Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

Abu Danish Aiman Bin Abu Sofian; Hooi Ren Lim; Heli Siti Halimatul Munawaroh; Zengling Ma; Kit Wayne Chew; Pau Loke Show

doi:10.1002/sd.2885

journal article Open Access Jan 08, 2024

Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

Abu Danish Aiman Bin Abu Sofian

Hooi Ren Lim Heli Siti Halimatul Munawaroh Zengling Ma Kit Wayne Chew

Pau Loke Show

Sustainable Development Vol. 32 No. 4 pp. 3953-3978 · Wiley

View at Publisher Save 10.1002/sd.2885

Abstract

AbstractThis article evaluates the present global condition of solar and wind energy adoption and explores their benefits and limitations in meeting energy needs. It examines the historical and evolutionary growth of solar and wind energy, global trends in the usage of renewable energy, and upcoming technologies, including floating solar and vertical‐axis wind turbines. The importance of smart grid technology and energy storage alternatives for enhancing the effectiveness and dependability of renewable energy is explored. In addition, the role of Electric Vehicles (EVs) in a modern smart grid has been assessed. Furthermore, the economic benefits, and most recent technological developments of solar and wind energy and their environmental and social ramifications. The potential of solar and wind energy to meet the increasing global energy demand and the problems and opportunities facing the renewable energy industry have shown excellent promise. Machine learning applications for solar and wind energy generation are vital for sustainable energy production. Machine learning can help in design, optimization, cost reduction, and, most importantly, in improving the efficacy of solar and wind energy, including advancing energy storage. This assessment is a crucial resource for policymakers, industry leaders, and researchers who aim to make the world cleaner and more sustainable. Ultimately, this review has shown the great potential of solar and wind energy in meeting global energy demands and sustainable goals.

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References

Details

Published: Jan 08, 2024
Vol/Issue: 32(4)
Pages: 3953-3978
License: View

Authors

A

Abu Danish Aiman Bin Abu Sofian

Department of Chemical and Environmental Engineering, Faculty of Science and Engineering University of Nottingham Malaysia Semenyih Malaysia

H

Hooi Ren Lim

Department of Chemical and Environmental Engineering, Faculty of Science and Engineering University of Nottingham Malaysia Semenyih Malaysia

H

Heli Siti Halimatul Munawaroh

Study Program of Chemistry, Faculty of Mathematics and Science Education Universitas Pendidikan Indonesia Bandung Indonesia

Z

Zengling Ma

Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection Wenzhou University Wenzhou China; National and Local Joint Engineering Research Center of Ecological Treatment Technology for Urban Water Pollution Wenzhou University Wenzhou China; College of Life and Environmental Science Wenzhou University Wenzhou China

K

Kit Wayne Chew

School of Chemistry, Chemical Engineering and Biotechnology Nanyang Technological University Singapore Singapore

P

Pau Loke Show

Department of Chemical Engineering Khalifa University Abu Dhabi UAE

Funding

Khalifa University of Science, Technology and Research Award: 8474000580

Cite This Article

Abu Danish Aiman Bin Abu Sofian, Hooi Ren Lim, Heli Siti Halimatul Munawaroh, et al. (2024). Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage. Sustainable Development, 32(4), 3953-3978. https://doi.org/10.1002/sd.2885

Machine learning and the renewable energy revolution: Exploring solar and wind energy solutions for a sustainable future including innovations in energy storage

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