Revolutionizing solar energy resources: The central role of generative AI in elevating system sustainability and efficiency

Rashin Mousavi, Arash Mousavi, Yashar Mousavi, Mahsa Tavasoli, Aliasghar Arab, Ibrahim Beklan Kucukdemiral, Alireza Alfi and Afef Fekih

Applied Energy, 2025, vol. 382, issue C, No S0306261925000261

Abstract: Driven by growing environmental concerns, such as global warming and the depletion of fossil fuels, the renewable energy industry, particularly solar energy, has risen to global prominence. In this context, generative artificial intelligence (Gen-AI) can play a valuable role in facilitating the development of more efficient, durable, and adaptable solar systems. Gen-AI’s multifaceted proficiency, from predictive maintenance and reducing downtime and costs to vital forecasting for grid management and strategic planning, extends to optimizing site selection for solar farms and smart grid integration, thereby enhancing solar energy flow, grid stability, and sustainable operation. This paper presents a comprehensive exploration of the role of Gen-AI in revolutionizing the solar energy industry. Focusing on various aspects of solar energy systems, including design, optimization, sizing, maintenance, energy forecasting, site selection, and smart grid integration, the study investigates the transformative impact of Gen-AI across these domains. It demonstrates how Gen-AI enhances the efficiency, sustainability, and adaptability of solar systems, driving strategic decision-making and optimizing the integration of solar power within complex energy ecosystems. Furthermore, the paper concludes by discussing the challenges and future prospects of employing Gen-AI in the solar energy domain, providing a comparative analysis of the current and future scenarios, and underscoring the advantages, disadvantages, and challenges of Gen-AI implementation.

Keywords: Generative artificial intelligence; Solar energy systems; AI-driven solar solutions; Solar photovoltaic systems design and optimization; Solar systems predictive maintenance (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.125296

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