An empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaks

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dc.contributor.authorAbu-Bakar, Halidu
dc.contributor.authorWilliams, Leon
dc.contributor.authorHallett, Stephen H.
dc.date.accessioned2021-07-29T10:43:10Z
dc.date.available2021-07-29T10:43:10Z
dc.date.issued2021-07-18
dc.description.abstractCharacterising individual households’ consumption patterns reliably and ascertaining the extent to which these patterns change and how they underpin aggregate demand continues to present a challenge. This paper presents an empirical characterisation of household water consumption patterns, based on consumer segmentation, to improve the accuracy of demand forecasting and to develop both proactive and responsive water conservation strategies. Medium resolution smart metre data for 2019 for 10,000 households were analysed using Machine Learning (ML), revealing four household clusters whose significant differences are underpinned by a variety of indicators in their temporal consumption patterns. The clusters, labelled according to the predominant peak demand times of constituent households, are ‘Evening Peaks’ (EP), ‘Late Morning’ (LM), ‘Early Morning’ (EM) and ‘Multiple Peaks’ (EP). Some of the significant findings include the fact that on average households in EM only record one peak event in 24 h, compared with the MP clusters’ four peak events, with 2 in every 5 households in MP having a confirmed internal leak compared with 1 in every 5 for the other three clusters. A total of 31,788 Cubic metres (m3) was consumed, constituting a monthly mean of 2,649m3, equating to a per household consumption (PHC) of ~270 litres per household per day (l/h/d). Results also revealed the clusters’ distributed dominance of hourly demand and the most active clusters in different seasons. The paper concludes that identifying the significant differences characterising consumption patterns and their concomitant impact on network demand will not only serve to enhance demand forecasting and the prediction of geographical consumption hotspots but will also allow the delivery of targeted intervention measures according to households’ shared characteristics.en_UK
dc.identifier.citationAbu-Bakar H, Williams L, Hallett SH. (2021) An empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaks. Resources, Conservation and Recycling, Volume 174, November 2021, Article number 105792en_UK
dc.identifier.issn0921-3449
dc.identifier.urihttps://doi.org/10.1016/j.resconrec.2021.105792
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16946
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDemand forecastingen_UK
dc.subjectSegmentationen_UK
dc.subjectConsumption patternsen_UK
dc.subjectDemand-side managementen_UK
dc.subjectHousehold water consumptionen_UK
dc.subjectPeak demanden_UK
dc.titleAn empirical water consumer segmentation and the characterisation of consumption patterns underpinning demand peaksen_UK
dc.typeArticleen_UK

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