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dc.contributor.authorNguyen, KA
dc.contributor.authorStewart, RA
dc.contributor.authorZhang, H
dc.contributor.authorChowdhury, SH
dc.contributor.editorWeber, T
dc.contributor.editorMcPhee, MJ
dc.contributor.editorAnderssen, RS
dc.date.accessioned2018-03-07T03:49:27Z
dc.date.available2018-03-07T03:49:27Z
dc.date.issued2015
dc.identifier.isbn9780987214355
dc.identifier.urihttp://hdl.handle.net/10072/123396
dc.description.abstractThe current population boom has pushed demand on residential land in most world metropolitan cities to its extreme limit, and also imposed significant pressure on water security. To ease this problem, construction of residential apartment building has been the top priority of the governments. With the rapid advancement in technology, especially in water metering area, the next generation of these buildings is expected to not only offer their residents a comfortable living area but also make the water supply and management become much more effectively controllable. 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 smartphone and webbased applications. 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. This model was developed using data collected in several cities in Australia, and when applied on single residential property, the achieved accuracy ranged from 86% - 96%. The only limitation of Autoflow© is that when being deployed overseas, a new water end use dataset must be collected to retrain the model to adapt to the new regions. The ultimate goal of this study is to develop the next Autoflow© generation, called AutoflowAB, which can be applied in all future apartment buildings to help disaggregate water consumption autonomously into the six main categories including: shower, toilet, tap, clothes washer, dishwasher and evaporative air cooler without relying on any previously collected data for training. The key employed techniques are Dynamic Time Warping (DTW) and Decision Tree. The model has been tested on 150 residential properties in Australia where the accuracy ranges from 87 – 94 %, and also planned to be applied on 30 new 7-story buildings in the Commonwealth Games Village located in Gold Coast Australia.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherModelling and Simulation Society of Australia and New Zealand
dc.publisher.placeAustralia
dc.publisher.urihttp://www.mssanz.org.au/modsim2015/
dc.relation.ispartofconferencename21st International Congress on Modelling and Simulation (MODSIM) held jointly with the 23rd National Conference of the Australian-Society-for-Operations-Research / DSTO led Defence Operations Research Symposium (DORS
dc.relation.ispartofconferencetitle21ST INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2015)
dc.relation.ispartofdatefrom2015-11-29
dc.relation.ispartofdateto2015-12-04
dc.relation.ispartoflocationGold Coast, AUSTRALIA
dc.relation.ispartofpagefrom1476
dc.relation.ispartofpagefrom7 pages
dc.relation.ispartofpageto1482
dc.relation.ispartofpageto7 pages
dc.subject.fieldofresearchCivil engineering not elsewhere classified
dc.subject.fieldofresearchcode400599
dc.titleIntelligent model to categorise mechanised water end uses
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionVersion of Record (VoR)
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2015 Modellling & Simulation Society of Australia & New Zealand. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the author(s).
gro.hasfulltextFull Text
gro.griffith.authorStewart, Rodney A.
gro.griffith.authorChowdhury, Sanaul H.
gro.griffith.authorZhang, Hong
gro.griffith.authorNguyen, Khoi A.


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