Show simple item record

dc.contributor.authorNguyen, KA
dc.contributor.authorStewart, RA
dc.contributor.authorZhang, H
dc.date.accessioned2017-05-03T12:25:11Z
dc.date.available2017-05-03T12:25:11Z
dc.date.issued2013
dc.date.modified2013-10-02T23:41:36Z
dc.identifier.issn1364-8152
dc.identifier.doi10.1016/j.envsoft.2013.05.002
dc.identifier.urihttp://hdl.handle.net/10072/53484
dc.description.abstractThe rapid dissemination of residential water end-use (e.g. shower, clothes washer, etc.) consumption data to the customer via a web-enabled portal interface is becoming feasible through the advent of high resolution smart metering technologies. However, in order to achieve this paradigm shift in residential customer water use feedback, an automated approach for disaggregating complex water flow trace signatures into a registry of end-use event categories needs to be developed. This outcome is achieved by applying a hybrid combination of gradient vector filtering, Hidden Markov Model (HMM) and Dynamic Time Warping Algorithm (DTW) techniques on an existing residential water end-use database of 252 households located in South-east Queensland, Australia having high resolution water meters (0.0139 L/pulse), remote data transfer loggers (5 s logging) and completed household water appliance audits. The approach enables both single independent events (e.g. shower event) and combined events (i.e. several overlapping single events) to be disaggregated from flow data into a comprehensive end-use event registry. Complex blind source separation of concurrently occurring water end use events (e.g. shower and toilet flush occurring in same time period) is the primary focus of this present study. Validation of the developed model is achieved through an examination of 50 independent combined events.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent908011 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.publisher.placeUnited Kingdom
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom108
dc.relation.ispartofpageto127
dc.relation.ispartofjournalEnvironmental Modelling & Software
dc.relation.ispartofvolume47
dc.rights.retentionY
dc.subject.fieldofresearchWater resources engineering
dc.subject.fieldofresearchcode400513
dc.titleAn intelligent pattern recognition model to automate the categorisation of residential water end-use events
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2013 Elsevier. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. Please refer to the journal's website for access to the definitive, published version.
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorStewart, Rodney A.
gro.griffith.authorZhang, Hong
gro.griffith.authorNguyen, Khoi A.


Files in this item

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record