<![CDATA[General background: Solar energy is recognized as the most potent and abundant form of renewable energy available to meet global energy demands. Specific background: Despite its potential, solar power systems face challenges related to low efficiency, high operational costs, and safety concerns. Knowledge gap: These persistent issues require intelligent solutions, yet the integration of advanced artificial intelligence (AI) techniques into solar energy systems remains underexplored in practical and scalable contexts. Aims: This study aims to examine the role of AI—particularly Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL)—in addressing key limitations in solar power systems. Results: We highlight three main AI-driven use cases: performance forecasting, system optimization, and predictive maintenance, all of which significantly improve operational efficiency, reliability, and system longevity. Novelty: By leveraging AI’s adaptive and data-driven capabilities, this work presents an innovative framework for real-time decision-making and predictive analytics in solar energy systems. Implications: The findings underscore AI’s transformative potential in enabling the widespread, flexible, and sustainable integration of solar power into global energy infrastructures, thereby accelerating the transition toward a resilient and intelligent renewable energy future. Highlights: AI boosts solar performance via forecasting, optimization, and maintenance. Machine learning adapts systems using data-driven predictive models. Enhances sustainability by reducing costs and extending system lifespan. Keywords: Solar Energy, Artificial Intelligence, Machine Learning, Predictive Maintenance, System Optimization]]>
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