ABSTRACT:

The files provided here are the supporting data and code files for the analyses presented in "A low-cost, open source monitoring system for collecting high-resolution water use data on magnetically-driven residential water meters," an article in Sensors (https://doi.org/10.3390/s20133655). The data included in this resource were collected in laboratory testing and field deployment of the Cyberinfrastructure for Intelligent Water Supply (CIWS) datalogger, an open source, low cost device capable of collecting high temporal resolution data on magnetically driven water meters. The code included allows replication of the analyses presented in the article, and the raw data included allow for extension of the analyses conducted. In the article we present a low-cost (≈ $150) monitoring system for collecting high 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 variable time resolution intervals. 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 second 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 the device is capable of collecting data at a 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.

ABSTRACT:

The files provided here are the supporting data and code files for the analyses presented in "An open source cyberinfrastructure for collecting, processing, storing and accessing high temporal resolution residential water use data," an article in Environmental Modelling and Software (https://doi.org/10.1016/j.envsoft.2021.105137). The data included in this resource were processed using the Cyberinfrastructure for Intelligent Water Supply (CIWS) (https://github.com/UCHIC/CIWS-Server), and collected using the CIWS-Node (https://github.com/UCHIC/CIWS-WM-Node) data logging device. CIWS is an open-source, modular, generalized architecture designed to automate the process from data collection to analysis and presentation of high temporal residential water use data. The CIWS-Node is a low cost device capable of collecting this type of data on magnetically driven water meters. The code included allows replication of the analyses presented in the journal paper, and the raw data included allow for extension of the analyses conducted. The journal paper presents the architecture design and a prototype implementation for CIWS that 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.

ABSTRACT:

This resource contains standardized monthly water use data for single family residences (SFR) in the cities of Logan (11/2014 - 11/2018) and Providence (10/2017 - 05/2020), Utah, USA. Meter readings by Logan and Providence city are conducted on different days of the month, depending on the utility’s working schedule. Thus, the volume of water used within a given month must be estimated from two meter readings. We calculated standardized monthly water use, i.e., from the first to the last day of each month, as follows: Vn = DnMR1 * VMR1 / DMR1 + DnMR2 * V_MR2 / D_MR2 , where, Vn is the volume of water used for a month n. VMR1 is the water volume from the first meter reading (MR1) that contains water use for month n. DMR1 is the number of days covered by MR1 (i.e., the number of days since the previous meter reading), and DnMR1 is the number of days within month n to which MR1 applies. VMR2, DMR2, and DnMR2 have the same information for the second meter reading (MR2) that contains water use for month n.

ABSTRACT:

The files provided here are the supporting data and code files for the analyses presented in "Variability in Consumption and End Uses of Water for Residential Users in Logan and Providence, Utah, USA", an article published in JWRPM (https://ascelibrary.org/journal/jwrmd5). The journal paper assessed how differences water consumption are reflected in terms of timing and distribution of end uses across residential properties. The article provides insights into the variability of indoor and outdoor residential water use at the household level from the analysis of four to 23 weeks of 4-second resolution water use data at 31 single family residential properties. The data were collected in the cities of Logan and Providence, Utah, USA between 2019 and 2021. The 4-second resolution data is publicly available on: http://www.hydroshare.org/resource/0b72cddfc51c45b188e0e6cd8927227e. Standardized monthly values for single family residents in both cities were used in the article and are publicly available on: http://www.hydroshare.org/resource/16c2d60eb6c34d6b95e5d4dbbb4653ef. The code and data included in this resource allows replication of the analyses presented in the journal paper, and the raw data included allow for extension of the analyses conducted.

ABSTRACT:

The files provided here are the supporting data and code files for the analyses presented in "Impact of data temporal resolution on quantifying residential end uses of water", an article submitted to the Water journal (https://www.mdpi.com/journal/water). The journal paper assessed how the temporal resolution at which water use data are collected impacts our ability to identify water end use events, calculate features of individual events, and classify events by end use. Additionally, we also explored implications for data management associated with collecting this type of data as well as methods and tools for analyzing and extracting information from it. The data were collected in the cities of Logan and Providence, Utah, USA in 2022 and are included in this resource. The code and data included in this resource allow replication of the analyses presented in the journal paper, and the raw data included allow for extension of the analyses conducted.

ABSTRACT:

This resource contains raw data collected for the project "Increasing the Water Conservation Impact of Utah State University’s (USU) Extension Water Check Program with 5 Second Metering" (https://uwrl.usu.edu/water-check-study). The data is for ~ 78 households in Logan and Hyde Park, Utah collected in Summer and Fall 2022. 5-second water use data was collected over the entire period using a Flume Smart Home Water Monitoring Device. After ~ two weeks, a USU Extension Water Check was conducted during a site visit. There are 6 data sets in this resource. Data are anonymized and can be linked -- joined -- by the SiteID field.

1_Database_CSVFiles/
1) FlumePropertyData.csv => Metadata for the households collected by Flume when a device is installed and the Flume phone App was installed.
2) Sites.csv => Metadata for the households including city, state, and zipcode.
3) WaterCheckData.csv => Parcel, landscape, and irrigation system data collected as part of the USU Extension Water Check during a 1-hour visit to the household. Data also include Water Check recommendations to reduce irrigation water use.
4) RawWaterUseData/SITE_XXX.csv => Raw 5-second water use data collected by Flume Smart Home Water Monitoring Devices (http:/FlumeWater.com). One file for each household/SiteID. XXX is the SiteID.
5) daily_WeatherData_GVFarm.csv => Weather data from the nearest station - Greenville Farm, Cache Valley, Utah.
6) TrainingData.csv => Irrigation events identified by duration (minutes), volume_gal (gallons), average_fr_GPM (gallons per minute), label (type of event). These data are used to train a model that uses the raw 5-second data to classify irrigation events.
The code to classify the raw 5-second water use data is in a separate code repository - https://github.com/cjbas22/HelpUSUExtensionP.

2_AdditionalData => Folder with duplicate copies of the weather station and training data.

3_Database => Empty folder. Code in the repository https://github.com/cjbas22/HelpUSUExtensionP reads the raw csv files and creates a database with tables for each data file.