Non-intrusive Load Monitoring of Residential Water-heating Circuit Using Ensemble Machine Learning Techniques

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aut.relation.endpage57
aut.relation.issue4en_NZ
aut.relation.journalInventionsen_NZ
aut.relation.startpage57
aut.relation.volume5en_NZ
aut.researcherLie, Tek
dc.contributor.authorRehman, AUen_NZ
dc.contributor.authorLie, TTen_NZ
dc.contributor.authorVallès, Ben_NZ
dc.contributor.authorTito, SRen_NZ
dc.date.accessioned2020-11-24T22:29:46Z
dc.date.available2020-11-24T22:29:46Z
dc.description.abstractThe recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.en_NZ
dc.identifier.citationInventions. 2020; 5(4):57.
dc.identifier.doi10.3390/inventions5040057en_NZ
dc.identifier.issn2411-5134en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/13817
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/2411-5134/5/4/57
dc.rights© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectMachine learning; Neural networks; Ensemble learning; Load inference; Event detection; Feature selection; Water heating
dc.titleNon-intrusive Load Monitoring of Residential Water-heating Circuit Using Ensemble Machine Learning Techniquesen_NZ
dc.typeJournal Article
pubs.elements-id394731
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/Faculty Central
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