Publication:
Criteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substations

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dc.citedby4
dc.contributor.authorAzhar N.A.en_US
dc.contributor.authorRadzi N.A.M.en_US
dc.contributor.authorAzmi K.H.M.en_US
dc.contributor.authorSamidi F.S.en_US
dc.contributor.authorZainal A.M.en_US
dc.contributor.authorid57219033091en_US
dc.contributor.authorid57218936786en_US
dc.contributor.authorid57982272200en_US
dc.contributor.authorid57215054855en_US
dc.contributor.authorid57641618700en_US
dc.date.accessioned2023-05-29T09:37:45Z
dc.date.available2023-05-29T09:37:45Z
dc.date.issued2022
dc.description.abstractIn the future, as populations grow and more end-user applications become available, the current traditional electrical distribution substation will not be able to fully accommodate new applications that may arise. Consequently, there will be numerous difficulties, including network congestion, latency, jitter, and, in the worst-case scenario, network failure, among other things. Thus, the purpose of this study is to assist decision makers in selecting the most appropriate communication technologies for an electrical distribution substation through an examination of the criteria�s in-fluence on the selection process. In this study, nine technical criteria were selected and processed using machine learning (ML) software, RapidMiner, to find the most optimal technical criteria. Several ML techniques were studied, and Na�ve Bayes was chosen, as it showed the highest performance among the rest. From this study, the criteria were ranked in order of importance from most important to least important based on the average value obtained from the output. Seven technical criteria were identified as being important and should be evaluated in order to determine the most appropriate communication technology solution for electrical distribution substation as a result of this study. � 2022 by the authors. Licensee MDPI, Basel, Switzerland.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo3878
dc.identifier.doi10.3390/app12083878
dc.identifier.issue8
dc.identifier.scopus2-s2.0-85128871411
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85128871411&doi=10.3390%2fapp12083878&partnerID=40&md5=a47dc46accd228f58344ca52d365b11e
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26918
dc.identifier.volume12
dc.publisherMDPIen_US
dc.relation.ispartofAll Open Access, Gold
dc.sourceScopus
dc.sourcetitleApplied Sciences (Switzerland)
dc.titleCriteria Selection Using Machine Learning (ML) for Communication Technology Solution of Electrical Distribution Substationsen_US
dc.typeArticleen_US
dspace.entity.typePublication
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