A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning.

Jathar, LD; Nikam, K; Awasarmol, UV; Gurav, R; Patil, JD; Shahapurkar, K; Soudagar, MEM; Khan, TMY; Kalam, MA; Hnydiuk-Stefan, A; Gürel, AE; Hoang, AT; Ağbulut, Ü

A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning.

Jathar, LD Nikam, K Awasarmol, UV Gurav, R Patil, JD Shahapurkar, K Soudagar, MEM Khan, TMY Kalam, MA Hnydiuk-Stefan, A Gürel, AE Hoang, AT Ağbulut, Ü

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Heliyon, 2024, 10, (3), pp. e25407
Issue Date:: 2024-02-15

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Field	Value	Language
dc.contributor.author	Jathar, LD
dc.contributor.author	Nikam, K
dc.contributor.author	Awasarmol, UV
dc.contributor.author	Gurav, R
dc.contributor.author	Patil, JD
dc.contributor.author	Shahapurkar, K
dc.contributor.author	Soudagar, MEM
dc.contributor.author	Khan, TMY
dc.contributor.author	Kalam, MA
dc.contributor.author	Hnydiuk-Stefan, A
dc.contributor.author	Gürel, AE
dc.contributor.author	Hoang, AT
dc.contributor.author	Ağbulut, Ü
dc.date.accessioned	2024-04-10T06:52:07Z
dc.date.available	2024-01-25
dc.date.available	2024-04-10T06:52:07Z
dc.date.issued	2024-02-15
dc.identifier.citation	Heliyon, 2024, 10, (3), pp. e25407
dc.identifier.issn	2405-8440
dc.identifier.issn	2405-8440
dc.identifier.uri	http://hdl.handle.net/10453/177676
dc.description.abstract	Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.
dc.format	Electronic-eCollection
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Heliyon
dc.relation.isbasedon	10.1016/j.heliyon.2024.e25407
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning.
dc.type	Journal Article
utslib.citation.volume	10
utslib.location.activity	England
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-10T06:51:55Z
pubs.issue	3
pubs.publication-status	Published online
pubs.volume	10
utslib.citation.issue	3

Abstract:

Integration of photovoltaic (PV) systems, desalination technologies, and Artificial Intelligence (AI) combined with Machine Learning (ML) has introduced a new era of remarkable research and innovation. This review article thoroughly examines the recent advancements in the field, focusing on the interplay between PV systems and water desalination within the framework of AI and ML applications, along with it analyses current research to identify significant patterns, obstacles, and prospects in this interdisciplinary field. Furthermore, review examines the incorporation of AI and ML methods in improving the performance of PV systems. This includes raising their efficiency, implementing predictive maintenance strategies, and enabling real-time monitoring. It also explores the transformative influence of intelligent algorithms on desalination techniques, specifically addressing concerns pertaining to energy usage, scalability, and environmental sustainability. This article provides a thorough analysis of the current literature, identifying areas where research is lacking and suggesting potential future avenues for investigation. These advancements have resulted in increased efficiency, decreased expenses, and improved sustainability of PV system. By utilizing artificial intelligence technologies, freshwater productivity can increase by 10 % and efficiency. This review offers significant and informative perspectives for researchers, engineers, and policymakers involved in renewable energy and water technology. It sheds light on the latest advancements in photovoltaic systems and desalination, which are facilitated by AI and ML. The review aims to guide towards a more sustainable and technologically advanced future.

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http://hdl.handle.net/10453/177676