AI-based predictive maintenance of solar photovoltaics systems: a comprehensive review

The need for predictive maintenance methods has arisen as a key element in improving operational efficiency, reliability, and life expectancy of photovoltaic (PV) systems and the future complex renewable energy infrastructure sets. The Machine learning (ML) technique is sub part of Artificial Intelligence (AI) technology which has widened their adoption in energy analytics, resulting in numerous studies proposing different algorithms for monitoring, prediction, and prevention of system failures. The overview of these approaches is yet to be exhaustive in the existing literature regarding a metric-based evaluation. In addressing this gap, the article undertakes a structured review of the state-of-the-art recent peer-reviewed literature on predictive maintenance in solar PV systems. Each work will, therefore, be appraised against standardized performance metrics models, which include aspects such as accuracy, precision, recall, F1-score, area under the curve (AUC), and model-specific indicators- Root Mean Square Error (RMSE), latency, and execution delays. A numerical analysis table summarizes and compares the predictive capabilities of techniques such as Random Forest, CatBoost, Convolutional Neural Network (CNN) ensembles, Long Short-Term Memory (LSTM) autoencoders, Supervisory Control and Data Acquisition (SCADA) IoT frameworks, and Digital Twins. High-performing models, such as CatBoost and custom CNN architectures, indicate the effectiveness of hybrid deep learning strategies in fault diagnostics. The review establishes a new benchmark for evaluating PdM systems, readying the bar between academic innovation and real-world deployment. It outlines future research directions including model generalization, real-time edge AI deployment, and integration with climate-aware forecasting systems. This work complements an important entry point for other works by researchers and industry stakeholders’ intent on deploying scalable and resilient predictive maintenance solutions in renewable energy networks.