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dc.contributor.authorKhoi, Anh Nguyen
dc.contributor.authorStewart, Rodney A
dc.contributor.authorZhang, Hong
dc.contributor.authorJones, Christopher
dc.date.accessioned2017-06-19T02:11:23Z
dc.date.available2017-06-19T02:11:23Z
dc.date.issued2015
dc.identifier.issn1568-4946
dc.identifier.doi10.1016/j.asoc.2015.03.007
dc.identifier.urihttp://hdl.handle.net/10072/69202
dc.description.abstractOver half of the world's population will live in urban areas in the next decade, which will impose significant pressure on water security. The advanced management of water resources and their consumption is pivotal to maintaining a sustainable water future. To contribute to this goal, the aim of this study was to develop an autonomous and intelligent system for residential water end-use classification that could interface with customers and water business managers via a user-friendly web-based application. Water flow data collected directly from smart water meters connected to dwellings includes both single (e.g., a shower event occurring alone) and combined (i.e., an event that comprises several overlapping single events) water end use events. The authors recently developed an intelligent application called Autoflow which served as a prototype tool to solve the complex problem of autonomously categorising residential water consumption data into a registry of single and combined events. However, this first prototype application achieved overall recognition accuracy of 85%, which is not sufficient for a commercial application. To improve this accuracy level, a larger dataset consisting of over 82 thousand events from over 500 homes in Melbourne and South-east Queensland, Australia, were employed to derive a new single event recognition method employing a hybrid combination of Hidden Markov Model (HMM), Artificial Neural Networks (ANN) and the Dynamic Time Warping (DTW) algorithm. The classified single event registry was then used as the foundations of a sophisticated hybrid ANN-HMM combined event disaggregation module, which was able to strip apart concurrently occurring end use events. The new hybrid model's recognition accuracy ranged from 85.9 to 96.1% for single events and 81.8 to 91.5% for combined event disaggregation, which was a 4.9% and 8.0% improvement, respectively, when compared to the first prototype model. The developed Autoflow tool has far-reaching implications for enhanced urban water demand planning and management, sustained customer behaviour change through more granular water conservation awareness, and better customer satisfaction with water utility providers.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeNetherlands
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom118
dc.relation.ispartofpageto131
dc.relation.ispartofjournalApplied Soft Computing
dc.relation.ispartofvolume31
dc.rights.retentionY
dc.subject.fieldofresearchApplied mathematics
dc.subject.fieldofresearchInformation systems
dc.subject.fieldofresearchWater resources engineering
dc.subject.fieldofresearchcode4901
dc.subject.fieldofresearchcode4609
dc.subject.fieldofresearchcode400513
dc.titleIntelligent autonomous system for residential water end use classification: Autoflow
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorStewart, Rodney A.
gro.griffith.authorZhang, Hong
gro.griffith.authorNguyen, Khoi A.


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