From state-of-the-art to next-generation solutions: A review for Gen-AI integration in photovoltaic maintenance
Tan Kah Hui, Ah Khoon Hwee, Liu QingHua, Khoo Terh Jing, Chukwuka Christian Ohueri
Abstract
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.