelligence approaches can clarify the impact of internal factors on efficiency calculation, thereby improving comprehension and enabling informed decision-making.