## 2 Background  
The incorporation of machine learning in the renewable energy sector has garnered considerable attention in recent years. Numerous research studies have investigated the utilization of machine learning algorithms [13] for the prediction and optimization of energy systems, including photovoltaic systems [14]. [PERSON] et al. [2] conducted an extensive assessment of machine learning methodologies for estimating solar power generation. They underscored the efficacy of machine learning algorithms in identifying non-linear patterns in solar irradiance and photovoltaic \begin{table}
\begin{tabular}{c c c} \hline \hline
**ReferStudy** & **Method** & **Result** \\ \hline
[6] & SVR, MLP & & Performance evaluation of two machine learning techniques \\
[7] & LR, MLP, RBF, SVR, GP, ANFIS & Smart Aspects of CatBoost Algorithm \\
[8] & LR, SVR, Fine Tree, Medium Tree, Coarse Tree, & Regression Algorithms for Energy Estimation in Residential Buildings. \\  & Boosted Trees, Bagged Trees, GP & The heating and cooling loads of residential buildings. \\
[9] & LS-Boosting, RTE, SPLA & Comprehensive preference learning and \\  & RF, RDT, Lazy K-Star, AMT,MLP, M5 Rules, & feature validity for designing \\
[10] & DPC, MPR, XNN, RBF, MLR, GPR,S MOR, & energy-efficient residential buildings \\  & LMSR, LIVL, EN & using machine learning paradigms. \\
[11] & MLP, RBF, ElasticNet, RF, TreeModel, LWL & Predicting heating load in energy efficient buildings through machine learning techniques. \\  & & Exploratory analysis of machine learning \\
[12] & MLP, KNN, SVR, RF, GBM,XGBoost & Techniques to predict energy efficiency in buildings. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The literature summary including similar studies. output, accentuating the potential for enhanced forecasting precision. Nonetheless, their emphasis was largely on exterior environmental conditions, with minimal attention to internal PV module attributes. [PERSON] et al. [15] created artificial neural network (ANN) and regression models to forecast photovoltaic (PV) module power production based on meteorological variables and operating temperature as inputs. Their ANN model, specifically utilizing the Levenberg-Marquardt training procedure, attained exceptional precision (R\({}^{2}\) reaching 0.998) and asserted superiority over previous models. [PERSON] et al. [16] created an automated, data-driven machine learning instrument for modeling building energy systems utilizing sensor data, thereby diminishing dependence on the comprehensive system knowledge necessitated by gray and white box models. Their approach effectively simulated diverse energy systems in numerous scenarios, facilitating forecasting, control techniques, and compensating for absent data. The application of sophisticated gradient boosting algorithms in photovoltaic systems has been investigated. [PERSON] and [PERSON] [17] created an ensemble model called XGBF-DNN, which integrates extreme gradient boosting forests and deep neural networks through ridge regression to predict hourly global horizontal irradiance. Their model, utilizing specific input features such as temperature, clear-sky index, relative humidity, and hour of the day, exhibited enhanced prediction accuracy and stability across various Indian climates in comparison to conventional machine learning techniques, rendering it suitable for energy forecasting applications, including solar and wind power. Explainable AI methodologies have garnered attention across multiple fields but remain largely unexamined in photovoltaic system modeling. [PERSON] and [PERSON] [4] presented SHAP as a technique for elucidating intricate machine learning models. Their methodology has been utilized in sectors such as healthcare and finance, although it has not gained widespread acceptance in renewable energy research. [PERSON] et al. [5] created LIME to furnish localized explanations for specific forecasts.