Water utilities strive to achieve a sustainable reduction in per capita consumption (PCC) by optimising their peak demand management strategies. Socioeconomic (SE) and socio-demographic (SD) characteristics have been proven to correlate with PCC. However, the full extent to which these characteristics underpin peak demand and PCC is yet to be fully understood. Previous work used medium resolution smart meter data from 10,000 households to discover and characterise temporal consumption patterns that underpin peak demand, identifying four distinct clusters of households, namely “Evening Peak” (EP), Late Morning Peak (LM), Early Morning Peak (EM) and Multiple Peak (MP). Using survey results, "Acorn household classification", household occupancy and UK population and household attribute data, this study attempts to draw a correlation between the four clusters and known variables of the participating households. Results have revealed a strong correlation between many endogenous attributes (particularly housing, occupancy, age, number of children and household income) and households' consumption patterns underpinning peak demand. Some 56% of families in privately rented housing show EP characteristics compared with 22% owner-occupiers and 9% social renters. EP households with teenage boys have 37% higher per household consumption (PHC) than average, while EM families with teenage girls use 47% more water in early morning showers than average.

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 time-consuming 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.

Characterising 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.

The COVID-19 lockdown has instigated significant changes in household behaviours across a variety of categories including water consumption, which in the south and east regions of England is at an all-time high. We analysed water consumption data from 11,528 households over 20 weeks from January 2020, revealing clusters of households with distinctive temporal patterns. We present a data-driven household water consumer segmentation characterising households’ unique consumption patterns and we demonstrate how the understanding of the impact of these patterns of behaviour on network demand during the COVID-19 pandemic lockdown can improve the accuracy of demand forecasting. Our results highlight those groupings with the highest and lowest impact on water demand across the network, revealing a significant quantifiable change in water consumption patterns during the COVID-19 lockdown period. The implications of the study to urban water demand forecasting strategies are discussed, along with proposed future research directions.