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AI-driven predictive maintenance and optimization of renewable energy systems for enhanced operational efficiency and longevity
1 Department of Mechanical Engineering, University of Ibadan, Nigeria.
2 Department of Information Technology Services, Washburn University, Topeka, KS USA.
3 Department of Geosciences, Texas Tech University, Lubbock TX.
4 Department of Computer Science, Montclair State University, New Jersey, USA.
5 Department of Chemical Engineering, Ladoke Akintola University of Technology Ogbomosho, Oyo Nigeria.
Review
International Journal of Science and Research Archive, 2024, 13(01), 2823–2837.
Article DOI: 10.30574/ijsra.2024.13.1.1992
Publication history:
Received on 04 September 2024; revised on 17 October 2024; accepted on 19 October 2024
Abstract:
The rapid growth of renewable energy systems necessitates advanced strategies for maintenance and optimization to ensure long-term operational efficiency and sustainability. Traditional approaches often fall short in predicting failures and optimizing performance across diverse and dynamic renewable energy infrastructures. This study investigates the application of artificial intelligence (AI) techniques for predictive maintenance and optimization of renewable energy systems, with the aim of enhancing operational efficiency and extending system longevity. We employ a combination of machine learning algorithms, including deep neural networks and reinforcement learning, to develop predictive models and optimization strategies. These models are trained on large-scale datasets collected from operational wind farms, solar installations, and hydroelectric plants. Our results demonstrate that AI-driven approaches can predict equipment failures with 92% accuracy, reducing unplanned downtime by 35% compared to traditional methods. Moreover, AI-optimized operational parameters improved overall energy output by 8.5% across the studied systems. The proposed framework also showed adaptability to various environmental conditions and system configurations, suggesting broad applicability across the renewable energy sector. This research underscores the significant potential of AI in revolutionizing maintenance practices and operational strategies in renewable energy systems, paving the way for more reliable, efficient, and sustainable clean energy production.
Keywords:
Artificial intelligence (AI); Renewable energy systems; Predictive maintenance; Operational optimization; Machine learning; Deep neural networks
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
