Abstract
Abstract Water demand measurements have historically been conducted manually, from meter readings less than once per month. Leading water service providers have begun to deploy smart meters to collect high-resolution data. A low-cost flush counter was developed and connected to a real-time monitoring platform for 119 ultra-low flush toilets in 7 buildings on a university campus to explore how building users influence water demand. Toilet use followed a typical weekly pattern in which weekday use was 92% ± 4 higher than weekend use. Toilet demand was higher during term time and showed a strong, positive relationship with the number of building occupants. Mixed-use buildings tended to have greater variation in toilet use between term time and holidays than office-use buildings. The findings suggest that the flush sensor methodology is a reliable method for further consideration. Supplementary data from the study's datasets will enable practitioners to use captured data for (i) forecast models to inform water resource plans; (ii) alarm systems to automate maintenance scheduling; (iii) dynamic cleaning schedules; (iv) monitoring of building usage rates; (v) design of smart rainwater harvesting to meet demand from real-time data; and (vi) exploring dynamic water pricing models, to incentivise optimal on-site water storage strategies.
Highlights
Water resources managers might hope to access novel tools based on big data solutions (Chen & Han ) supported by high-performance computing (Morales-Hernández et al ) and receive highly granular, near-real-time forecasts for water demands that could, in turn, help them to optimally manage the operation of water distribution networks upstream of a customer
We investigate data from a highresolution smart water metering platform installed as part of a large-scale water demand management programme at the University of Exeter, UK
The water consumption data gathered at 119 water closets (WC) show that water demand has significant variation in term time versus non-term time periods
Summary
Studies on residential and commercial water use were driven by the need to quantify network demand, improve the design of water distribution systems, develop short-. Water demand at commercial premises such as offices and educational facilities has historically been measured using analogue water meters. The majority of these meters are read monthly or quarterly to coincide with the local water service provider’s billing cycle (Thames Water a). Water resources managers might hope to access novel tools based on big data solutions (Chen & Han ) supported by high-performance computing (Morales-Hernández et al ) and receive highly granular, near-real-time forecasts for water demands that could, in turn, help them to optimally manage the operation of water distribution networks upstream of a customer
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