From state-of-the-art to next-generation solutions: A review for Gen-AI integration in photovoltaic maintenance

Enhancing solar efficiency through effective maintenance is critical for photovoltaic (PV) systems. The integration of Generative Artificial Intelligence (Gen-AI) with advanced technologies offers a promising approach to optimize PV maintenance. However, despite its potential, research on Gen-AI integration remains limited. To bridge this gap, this study aims to evaluate the current state-of-the-art applications in PV maintenance and based on identified limitations, propose novel Gen-AI integration strategies to enhance system performance and operational efficiency. In doing so, a Systematic Literature Review (SLR) was conducted to address the identified research gap, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Using the Web of Science (WoS) database, a total of 162 records were retrieved, of which 84 peer-reviewed articles were selected for detailed analysis. Therefore, this study contributes a comprehensive review of previous frameworks, key application components, findings, and associated advantages, disadvantages, and quantitative metrics in state-of-the-art PV system maintenance. Building on these findings, this study critically evaluates existing limitations from technical, environmental, and economic perspectives. This study’s main contribution lies in the development of a conceptual integration roadmap that introduces immersive systems, robotic platforms, unmanned aerial vehicles (UAVs), and satellite-based technologies as next-generation Gen-AI solutions for PV maintenance. It concludes that Gen-AI based technologies have the potential to advance PV maintenance, by highlighting their integration potential, strengths and weaknesses, and offering recommendations for future improvements in Gen-AI implementation.