A Low-Cost, Open Source Monitoring System for Collecting High Temporal Resolution Water Use Data on Magnetically Driven Residential Water Meters

Abstract

<jats:p>We present a low-cost (≈$150) monitoring system for collecting high temporal resolution residential water use data without disrupting the operation of commonly available water meters. This system was designed for installation on top of analog, magnetically driven, positive displacement, residential water meters and can collect data at a variable time resolution interval. The system couples an Arduino Pro microcontroller board, a datalogging shield customized for this specific application, and a magnetometer sensor. The system was developed and calibrated at the Utah Water Research Laboratory and was deployed for testing on five single family residences in Logan and Providence, Utah, for a period of over 1 month. Battery life for the device was estimated to be over 5 weeks with continuous data collection at a 4 s time interval. Data collected using this system, under ideal installation conditions, was within 2% of the volume recorded by the register of the meter on which they were installed. Results from field deployments are presented to demonstrate the accuracy, functionality, and applicability of the system. Results indicate that the device is capable of collecting data at a temporal resolution sufficient for identifying individual water use events and analyzing water use at coarser temporal resolutions. This system is of special interest for water end use studies, future projections of residential water use, water infrastructure design, and for advancing our understanding of water use timing and behavior. The system’s hardware design and software are open source, are available for potential reuse, and can be customized for specific research needs.</jats:p>

Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the Edge

“…The CIWS computational node uses the same methods for measuring flow through magnetically-driven water meters as the CIWS datalogger. Here, we provide a brief description for completeness, but more specific details are provided by [ 7 ]. Both the CIWS datalogger and CIWS computational node were designed to measure and record water flow through magnetically-driven, residential water meters.…”

“…Previous applications of smart metering data and associated research studies have necessarily focused on the small number of cases where cities have upgraded to newer electronic meters or where individual dataloggers can be deployed to existing meters to collect high temporal resolution data. Most of these have been conducted in Australia [ 2 , 3 , 4 , 5 , 6 ] and the United States [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. In these studies, high-resolution water use data were collected in the field and then transferred to a centralized location for post-processing to examine residential water use behavior.…”

“…In the aforementioned non-intrusive smart metering studies [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ], several water end use disaggregation and classification algorithms were adopted to break down the total water use registered on the household’s main meter into different water end use categories. Regardless of the algorithm adopted, the primary focus of all of these studies was to better understand the water use behavior of residential users.…”

“…Regardless of the algorithm adopted, the primary focus of all of these studies was to better understand the water use behavior of residential users. For example, using disaggregated and classified water end use events, references [ 2 , 6 ] developed a theoretical integrated water use model for understanding household water consumption, references [ 3 , 5 , 12 , 13 , 14 ] evaluated the effectiveness of implemented water demand management and rebate programs, reference [ 4 ] reconciled differences between perceived and actual residential end use water consumption, reference [ 7 ] presented an open source, low cost monitoring system for the collection of high-resolution water data on residential water meters, reference [ 8 ] estimated the probability of water fixtures being used during peak hours, references [ 9 , 15 ] provided a detailed understanding of how water is being used inside residential settings, and reference [ 10 ] estimated the price elasticity of water demand with water end use data.…”

“…We developed the computational node as part of a larger effort aimed at developing Cyberinfrastructure for Intelligent Water Supply (CIWS). The design of the computational node builds upon our earlier work in building a simpler datalogger capable of recording data at a user-configurable temporal resolution as high as every 1 s (CIWS datalogger, [ 7 ]). Using the CIWS datalogger, data must be manually downloaded in the field and then post processed to generate useful information.…”