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World Journal of Advanced Research and Reviews
Article . 2024 . Peer-reviewed
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ZENODO
Article . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
ZENODO
Article . 2024
License: CC BY
Data sources: Datacite
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Artificial intelligence (AI) in renewable energy: A review of predictive maintenance and energy optimization

Authors: Shedrack Onwusinkwue; Femi Osasona; Islam Ahmad Ibrahim Ahmad; Anthony Chigozie Anyanwu; Samuel Onimisi Dawodu; Ogugua Chimezie Obi; Ahmad Hamdan;

Artificial intelligence (AI) in renewable energy: A review of predictive maintenance and energy optimization

Abstract

The integration of Artificial Intelligence (AI) in the renewable energy sector has emerged as a transformative force, enhancing the efficiency and sustainability of energy systems. This paper provides a comprehensive review of the application of AI in two critical aspects of renewable energy in relation to predictive maintenance and energy optimization. Predictive maintenance, enabled by AI, has revolutionized the renewable energy landscape by predicting and preventing equipment failures before they occur. Utilizing machine learning algorithms, AI analyzes vast amounts of data from sensors and historical performance to identify patterns indicative of potential faults. This proactive approach not only minimizes downtime but also extends the lifespan of renewable energy infrastructure, resulting in substantial cost savings and improved reliability. Furthermore, AI plays a pivotal role in optimizing the energy output of renewable sources. Through advanced data analytics and real-time monitoring, AI algorithms can adapt to changing environmental conditions, predicting energy production patterns and optimizing resource allocation. This ensures maximum energy yield from renewable sources, making them more competitive with traditional energy sources. The paper delves into specific AI techniques such as deep learning, neural networks, and predictive analytics employed for predictive maintenance and energy optimization in various renewable energy systems like solar, wind, and hydropower. Challenges and opportunities associated with implementing AI in renewable energy are discussed, including data security, interoperability, and the need for standardized frameworks. The synthesis of AI technologies with renewable energy not only addresses operational challenges but also contributes to the global transition towards sustainable and clean energy solutions. This review serves as a valuable resource for researchers, practitioners, and policymakers seeking insights into the evolving landscape of AI applications in the renewable energy sector. As technology continues to advance, the synergies between AI and renewable energy are poised to shape the future of the global energy paradigm.

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Keywords

Artificial Intelligence, Predictive Maintenance, Energy Optimization, Renewable Energy, Review

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
59
Top 1%
Top 10%
Top 1%
gold