Predictive Maintenance of Renewable Energy Infrastructure Using AI: A Comprehensive Review

Abstract

As the world increasingly shifts toward renewable energy, ensuring that our infrastructure, like wind turbines, solar panels, and hydroelectric plants, runs smoothly is more important than ever. Traditional maintenance methods, including reactive strategies and scheduled checks, often lead to costly downtimes and inefficient resource use. This is where artificial intelligence (AI) can make a difference. By harnessing AI-driven predictive maintenance, we can use advanced machine learning and real-time data from Internet of Things (IoT) sensors to spot potential equipment failures before they happen. This proactive approach allows us to optimize maintenance schedules, cut down on unexpected outages, lower operational costs, and ultimately extend the lifespan of our assets. Techniques like regression analysis, decision trees, neural networks, and deep learning models help us sift through complex data sets to find early signs of wear and tear or other anomalies. We’ve already seen impressive results in the wind and solar sectors, where AI-based maintenance strategies have led to better operational efficiency, significant cost savings, and increased reliability. Future research should aim at developing scalable hybrid AI models, standardizing data practices, and exploring applications across different sectors. Supportive policies and workforce development will also be critical in this journey. By blending AI with IoT technologies and sustainable practices, predictive maintenance will become essential in optimizing renewable energy systems, minimizing environmental impacts, and aiding the global move towards a low-carbon economy.