Show simple item record

dc.contributor.authorCominola, A
dc.contributor.authorNguyen, K
dc.contributor.authorGiuliani, M
dc.contributor.authorStewart, RA
dc.contributor.authorMaier, HR
dc.contributor.authorCastelletti, A
dc.date.accessioned2019-12-16T01:48:20Z
dc.date.available2019-12-16T01:48:20Z
dc.date.issued2019
dc.identifier.issn0043-1397
dc.identifier.doi10.1029/2019WR024897
dc.identifier.urihttp://hdl.handle.net/10072/389799
dc.description.abstractKnowledge on the determinants and patterns of water demand for different consumers supports the design of customized demand management strategies. Smart meters coupled with big data analytics tools create a unique opportunity to support such strategies. Yet, at present, the information content of smart meter data is not fully mined and usually needs to be complemented with water fixture inventory and survey data to achieve detailed customer segmentation based on end use water usage. In this paper, we developed a data‐driven approach that extracts information on heterogeneous water end use routines, main end use components, and temporal characteristics, only via data mining existing smart meter readings at the scale of individual households. We tested our approach on data from 327 households in Australia, each monitored with smart meters logging water use readings every 5 s. As part of the approach, we first disaggregated the household‐level water use time series into different end uses via Autoflow. We then adapted a customer segmentation based on eigenbehavior analysis to discriminate among heterogeneous water end use routines and identify clusters of consumers presenting similar routines. Results revealed three main water end use profile clusters, each characterized by a primary end use: shower, clothes washing, and irrigation. Time‐of‐use and intensity‐of‐use differences exist within each class, as well as different characteristics of regularity and periodicity over time. Our customer segmentation analysis approach provides utilities with a concise snapshot of recurrent water use routines from smart meter data and can be used to support customized demand management strategies.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherAmerican Geophysical Union
dc.relation.ispartofjournalWater Resources Research
dc.subject.fieldofresearchPhysical geography and environmental geoscience
dc.subject.fieldofresearchCivil engineering
dc.subject.fieldofresearchEnvironmental engineering
dc.subject.fieldofresearchcode3709
dc.subject.fieldofresearchcode4005
dc.subject.fieldofresearchcode4011
dc.subject.keywordsScience & Technology
dc.subject.keywordsLife Sciences & Biomedicine
dc.subject.keywordsPhysical Sciences
dc.subject.keywordsEnvironmental Sciences
dc.subject.keywordsLimnology
dc.titleData Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationCominola, A; Nguyen, K; Giuliani, M; Stewart, RA; Maier, HR; Castelletti, A, Data Mining to Uncover Heterogeneous Water Use Behaviors From Smart Meter Data, Water Resources Research, 2019
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.date.updated2019-12-16T01:25:59Z
dc.description.versionVersion of Record (VoR)
gro.description.notepublicThis publication has been entered into Griffith Research Online as an Advanced Online Version.
gro.rights.copyright©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Khoi A.
gro.griffith.authorStewart, Rodney 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