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dc.contributor.authorRahim, Md Shamsur
dc.contributor.authorNguyen, Khoi Anh
dc.contributor.authorStewart, Rodney Anthony
dc.contributor.authorAhmed, Tanvir
dc.contributor.authorGiurco, Damien
dc.contributor.authorBlumenstein, Michael
dc.date.accessioned2021-10-12T03:55:26Z
dc.date.available2021-10-12T03:55:26Z
dc.date.issued2021
dc.identifier.issn0950-7051
dc.identifier.doi10.1016/j.knosys.2021.107522
dc.identifier.urihttp://hdl.handle.net/10072/408904
dc.description.abstractWater utility companies in urban areas face two major challenges: ensuring there is enough water for everyone during prolonged drought and maintaining adequate water pressure during the hours of peak demand. These issues can be overcome by applying data analytics and machine learning to the data gathered from digital water meters. For water conservation and demand management strategies to be effective, utility companies need to gain a better understanding of consumer behaviours, habits and routines. To accomplish this goal, we adapted a clustering approach to reveal residential water consumption patterns within metered data. In the experiment, we used two data sets (engineered features data set as well as the times of use and weighted probabilities of use data set) based on the data collected over 10 months from 306 households in Melbourne, Australia. For the engineered features data set, first, we identified the number of optimal clusters. We then performed extensive experiments to find the best clustering approach in terms of performance evaluation and clustering quality. We chose the hierarchical agglomerative clustering technique based on the nature of the data and the objective of the study. We observed that for the engineered features data set, k-means is the best performing clustering technique after considering performance metrics. For the other data set, we found that the number of clusters varies based on the type of water-consumption event, type of day (i.e., weekday or weekend), profiling interval and probability of use. In addition, we observed that insight into tap-water usage could be used to determine the population’s adaptation of hygiene practices in an unprecedented time, such as the COVID-19 pandemic. Finally, we recommend that future clustering studies also employ aligned socio-demographic data and other key features.
dc.description.peerreviewedYes
dc.languageen
dc.publisherElsevier BV
dc.relation.ispartofpagefrom107522
dc.relation.ispartofjournalKnowledge-Based Systems
dc.subject.fieldofresearchUrban and regional planning
dc.subject.fieldofresearchBuilt environment and design
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchcode3304
dc.subject.fieldofresearchcode33
dc.subject.fieldofresearchcode46
dc.titleA clustering solution for analyzing residential water consumption patterns
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationRahim, MS; Nguyen, KA; Stewart, RA; Ahmed, T; Giurco, D; Blumenstein, M, A clustering solution for analyzing residential water consumption patterns, Knowledge-Based Systems, 2021, pp. 107522
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated2021-10-11T00:43:06Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2021 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (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.authorNguyen, Khoi A.


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