Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments

Larson, Eric C.; Froehlich, Jon E.; Campbell, Tim; Haggerty, Conor; Atlas, Les; Fogarty, James; Patel, Shwetak

doi:10.1016/j.pmcj.2010.08.008

Cited by 47 publications

(30 citation statements)

References 30 publications

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“…Zeifman and Roth (2011) also recently surveyed this literature; their focus is on comparing algorithmic approaches. Although electricity is the focus of the rest of this paper, the use of disaggregation for other energy-related applications is also promising (i.e., gas, water, and transportation) 22 (Yamagami, Nakamura, Meier, 1996;Cohn et al, 2010;Patel et al, 2007;Larson et al, 2010;Froehlich et al, 2009a,b).…”

Section: Disaggregation Algorithms and Their Requirementsmentioning

confidence: 99%

Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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This paper aims to address two timely energy problems. First, significant low-cost energy reductions can be made in the residential and commercial sectors, but these savings have not been achievable to date. Second, billions of dollars are being spent to install smart meters, yet the energy saving and financial benefits of this infrastructure -without careful consideration of the human element -will not reach its full potential. We believe that we can address these problems by strategically marrying them, using disaggregation. Disaggregation refers to a set of statistical approaches for extracting end-use and/or appliance level data from an aggregate, or wholebuilding, energy signal. In this paper, we explain how appliance level data affords numerous benefits, and why using the algorithms in conjunction with smart meters is the most cost-effective and scalable solution for getting this data. We review disaggregation algorithms and their requirements, and evaluate the extent to which smart meters can meet those requirements. Research, technology, and policy recommendations are also outlined.

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Section: Disaggregation Algorithms and Their Requirementsmentioning

confidence: 99%

Is disaggregation the holy grail of energy efficiency? The case of electricity

Gupta²,

et al. 2013

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“…The pressure wave output, referred to as a particular fixture or appliance's pressure signature, is recorded using a graphical logging tool, and feedback is provided in real-time. In the initial version of the technology, two approaches were used to classify this feedback, a templatebased matching scheme, and a stochastic approach using a Hidden Markov Model (Larson et al 2012). The second version uses a probabilistic approach which is based, in part, on speech recognition algorithms and includes Figure 1.…”

Section: Methodsmentioning

confidence: 99%

“…The second version uses a probabilistic approach which is based, in part, on speech recognition algorithms and includes Figure 1. HydroSense -Examples of pressure transients caused be various water using appliances (Larson et al 2012). Y-axis displays pressure (PSI).…”

Section: Methodsmentioning

confidence: 99%

“…Output was confirmed using systematic testing with known pressure loads (Larson et al 2012). The second version was tested with "real-world" experiments in five residences for a five-week period.…”

Section: Methodsmentioning

confidence: 99%

“…Data is obtained based on a continuous analysis of pressure and pressure changes within a home's plumbing infrastructure (Larson et al, 2012). Two versions of HydroSense have been developed and studied.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A critical review of methods used to obtain flow patterns and volumes of individual domestic water using appliances

Morrison

Friedler

2014

Urban Water Journal

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Determining the daily flow rates and water use patterns of specific household fixtures and appliances is important for water use monitoring, deriving effective strategies for water demand management, designing onsite water reuse systems, and efficient planning and investment in the urban water sector. Currently, there are a limited number of publications which address the need to measure flow patterns and volumes of individual household water-using appliances, or which analyze the alternatives to do so. This paper critically reviews and compares different methods of obtaining the specific flow patterns and volumes of water used by individual household water-using microcomponents (i.e. toilet, bath/shower, washbasin, washing machine, kitchen sink, dishwasher, etc.) in order to make recommendations about the appropriateness of these methodologies for various applications.

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Flow‐Signature Analysis of Water Consumption in Nonresidential Building Water Networks Using High‐Resolution and Medium‐Resolution Smart Meter Data: Two Case Studies

Clifford

Mulligan

Comer

et al. 2018

Water Resources Research

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Real‐time monitoring of water consumption activities can be an effective mechanism to achieve efficient water network management. This approach, largely enabled by the advent of smart metering technologies, is gradually being practiced in domestic and industrial contexts. In particular, identifying water consumption habits from flow‐signatures, i.e., the specific end‐usage patterns, is being investigated as a means for conservation in both the residential and nonresidential context. However, the quality of meter data is bivariate (dependent on number of meters and data temporal resolution) and as a result, planning a smart metering scheme is relatively difficult with no generic design approach available. In this study, a comprehensive medium‐resolution to high‐resolution smart metering program was implemented at two nonresidential trial sites to evaluate the effect of spatial and temporal data aggregation. It was found that medium‐resolution water meter data were capable of exposing regular, continuous, peak use, and diurnal patterns which reflect group wide end‐usage characteristics. The high‐resolution meter data permitted flow‐signature at a personal end‐use level. Through this unique opportunity to observe water usage characteristics via flow‐signature patterns, newly defined hydraulic‐based design coefficients determined from Poisson rectangular pulse were developed to intuitively aid in the process of pattern discovery with implications for automated activity recognition applications. A smart meter classification and siting index was introduced which categorizes meter resolution in terms of their suitable application.

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Disaggregated water sensing from a single, pressure-based sensor: An extended analysis of HydroSense using staged experiments

Cited by 47 publications

References 30 publications

Is disaggregation the holy grail of energy efficiency? The case of electricity

Is disaggregation the holy grail of energy efficiency? The case of electricity

A critical review of methods used to obtain flow patterns and volumes of individual domestic water using appliances

Flow‐Signature Analysis of Water Consumption in Nonresidential Building Water Networks Using High‐Resolution and Medium‐Resolution Smart Meter Data: Two Case Studies

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