%0 Journal Article
%T AI-Driven Predictive Maintenance in Renewable Energy Infrastructure
%A Olusegun Samuel Ojuekaiye
%J Open Access Library Journal
%V 12
%N 11
%P 1-30
%@ 2333-9721
%D 2025
%I Open Access Library
%R 10.4236/oalib.1113769
%X This paper explores the integration of Artificial Intelligence (AI) into renewable energy infrastructure, with a particular focus on AI-driven predictive maintenance techniques. As renewable energy systems such as solar, wind, and wave power gain prominence in global energy transition efforts, challenges such as intermittency, wear and tear, and inefficiencies in maintenance practices persist. Predictive maintenance, powered by advanced AI technologies like machine learning, deep learning, and reinforcement learning, has the potential to enhance system reliability, reduce operational costs, and optimize energy production. This study reviews the historical development of maintenance strategies in energy systems, identifies the benefits of AI in predictive maintenance, and highlights its role in improving the sustainability and efficiency of renewable energy infrastructure. Furthermore, it discusses the impact of AI on grid stability and energy storage in decentralized systems, contributing to the overall reliability of renewable energy net-works. The study also emphasizes the importance of policy frameworks, investment in data collection technologies, and stakeholder collaboration in advancing AI-driven innovations. By examining real-world applications and challenges, this paper provides valuable insights into the future of AI in renewable energy, suggesting pathways for maximizing its potential to achieve global sustainability goals.
%K Artificial Intelligence
%K Predictive Maintenance
%K Renewable Energy
%K Machine Learning
%K Deep Learning
%K Energy Infrastructure
%K Sustainability
%K Grid Stability
%U http://www.oalib.com/paper/6865414