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Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study

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dc.contributor.authorElixie A.E.en_US
dc.contributor.authorAlkahtani A.A.en_US
dc.contributor.authorAlkawsi G.en_US
dc.contributor.authorSalle S.F.en_US
dc.contributor.authorFazea Y.en_US
dc.contributor.authorEkanayake J.en_US
dc.contributor.authorid57221747754en_US
dc.contributor.authorid55646765500en_US
dc.contributor.authorid57191982354en_US
dc.contributor.authorid57221743470en_US
dc.contributor.authorid56803894200en_US
dc.contributor.authorid7003409510en_US
dc.date.accessioned2023-05-29T08:11:56Z
dc.date.available2023-05-29T08:11:56Z
dc.date.issued2020
dc.description.abstractEfficient energy consumption has always been of significant interest to decision-makers in many countries. Awareness, knowledge and a real understanding of proper use of energy patterns is a key element in improving consumption behaviour. Despite the amount of available knowledge on how to save energy, many consumers still fail to take noticeable steps to enhance energy efficiency and conservation. Many significant and innovative studies have been conducted, yet there is still room for more sophisticated approaches to persuade users to optimize energy consumption. Therefore, integrating the Internet-of-Things (IoT) devices such as smart meters and mobile applications in a coherent framework would be one solution to achieving the desired changes in energy consumption behaviour. The present paper investigates current work in progress for optimizing energy use with IoT devices to provide sufficient feedback for users. This paper adopts a non-intrusive load monitoring algorithm (NILM) to assist in generating a recommender system based on smart meter data. The NILM identifies appliances and patterns of user consumption behaviour and disaggregates consumption of individual appliances from a single-point smart meter data. The results benefits not only household consumers but also energy providers and top decision-makers. � 2020. Natural Sciences Publishing Cor. All Rights Reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.18576/amis/140609
dc.identifier.epage1027
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85100054790
dc.identifier.spage1017
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85100054790&doi=10.18576%2famis%2f140609&partnerID=40&md5=80b098704c7137c61f9fd014ea20ea3c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25629
dc.identifier.volume14
dc.publisherNatural Sciences Publishingen_US
dc.sourceScopus
dc.sourcetitleApplied Mathematics and Information Sciences
dc.titleNon-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Studyen_US
dc.typeArticleen_US
dspace.entity.typePublication
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