Advancing open source cyberinfrastructure for collecting, processing, storing and accessing high temporal resolution residential water use data

2021 ◽  
Author(s):  
Camilo J. Bastidas Pacheco ◽  
Jeffery S. Horsburgh ◽  
Joseph C. Brewer ◽  
Robb J. Tracy ◽  
Juan Caraballo
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Water Use ◽  
Temporal Resolution ◽  
Water Conservation ◽  
High Temporal Resolution ◽  
Base System ◽  
Technical Requirements ◽  
Residential Water Use ◽  
Residential Water

<p>Collecting and managing high temporal resolution (< 1 minute) residential water use data is challenging due to cost and technical requirements associated with the volume and velocity of data collected. It is well known that this type of data has potential to expand our knowledge of residential water use, inform future water use predictions, and improve water conservation strategies. However, most studies collecting this type of data have been focused on the practical application of the data (e.g., developing and applying end use disaggregation algorithms) with much less focus on how the data were collected, retrieved, quality controlled, and managed to enable data visualization and analysis. We developed an open-source, modular, generalized cyberinfrastructure system to automate the process from data collection to analysis. The system has three main architectural components: first, the sensors and dataloggers for water use monitoring; second, the data communication, parsing and archival tools; and third, the analyses, visualization and presentations of data produced for different audiences. For the first component, we present a low-cost datalogging device, designed for installation on top of existing, analog, magnetically driven, positive displacement, residential water meters that can collect data at a user configurable time resolution interval. The second component consists of a system developed using existing open-source software technologies that manages the data collected, including services and databasing. The final element includes software tools for retrieving the data that can be integrated with advanced data analytics tools. The system was used in a single family residential water use data collection case study to test the scalability and performance of its functionalities within our design constraints. Testing with a base system configuration, our results show that the system requires approximately six minutes to process a single day of data collected at a four second temporal resolution for 500 properties. Thus, the system proved to be effective beyond the typical number of participants observed in similar studies of residential water use and would scale well beyond this even with the modest system resources we used for testing. All elements of the cyberinfrastructure developed are freely available in open source repositories for re-use.</p>

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

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3655 ◽  
Author(s):  
Camilo J. Bastidas Pacheco ◽  
Jeffery S. Horsburgh ◽  
Robb J. Tracy
Keyword(s):  
Open Source ◽  
Monitoring System ◽  
Water Use ◽  
Temporal Resolution ◽  
Low Cost ◽  
High Temporal Resolution ◽  
Time Interval ◽  
Residential Water Use ◽  
Water Meters ◽  
Residential Water

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.

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

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5310
Author(s):  
Nour A. Attallah ◽  
Jeffery S. Horsburgh ◽  
Arle S. Beckwith ◽  
Robb J. Tracy
Keyword(s):  
Data Collection ◽  
Open Source ◽  
Water Use ◽  
Edge Computing ◽  
Raspberry Pi ◽  
Residential Home ◽  
High Temporal Resolution ◽  
End Use ◽  
Water Meters ◽  
Residential Water

We present a new, open source, computationally capable datalogger for collecting and analyzing high temporal resolution residential water use data. Using this device, execution of water end use disaggregation algorithms or other data analytics can be performed directly on existing, analog residential water meters without disrupting their operation, effectively transforming existing water meters into smart, edge computing devices. Computation of water use summaries and classified water end use events directly on the meter minimizes data transmission requirements, reduces requirements for centralized data storage and processing, and reduces latency between data collection and generation of decision-relevant information. The datalogger couples an Arduino microcontroller board for data acquisition with a Raspberry Pi computer that serves as a computational resource. The computational node was developed and calibrated at the Utah Water Research Laboratory (UWRL) and was deployed for testing on the water meter for a single-family residential home in Providence City, UT, USA. Results from field deployments are presented to demonstrate the data collection accuracy, computational functionality, power requirements, communication capabilities, and applicability of the system. The computational node’s hardware design and software are open source, available for potential reuse, and can be adapted to specific research needs.

The implications of climate change on residential water use: a micro-scale analysis of Portland (OR), USA

2012 ◽  
Vol 3 (3) ◽  
pp. 225-238 ◽  
Author(s):  
Vivek Shandas ◽  
Meenakshi Rao ◽  
Moriah McSharry McGrath
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Environmental Sustainability ◽  
Water Conservation ◽  
Urban Areas ◽  
Future Climate ◽  
Metropolitan Region ◽  
Study Region ◽  
Residential Water Use ◽  
Residential Water

Social and behavioral research is crucial for securing environmental sustainability and improving human living environments. Although the majority of people now live in urban areas, we have limited empirical evidence of the anticipated behavioral response to climate change. Using empirical data on daily household residential water use and temperature, our research examines the implications of future climate conditions on water conservation behavior in 501 households within the Portland (OR) metropolitan region. We ask whether and how much change in ambient temperatures impact residential household water use, while controlling for taxlot characteristics. Based on our results, we develop a spatially explicit description about the changes in future water use for the study region using a downscaled future climate scenario. The results suggest that behavioral responses are mediated by an interaction of household structural attributes, and magnitude and temporal variability of weather parameters. These findings have implications for the way natural resource managers and planning bureaus prepare for and adapt to future consequences of climate change.

scholarly journals Estimating the impact of climate change on residential water use using panel data analysis: a case study of Lilongwe, Malawi

2017 ◽  
Vol 8 (2) ◽  
pp. 217-226 ◽  
Author(s):  
Chikondi Makwiza ◽  
Musandji Fuamba ◽  
Fadoua Houssa ◽  
Heinz Erasmus Jacobs
Keyword(s):  
Climate Change ◽  
Water Use ◽  
Water Conservation ◽  
Linear Models ◽  
Panel Data Analysis ◽  
Empirical Relationship ◽  
Residential Water Use ◽  
The Impact ◽  
Residential Water ◽  
Emissions Scenario

Abstract In this study, panel linear models were used to develop an empirical relationship between metered household water use and the independent variables plot size and theoretical irrigation requirement. The estimated statistical model provides a means of estimating the climate-sensitive component of residential water use. Ensemble averages of temperature and rainfall projections were used to quantify potential changes in water use due to climate change by 2050. Annual water use per household was estimated to increase by approximately 1.5% under the low emissions scenario or 2.3% under the high emissions scenario. The model results provide information that can enhance water conservation initiatives relating particularly to outdoor water use. The model approach presented utilizes data that are readily available to water supply utilities and can therefore be easily replicated elsewhere.

scholarly journals Advances in the Economic Analysis of Residential Water Use: An Introduction

Water ◽  
2018 ◽  
Vol 10 (9) ◽  
pp. 1162 ◽  
Author(s):  
Arnaud Reynaud ◽  
Giulia Romano
Keyword(s):  
Water Use ◽  
Water Conservation ◽  
Water Demand ◽  
Water Price ◽  
Special Issue ◽  
Residential Water Demand ◽  
Residential Water Use ◽  
Household Water ◽  
The Impact ◽  
Residential Water

The aim of this Special Issue is to gather evidence on the impact of price policies (PP) and non-price policies (NPP) in shaping residential water use in a context of increased water scarcity. Indeed, a large body of the empirical economic literature on residential water demand has been devoted to measuring the impact of PP (water price increases, use of block rate pricing or peak pricing, etc.). The consensus is that the residential water demand is inelastic with respect to water price, but not perfectly. Given the low water price elasticity, pricing schemes may not always be effective tools for modifying household water behaviors. This is puzzling since increasing the water price is still viewed by public authorities as the most direct economic tool for inducing water conservation behaviors. Additional evidence regarding the use of PP in shaping residential water use is then required. More recently, it has been argued that residential consumers may react to NPP, such as water conservation programs, education campaigns, or smart metering. NPP are based on the idea that residential water users can implement strategies that will result in water savings via changing their individual behaviors. Feedback information based on smart water metering is an example of approach used by some water utilities. There are still large gaps in the knowledge on the residential water demand, and in particular on the impact of PP and NPP on residential water use, household water affordability and water service performance. These topics are addressed in this Special Issue “Advances in the Economic Analysis of Residential Water Use”.

Advancing the cyberinfrastructure for smart water metering: A new open source water end use disaggregation algorithm

2021 ◽  
Author(s):  
Nour Attallah ◽  
Jeffery Horsburgh ◽  
Camilo Bastidas Pacheco
Keyword(s):  
Open Source ◽  
Water Use ◽  
Temporal Resolution ◽  
Data Repository ◽  
High Temporal Resolution ◽  
Time Interval ◽  
Smart Water ◽  
Consumption Data ◽  
End Use ◽  
Water Metering

<p>Water end use disaggregation aims to separate household water consumption data collected from a single residential water meter into appliance/fixture-level consumption data. In recent years, the field has rapidly expanded as the value of disaggregated data has been shown for understanding water use behavior, identifying anomalies, and identifying opportunities for conserving water. Several methods have been developed for disaggregating water end uses from high temporal resolution water use data collected using residential smart water meters. However, most existing methods have been incorporated into proprietary software tools and have been tested using datasets that are inaccessible due to privacy issues, with the result being that neither the code nor the data from these studies are available for verification or potential reuse. We describe and demonstrate a new, open source, and reproducible water end use disaggregation and classification tool that builds upon the results of existing smart water metering and end use disaggregation studies. The tool was designed and developed in Python and can be accessed via any current Python programming environment. It was tested on anonymized, high temporal resolution datasets collected from 31 residential dwellings located in the Cities of Logan and Providence, Utah, USA for a period of one month. Results from different meter types and sizes were tested to demonstrate the accuracy and reproducibility of the tool in disaggregating and classifying high temporal resolution data into individual water end use events. Execution of the tool requires approximately one minute for processing one-day of data collected at a four second time interval for one dwelling. The disaggregation tool is open source and can be adapted to specific research needs. The anonymized dataset we used to develop and test the tool is openly available in the HydroShare data repository.</p>

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