This paper presents a comprehensive review conducted with reference to a pioneering, comprehensive, and data-driven framework proposed for solar Photovoltaic (PV) power generation prediction. . This paper explores the application of Explainable AI (XAI) through the proposed SPXAI model to enhance the efficiency and reliability of solar energy systems. SPXAI collects extensive power production data from solar farms and employs machine learning and deep learning models to analyze this data. . This study presents a comprehensive evaluation of solar power forecasting methods developed between 2021 and 2025, a period marked by the rapid advancement in artificial intelligence (AI) and a significant increase in hybrid deep learning models applied to this domain. The review covers traditional. . The intermittent and stochastic nature of Renewable Energy Sources (RESs) necessitates accurate power production prediction for effective scheduling and grid management. The proposed deep learning-based model is designed to predict SPG for various locations by leveraging a comprehensive dataset from multiple sites in the. .
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