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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.contributor.authorJathar, Laxmikant D.
dc.contributor.authorNikam, Keval
dc.contributor.authorV. Awasarmol, Umesh
dc.contributor.authorGurav, Raviraj
dc.contributor.authorPatil, Jitendra D.
dc.contributor.authorShahapurkar, Kiran
dc.contributor.authorSoudagar, Manzoore Elahi M.
dc.contributor.authorKhan, T. M. Yunus
dc.contributor.authorKalam, M. A.
dc.contributor.authorHnydiuk-Stefan, Anna
dc.contributor.authorGurel, Ali Etem
dc.contributor.authorHoang, Anh Tuan
dc.contributor.authorAgbulut, Umit
dc.date.accessioned2026-06-27T15:07:54Z
dc.date.issued2024
dc.description.abstractIntegration 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.en
dc.description.sponsorshipDeanship of Scientific Research at King Khalid University [R.G.P. 1/182/44]
dc.description.urihttps://doi.org/10.1016/j.heliyon.2024.e25407
dc.identifier.doi10.1016/j.heliyon.2024.e25407
dc.identifier.eissn2405-8440
dc.identifier.issue3
dc.identifier.pubmed38371991
dc.identifier.urihttps://hdl.handle.net/20.500.14981/68314
dc.identifier.volume10
dc.identifier.wos001182267100001
dc.language.isoeng
dc.publisherCELL PRESS
dc.relation.ispartofHELIYON
dc.rightsopenAccess
dc.subjectArtificial intelligence
dc.subjectMachine learning
dc.subjectPhotovoltaic
dc.subjectDesalination
dc.subjectPOWER POINT TRACKING
dc.subjectGLOBAL SOLAR-RADIATION
dc.subjectFUZZY INFERENCE SYSTEM
dc.subjectALONE PV SYSTEM
dc.subjectNEURAL-NETWORK
dc.subjectFAULT-DIAGNOSIS
dc.subjectPARAMETER-IDENTIFICATION
dc.subjectOPTIMIZATION ALGORITHM
dc.subjectMPPT TECHNIQUES
dc.subjectGENETIC ALGORITHMS
dc.subjectScience & Technology - Other Topics
dc.titleA comprehensive analysis of the emerging modern trends in research on photovoltaic systems and desalination in the era of artificial intelligence and machine learning
dc.typeReview
dspace.entity.typePublication
local.import.sourceWOS

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