Sensing from the basement

Fogarty, James; Au, Carolyn; Hudson, Scott E.

doi:10.1145/1166253.1166269

Cited by 137 publications

(4 citation statements)

References 14 publications

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“…Fogarty et al [12] presents a low-cost microphone-based sensor system for elder activity sensing. Battery powered sensors are attached to pipes at critical locations in the household's water distribution system.…”

Section: A Contributionmentioning

confidence: 99%

Electric Water Heater Energy Consumption Determination Using Outlet Temperature and Volumetric Estimation

Nel

Booysen

Merwe

2015

2015 IEEE Symposium Series on Computational Intelligence

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This paper presents the use of outlet temperature and water meter data as inputs to a physical model of a domestic electric water heater (EWH) for estimating the energy consumption for various control settings. Four sets of actual household data, consisting of at least 7 consecutive days each, is used to determine the accuracy of the energy consumption estimates in comparison to measured energy consumption. Both the outlet temperature and water meter data inputs used were able to estimate the total energy input with an error of less than 10 percent for 3 of the 4 datasets considered. Additionally, both methods are also implemented as a smartphone application that can be used to obtain input from users, as well as provide instantaneous feedback on the impact of control changes.

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Section: A Contributionmentioning

confidence: 99%

Electric Water Heater Energy Consumption Determination Using Outlet Temperature and Volumetric Estimation

Nel

Booysen

Merwe

2015

2015 IEEE Symposium Series on Computational Intelligence

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“…This proprietary device is installed on positive displacement, magnetically driven meters and can collect high temporal resolution data, but a single unit can cost over $2000 USD. Other authors have developed and tested different sensors and data-recording devices to identify when a fixture is used within the house [9][10][11][12]. With these technologies, disaggregation of end uses requires the existence of a smart meter capable of high temporal resolution data collection.…”

Section: Introductionmentioning

confidence: 99%

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

Pacheco

Horsburgh

Tracy

2020

Sensors

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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.

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“…Prior works on water end-use disaggregation have focused on methods incorporating data for whole-home water flow rate [10][11][12][13][14], water pressure [15][16][17], or from microphones [18]. Other methods have taken a multi-modal sensing approach [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Water Event Categorization Using Sub-Metered Water and Coincident Electricity Data

Vitter

Webber

2018

Water

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This study evaluated the potential for data from dedicated water sub-meters and circuit-level electricity gauges to support accurate water end-use disaggregation tools. A supervised learning algorithm was trained to categorize end-use events from an existing database consisting of features related to whole-home and hot water use. Additional features were defined based on dedicated irrigation metering and circuit-level electricity gauges on major water appliances. Support vector machine classifiers were trained and tested on portions of the database using multiple feature combinations, and then externally validated on water event data collected under dissimilar conditions from a demonstration house in Austin, Texas, USA. On the testing data, a trained classifier achieved true positive rates for occurrences and volume exceeding 95% for most categories and 93% for toilet events. Performance for faucet events was less than 90%. Initial results suggest that dedicated sub-meters and circuit-level electricity gauges can facilitate highly accurate categorization with simple features that do not rely on flow rate gradients.

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Sensing from the basement

Cited by 137 publications

References 14 publications

Electric Water Heater Energy Consumption Determination Using Outlet Temperature and Volumetric Estimation

Electric Water Heater Energy Consumption Determination Using Outlet Temperature and Volumetric Estimation

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

Water Event Categorization Using Sub-Metered Water and Coincident Electricity Data

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