Advancing Data Collection, Management, and Analysis for Quantifying Residential Water Use via Low Cost, Open Source, Smart Metering Infrastructure

Urbanization, climate change, aging infrastructure, and the cost of delivering water to residential customers make it vital that we achieve a higher efficiency in the management of urban water resources. Understanding how water is used at the household level is vital for this objective.Water meters measure water use for billing purposes, commonly at a monthly, or coarser temporal resolutions. This is insufficient to understand where water is used (i.e., the distribution of water use across different fixtures like toilets, showers, outdoor irrigation), when water is used (i.e., identifying peaks of consumption, instantaneous or at hourly, daily, weekly intervals), the efficiency of water using fixtures, or water use behaviors across different households. Most smart meters available today are not capable of collecting data at the temporal resolutions needed to fully characterize residential water use, and managing this data represents a challenge given the rapidly increasing volume of data generated. The research in this dissertation presents low cost, open source cyberinfrastructure (datalogging and data management systems) to collect and manage high temporal resolution, residential water use data. Performance testing of the cyberinfrastructure demonstrated the scalability of the system to multiple hundreds of simultaneous data collection devices. Using this cyberinfrastructure, we conducted a case study application in the cities of Logan and Providence, Utah where we found significant variability in the temporal distribution, timing, and volumes of indoor water use. This variability can impact the design of water conservation programs, estimations and forecast of water demand, and sizing of future water infrastructure. Outdoor water use was the largest component of residential water use, yet homeowners were not significantly overwatering their landscapes. Opportunities to improve the efficiency of water using fixtures and to conserve water by promoting behavior changes exist among participants

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

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

An Open Source Cyberinfrastructure for Collecting, Processing, Storing and Accessing High Temporal Resolution Residential Water Use Data

Collecting and managing high temporal resolution residential water use data is challenging due to cost and technical requirements associated with the volume and velocity of data collected. We developed an open-source, modular, generalized architecture called Cyberinfrastructure for Intelligent Water Supply (CIWS) to automate the process from data collection to analysis and presentation of high temporal residential water use data. A prototype implementation was built using existing open-source technologies, including smart meters, databases, and services. Two case studies were selected to test functionalities of CIWS, including push and pull data models within single family and multi-unit residential contexts, respectively. CIWS was tested for scalability and performance within our design constraints and proved to be effective within both case studies. All CIWS elements and the case study data described are freely available for re-use

Understanding Residential Water Use in Logan and Providence, Utah Using Detailed Water End Use Data

Urbanization, climate change, aging infrastructure, and the cost of delivering water to residential customers make it vital that we achieve a higher efficiency in the management of urban water resources. In the last decade, several detailed water end use monitoring studies have been proposed to advance water demand management and promote conservation. Recently, a team of researchers at USU collected and analyzed a high temporal resolution (4 -second) dataset for 31 single family residential properties in Logan and Providence, Utah. We used water end use events extracted from the high temporal resolution dataset to examine indoor and outdoor residential water use at the household level. We identified and classified end uses of water for each property and analyzed monthly water use records to understand how water use varies for users at different levels of consumption. Our results indicate that indoor water use is influenced more by the frequency of use than by the characteristics of water using fixtures. Additionally, we observed variations in indoor water use volume, timing, and distribution of end uses at properties with longer data collection periods. We illustrate opportunities to conserve water indoors and outdoors through adoption of more efficient fixtures and promoting conservation behaviors

Impact of Data Temporal Resolution on Quantifying Residential End Uses of Water

Residential water end-use events (e.g., showers, toilets, faucets, etc.) can be derived from high temporal resolution (<1 min) water metering data. Past studies have collected data at different temporal resolutions (e.g., 4 s, 5 s, or 10 s) without assessing the impact of the temporal aggregation interval on end-use event features (e.g., volume, flowrate, duration) due to the unavailability of data at a sufficient temporal resolution to enable such analyses. We recorded the time between every magnetic pulse generated by a magnetically driven residential water meter’s measurement element (full pulse resolution) using a new, open-source datalogging device and collected data for two residential homes in Utah, USA. We then examined water use events without temporally aggregating data and compared to the same data aggregated at different time intervals to evaluate how temporal resolution of the data affects our ability to identify end-use events, calculate features of individual events, and classify events by end use. Our results show how collecting full pulse resolution data can provide more accurate estimates of event occurrence, timing, and features along with producing more discriminative event features that can only be estimated from full pulse resolution data to make event classification easier and more accurate

Datapalooza 2022: Data Management 101

Have questions about research data? Want to get more out of the data tools you are using? Ever have a computer fail mid-project? Datapalooza 2022 is for you! Datapalooza will cover the data management basics and features a panel of researchers from across campus. Learn how to put best practices into action, and share in some data horror stories. In-person participants are welcome to bring their own lunch; dessert is provided! Please register for Datapalooza online. Registered participants will receive Zoom access. Join us March 30th from 12:00-1:15 p.m. in LIB 101 or on Zoom! Datapalooza is sponsored by USU Libraries in partnership with the Office of Research and the School of Graduate Studies Graduate Training Series, GrT