Water use signature patterns for analyzing household consumption using medium resolution meter data

Abstract

[1] Providers of potable water to households and businesses are charged with conserving water. Addressing this challenge requires accurate information about how water is actually being used. So smart meters are being deployed on a large scale by water providers to collect medium resolution water use data. This paper presents water use signature patterns, the first technique designed for medium resolution meters for discovering patterns that explain how households use water. Signature patterns are clusters (subsets) of water meter readings specified by patterns on volumes and calendar dates. Four types of signature pattern are introduced in this paper: continuous flow days; exceptional peak use days; programmed patterns with recurrent hours; and normal use partitioned by season and period of the day. Signature patterns for each household are calculated using efficient selection rules that scale for city populations and years of data collection. Data from a real-world, large-scale, smart metering trial are analyzed using water use signature patterns. The results demonstrate that water use behaviors are distinctive, for both individuals and populations. Signatures can identify behaviors that are promising targets for water conservation. Pattern discovery can be automated with an efficient and scalable computer program. By identifying relevant consumption patterns in medium resolution meter data, water use signature patterns can help to achieve the water conservation potential of large-scale smart metering.Citation: Cardell-Oliver, R. (2013), Water use signature patterns for analyzing household consumption using medium resolution meter data, Water Resour. Res., 49,[8589][8590][8591][8592][8593][8594][8595][8596][8597][8598][8599]

Flow‐Signature Analysis of Water Consumption in Nonresidential Building Water Networks Using High‐Resolution and Medium‐Resolution Smart Meter Data: Two Case Studies

“…This reflects the well‐known statement, “If you can't measure it, you can't manage it” which has been used increasingly in the water industry (Hearn, ; Hood, ; Paisley & Henshaw, ). In the past, water bills issued by utility managers have been an oblique form of water consumption metric employed in residential and nonresidential buildings (DeOreo et al, ) which tend to mask detailed water usage activities; information which is critical to inform the end‐user of their consumption habits (Cardell‐Oliver, ) and to inform bottom‐up end‐use models (Creaco et al, ). For example, knowing that a bill equates to approximately 100 m 3 per month is interesting, but it does not inform the consumer if they are being efficient or wasteful, nor if this consumption is sustainable.…”

“…Smart meters permit end‐users to monitor usage characteristics at all times on a digital platform allowing greater control through water usage transparency. In particular, a significant benefit of end‐user access to smart meter data is the ability to intuitively recognize and self‐report normal usage characteristics (baseline flows) and flow patterns which reflect specific usage activities in a process called flow‐signature analysis (Cardell‐Oliver, ; DeOreo et al, ).…”

“…In the residential context, water consumption can account for up to 66% of the total water supplied by the utility and therefore has significant potential for conservation (Cahill & Lund, ; Willis et al, ). Academic discussions on residential water conservation methods have been ongoing since as early as 1910 (Cahill & Lund, ; Van Hise, ) but following the growth of smart metering technologies, recent studies have utilized various meter types, number of, resolution of acquisition (temporal aggregation), data transfer and activity recognition methods (Beal & Stewart, ; Cahill & Lund, ; Cardell‐Oliver, ; Giurco et al, ; Hamilton, ; Larson et al, ; Willis et al, ).…”

“…Britton et al () later used hourly meter data to identify domestic leaks. Cardell‐Oliver () again analyzed household consumption using hourly data obtained from 187 houses from the real‐world smart metering trial run by Western Australian Water Corporation (Cardell‐Oliver & Peach, ). Beal and Stewart () also adopted hourly data (aggregated from 5 s data) to disaggregate diurnal flow‐signature patterns in the household.…”

Gamified Approaches for Water Management Systems: An Overview

“…Within the broad portfolio of demand-side management interventions that can be implemented (i.e., technological, financial, legislative, maintenance, and educational), many recent works (e.g., [8] [9] [10] [11]) devoted attention to exploring the effect of behavioural change programs aiming at promoting water saving practices and increasing water awareness among users. Detailed investigation regarding behavioural interventions has been possible especially since the late 1990s, with the development of smart water meters [12], which enabled to monitor water consumption data with very high spatial (household) and temporal (from several minutes up to few seconds) sampling resolution [13]. Smart meters provide essential data to characterize the behavior of individual users and support the development of customized demand-side management interventions targeted on specific groups of users, and tailored to act on the drivers of their water consumption behavior.…”