A review of household water demand management and consumption measurement

被引:72
作者
Abu-Bakar, Halidu [1 ]
Williams, Leon [1 ]
Hallett, Stephen Henry [2 ]
机构
[1] Cranfield Univ, Cranfield Ctr Competit Creat Design, Coll Rd, Cranfield MK43 0AL, Beds, England
[2] Cranfield Univ, Ctr Environm & Agr Informat, Coll Rd, Cranfield MK43 0AL, Beds, England
基金
英国工程与自然科学研究理事会;
关键词
End use; Smart meter; Usage data; Demand-side management; Water consumption; Sensors; URBAN WATER; SMART METERS; CONSERVATION; MODEL; DETERMINANTS; STRATEGIES; EFFICIENCY; ENGLAND; IMPACT; DISAGGREGATION;
D O I
10.1016/j.jclepro.2021.125872
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Rapid population growth and economic prosperity among other factors are exacerbating existing water stress in the east and southeast regions of England, hence, the water sector is increasingly shifting focus from the expansion of water sources and increased abstraction to demand-side management (DSM) strategies aimed at improving household water efficiency and reducing per capita consumption. A crucial component of water DSM strategy is a good understanding of household water use patterns and the myriad factors that influence them. Smart metering, conflated with innovative techniques and groundbreaking ancillaries continue to support DSM strategies by providing quasi-real-time data, offering powerful insights into household water consumption patterns and delivering behaviour-changing feedback to consumers. This paper presents a comprehensive review of the current state of household water consumption and their determinants as reported in the literature. The paper also reviews the methods and techniques for measuring and understanding consumption patterns and discuss prominent DSM instruments utilised in the household water demand sector globally along with their relative impact on per capita consumption (PCC). The review concludes that while disaggregation remains a very effective means of revealing consumption patterns at micro-component levels, the process is timeconsuming and costly, relying on high-resolution data, specific hardware and software combination, making it difficult to incorporate into the utility?s routine DSM framework. A future research is proposed, that may focus on an alternative, scalable consumption pattern recognition approach that can easily be incorporated into the utility?s DSM strategy using medium resolution smart-meter data. ? 2021 Published by Elsevier Ltd.
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页数:19
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