AI-Driven Predictive Maintenance for Renewable Energy Infrastructure

Abstract

Renewable energy systems, including wind, solar photovoltaic (PV), hydroelectric, biomass, and hybrid energy systems, play a vital role in sustainable energy generation and reducing greenhouse gas emissions. However, harsh operating conditions, equipment aging, and mechanical and electrical failures can significantly affect their reliability and performance. Traditional maintenance approaches often fail to detect faults at an early stage, resulting in increased costs, unexpected downtime, and reduced energy production. Artificial Intelligence (AI)-based predictive maintenance has emerged as an effective solution by combining real-time sensor data, historical records, and environmental information to predict equipment failures before they occur. Advanced AI techniques, including Machine Learning (ML), Deep Learning (DL), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Support Vector Machines (SVM), Random Forests (RF), and Transformer models, enable accurate fault diagnosis, anomaly detection, and Remaining Useful Life (RUL) estimation. This study reviews recent AI-driven predictive maintenance approaches, identifies key research gaps, and proposes an intelligent framework integrating IoT, edge computing, cloud platforms, deep learning, and Explainable AI (XAI). The proposed framework improves fault prediction, reduces downtime, and extends equipment lifespan, and supports reliable, sustainable, and intelligent renewable energy systems for future smart grid and Industry 5.0 applications.