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>

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.

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 Impact of COVID-19 emergency on residential water end-use consumption measured with a high-resolution IoT system

2021 ◽  
Author(s):  
A. Di Mauro ◽  
G. F. Santonastaso ◽  
S. Venticinque ◽  
A. Di Nardo
Keyword(s):  
High Resolution ◽  
Smart Cities ◽  
Temporal Frequency ◽  
Sustainable Use ◽  
Smart Water ◽  
Consumption Data ◽  
Before And After ◽  
End Use ◽  
Residential Water

Abstract In the era of Smart Cities, in which the paradigms of smart water and smart grid are keywords of technological progress, advancements in metering systems allow for water consumption data collection at the end-use level, which is necessary to profile users' behaviors and to promote sustainable use of water resources. In this paper, a real case study of residential water end-use consumption monitoring shows how data collected at a high-resolution rate allow for the evaluation of consumption profiles. The analysis was carried out by calculating consumption statistics, hourly consumption patterns, daily use frequency, and weekly use frequency. Then, the comparison of two consumption profiles, computed before and after the COVID-19 lockdown, allows us to understand how a change in social and economic factors can affect users' behavior. Finally, new perspectives for water demand modeling and management, based on data with high temporal frequency, are presented.

scholarly journals Extracting household water use event characteristics from rudimentary data

2020 ◽  
Vol 69 (4) ◽  
pp. 387-397 ◽  
Author(s):  
Bettina Elizabeth Meyer ◽  
Heinz Erasmus Jacobs ◽  
Adeshola Ilemobade
Keyword(s):  
High Resolution ◽  
Water Use ◽  
Service Providers ◽  
Flow Measurements ◽  
High Resolution Data ◽  
High Resource ◽  
Smart Water ◽  
Household Water ◽  
End Use ◽  
End Uses

Abstract Household water end-uses have been extracted from high-resolution smart water meter data in various earlier studies. However, research on end-use disaggregation from rudimentary data is limited. Rudimentary data is defined as data recorded in intervals longer than 1 min, or data recorded with resolutions larger than 0.1 L/pulse. Developing countries typically deal with rudimentary data, due to the high cost and high resource investment associated with high-resolution data. The aim of this study was to extract useful event characteristics from rudimentary data, without identifying the actual end-uses per se. A case study was conducted in the City of Johannesburg, South Africa, where 63 homes were equipped with iPERL smart water meters. The meters recorded flow measurements every 15 s at a 1 L/pulse resolution, rendering the recorded data rudimentary. A total of 1,107,547 event pulses were extracted over the 217-day study period. Although the method presented is limited in the sense that water use events cannot be identified, the method allows for disaggregation of event pulses in the presence of rudimentary data. Using this tool, it is possible to lift valuable information from rudimentary data that would subsequently benefit service providers in setting water demand strategies.

scholarly journals Smart water meter for automatic meter reading

2022 ◽  
Vol 1212 (1) ◽  
pp. 012042
Author(s):  
A Amir ◽  
R Fauzi ◽  
Y Arifin
Keyword(s):  
Water Consumption ◽  
Smart City ◽  
Clean Water ◽  
Smart Meter ◽  
Maximum Difference ◽  
Smart Water ◽  
Consumption Data ◽  
Water Meters ◽  
Water Metering ◽  
Water Meter

Abstract Clean water is one of the main sectors in smart city that need well management. One of the clean water management is utilization of water meters. The smart meter is more suitable applied for smart city. Recent Smart Water Meter allows water authorities to obtain water consumption data remotely. It also provides ability to collect and record the data in real time that can be utilised for multipurpose. However, in Indonesia, the water meters are used only to measure the total volume of clean water consumption for billing purpose only using mechanical water meter and requires labour intensive manual. Currently, many researches on smart meter design have been developed. However, the smart meter only measure and record the water consumption, without ability in which customer can determine the amount of water as needed. This paper describes design and development of smart water metering with Internet of Things. Flow meter is used as a sensor of water flowing through the pipe. The ability of the proposed smart meter is not only to measure and to record the volume water consumed, but also the customer can determine the water desired and required. The volume of water measured by the smart meter is compared with the manual measurement. The result shows that the water measured manually differs slightly from smart meter measurement using water flow sensor. The maximum difference, error, is 0.03 litres. The proposed smart meter has ability to close the main valve once the determined amount of water is reached.

scholarly journals Advancing household water-use feedback to inform customer behaviour for sustainable urban water

2016 ◽  
Vol 17 (1) ◽  
pp. 198-205 ◽  
Author(s):  
Ariane Liu ◽  
Damien Giurco ◽  
Pierre Mukheibir
Keyword(s):  
Water Management ◽  
Water Use ◽  
Industrial Development ◽  
Urban Water ◽  
Urban Water Management ◽  
Positive Effects ◽  
Smart Water ◽  
Sustainable Urban Water Management ◽  
Household Water ◽  
Water Metering

Sustainable water management is increasingly essential in an age characterised by rapid population growth, urban and industrial development and climate change. Opportunities to promote conservation and water-use efficiencies remain attractive in directly reducing water demand. Smart water metering and the provision of detailed water-use feedback to consumers present exciting new opportunities for improved urban water management. This paper explores two smart water metering trials in New South Wales, Australia, which provided household water consumption feedback via (i) paper end-use reports and (ii) an online portal. This combination enabled a deeper exploration of the various impacts of detailed feedback enabled via smart water metering. The positive effects uncovered by the research present an important opportunity for smart water metering feedback to contribute towards more sustainable urban water management. Their summary contributes empirical evidence on the impacts for water utilities considering embarking on the smart water metering journey with their customers. The identification of future research and policy needs sets an agenda for smart water metering to promote a sustainable digital urban water future. Larger-scale trials are now required and utilities should integrate the design and plans for scalable advanced feedback programs at the outset of smart meter implementations.

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