## 1 Introduction
### Impact of Environmental Conditions on Renewable Energy Forecasting in the Literature
Confronting these problems necessitates resilient ML models that can adjust to changing settings and yield interpretable outcomes. In addition, prediction photovoltaic (PV) energy has emerged as a vital element of renewable energy systems. ML approaches, such as support vector machines (SVM), artificial neural networks (ANN), and EL methods, have been extensively utilized. Research demonstrates that ensemble methods, including GBM and RF, surpass conventional statistical techniques in predicting PV production by effectively capturing non-linear correlations and minimizing errors. The incorporation of real-time data, including solar irradiance and meteorological variables, improves the precision of photovoltaic forecasting models. Nonetheless, obstacles persist in expanding these models to accommodate greater datasets and integrating long-term forecasts. Subsequent studies ought to concentrate on enhancing machine learning techniques to mitigate these constraints and augment the incorporation of photovoltaic predictions into smart grid systems. This study confirms the supremacy of ML methods, particularly bagged trees, over traditional econometric approaches in renewable energy prediction. By effectively handling non-linear relationships and complex environmental interactions, ML models provide a significant advancement in accuracy and practicality for real-world applicationsThe reasons why DL models are superior to decision trees can be stated as follows: Since it can extract features from data, it learns more complex relationships and patterns. Model performance increases in large datasets. It has a higher performance than decision trees. This is because it contains more parameters. However, the low number of parameters in the dataset examples given in our study has led to the preference for decision trees instead of DL models, since they contain little information such as temperature, humidity, and pressure. This allows important information to be obtained in terms of explaining how the model makes decisions. It enables the interpretation of the given parameters and the emergence of relationships between them. In addition, having low computational costs allows it to produce the results more quickly. Another factor in preferring decision trees is that there is no need for data pre-processing. This research combines ML techniques with specialized knowledge to improve the precision of renewable energy predictions. The study seeks to deliver practical insights for optimizing energy production and integration by utilizing advanced ML approaches such as EL models. The results are anticipated to enhance the wider implementation of ML in renewable energy, tackling both theoretical and practical deficiencies. Notwithstanding the considerable progress in machine learning methodologies for renewable energy forecasting, there is a deficiency in studies that thoroughly assess tree-based ensemble algorithms in real-time contexts. Moreover, the majority of the research neglects the computational efficiency of these models, which is essential for practical use. Also, this study addresses this gap by performing a thorough investigation of tree-based algorithms, highlighting their accuracy, resilience, and computational viability for photovoltaic energy prediction across diverse environmental conditions.