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

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

Efficient 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 ener...

Full description

Saved in:

Bibliographic Details
Main Authors:	Elixie A.E., Alkahtani A.A., Alkawsi G., Salle S.F., Fazea Y., Ekanayake J.
Other Authors:	57221747754
Format:	Article
Published:	Natural Sciences Publishing 2023
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.uniten.dspace-25629
record_format	dspace
spelling	my.uniten.dspace-256292023-05-29T16:11:57Z Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study Elixie A.E. Alkahtani A.A. Alkawsi G. Salle S.F. Fazea Y. Ekanayake J. 57221747754 55646765500 57191982354 57221743470 56803894200 7003409510 Efficient 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. Final 2023-05-29T08:11:56Z 2023-05-29T08:11:56Z 2020 Article 10.18576/amis/140609 2-s2.0-85100054790 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100054790&doi=10.18576%2famis%2f140609&partnerID=40&md5=80b098704c7137c61f9fd014ea20ea3c https://irepository.uniten.edu.my/handle/123456789/25629 14 6 1017 1027 Natural Sciences Publishing Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Efficient 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.
author2	57221747754
author_facet	57221747754 Elixie A.E. Alkahtani A.A. Alkawsi G. Salle S.F. Fazea Y. Ekanayake J.
format	Article
author	Elixie A.E. Alkahtani A.A. Alkawsi G. Salle S.F. Fazea Y. Ekanayake J.
spellingShingle	Elixie A.E. Alkahtani A.A. Alkawsi G. Salle S.F. Fazea Y. Ekanayake J. Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study
author_sort	Elixie A.E.
title	Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study
title_short	Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study
title_full	Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study
title_fullStr	Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study
title_full_unstemmed	Non-Intrusive Electrical Load Monitoring and Identification: Approaches, Tools and a Case Study
title_sort	non-intrusive electrical load monitoring and identification: approaches, tools and a case study
publisher	Natural Sciences Publishing
publishDate	2023
_version_	1806427838502404096
score	13.222552

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

Similar Items