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Unlocking renewable energy potential: Harnessing machine learning and intelligent algorithms

1Institute of Engineering, HUTECH University, Ho Chi Minh City, Viet Nam

2Department of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu - 602105, India

3Mechanical Engineering Department, Politeknik Negeri Padang, West Sumatera, Indonesia

4 Institute of Maritime, Ho Chi Minh city University of Transport, Ho Chi Minh City, Viet Nam

5 Faculty of Automotive Engineering, Dong A University, Danang, Viet Nam, Viet Nam

6 Institute of Mechanical Engineering, Ho Chi Minh city University of Transport, Ho Chi Minh City, Viet Nam

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Received: 12 Feb 2024; Revised: 27 May 2024; Accepted: 7 Jun 2024; Available online: 25 Jun 2024; Published: 1 Jul 2024.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2024 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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This review article examines the revolutionary possibilities of machine learning (ML) and intelligent algorithms for enabling renewable energy, with an emphasis on the energy domains of solar, wind, biofuel, and biomass. Critical problems such as data variability, system inefficiencies, and predictive maintenance are addressed by the integration of ML in renewable energy systems. Machine learning improves solar irradiance prediction accuracy and maximizes photovoltaic system performance in the solar energy sector. ML algorithms help to generate electricity more reliably by enhancing wind speed forecasts and wind turbine efficiency. ML improves the efficiency of biofuel production by optimizing feedstock selection, process parameters, and yield forecasts. Similarly, ML models in biomass energy provide effective thermal conversion procedures and real-time process management, guaranteeing increased energy production and operational stability. Even with the enormous advantages, problems such as data quality, interpretability of the models, computing requirements, and integration with current systems still remain. Resolving these issues calls for interdisciplinary cooperation, developments in computer technology, and encouraging legislative frameworks. This study emphasizes the vital role of ML in promoting sustainable and efficient renewable energy systems by giving a thorough review of present ML applications in renewable energy, highlighting continuing problems, and outlining future prospects

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Keywords: Machine learning; Artificial Intelligence; Renewable energy; Waste-to-energy path; Sustainable energy

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