Leveraging Artificial Intelligence for optimizing renewable energy systems: A pathway to environmental sustainability

Chisom Assumpta Nnajiofor 1, Daniel Edet Eyo 2, *, Adedolapo Olujuwon Adegbite 3, Isyaku Abdullahi Odoguje 4, Elizabeth W. Salako 5, Femi Emmanuel Folorunsho 6 and Abdulafees Adeolu Adeyeye 7

1 Global Public Policy, Suffolk University, Boston, MA, USA.
2 Mechanical Engineering, School of Computing, Engineering and Digital Technology, Teesside University (SCEDT), Middlesbrough, UK.
3 D&I Solution, Schlumberger Oilfield Services, Port Harcourt, Nigeria.
4 Computer Science, Montclair State University, NJ, USA.
5 School of Chemical and Biomedical Sciences, Southern Illinois University, Carbondale, IL, USA.
6 Media (Emerging Media and Design Development), Ball State University, Muncie Indiana USA.
7 Supervision-Infrastructure, Khatib & Alami Consolidated Engineering Company, Doha, Qatar.
 
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(03), 2659–2665
Article DOI: 10.30574/wjarr.2024.23.3.2934
 
Publication history: 
Received on 14 July 2024; revised on 14 August 2024; accepted on 02 September 2024
 
Abstract: 
Artificial intelligence (AI) has emerged as a key enabler in optimizing renewable energy systems, significantly contributing to global efforts toward environmental sustainability. This review explores the application of AI technologies in enhancing the efficiency, reliability, and integration of renewable energy sources such as solar, wind, and hydropower. It focuses on how machine learning (ML), deep learning (DL), and other AI-driven algorithms improve energy forecasting, grid management, and storage optimization. Survey data and case studies demonstrate the potential of AI to minimize energy waste, reduce costs, and lower greenhouse gas emissions, reinforcing its role in transitioning to a sustainable energy future. The review concludes with a discussion of challenges and future research directions.
 
Keywords: 
Artificial Intelligence; Renewable Energy; Machine Learning (ML); Deep Learning (DL); Storage Optimization; Environmental Sustainability
 
Full text article in PDF: 
 
Copyright information: 
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

 

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