Download - Residential water users' modeling SOTA
Smart sensors and user modeling in residential water demand management
State of the art review
Andrea Cominola, Andrea Castelletti, Matteo Giuliani
19/04/2014_MILANO
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DOMESTIC WATER END USER
User/household attributes
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental committment
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DOMESTIC WATER END USER
User/household attributes
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental committment
External drivers
Climate
Water price
Regulations
Incentives
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DOMESTIC WATER END USER
End uses Toilet
Shower
Dishwasher
Washing machine
Garden
Swimming pool
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USERS’ INTERACTIONS
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WORK PHASES
STATE OF THE ART ASSESSMENT
DATA GATHERING USER PROFILES
MODELING RESPONSE TO WDM
STRATEGIES
MULTI-AGENT MODELS
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DATA GATHERING
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MEASURING WATER USE
quarterly / half yearly basis readings no real-time data conventional water meters resolution: 1 kilolitre (=1m3) no information on time-of-day
and water using device
Ineffective support to
WATER DEMAND
MANAGEMENT STRATEGIES
BILL-BASED APPROACH
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MEASURING WATER USE
BILL-BASED APPROACH
quarterly / half yearly basis readings no real-time data conventional water meters resolution: 1 kilolitre (=1m3) no information on time-of-day
and water using device
SMART METERING
Quasi real-time data Smart meters resolution:
72 pulses/L (=72k pulses/m3 )
Data logging resolution: 5-10 s interval
information on time-of-day
for consumption
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SMART METERS
SMART METERING TECHNOLOGIES
smart meters: one per dwelling (cost=10-100 $/piece)
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SMART METERS
SMART METERING TECHNOLOGIES
smart meters: one per dwelling (cost=10-100 $/piece)
pressure sensors: one per water using device
(cost= 10-50 $/piece)
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SMART METERS
smart meters
pressure sensors
costs - accuracy
easy to install acceptability by users
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SMART METERS
smart meters
pressure sensors
costs - accuracy
easy to install acceptability by users
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SMART METERS
STATE-OF-THE ART CASE STUDIES 2013 Nguyen, K. A., Zhang, H., &
Stewart, R. A. Development Of An Intelligent Model To Categorise Residential Water End Use Events. Journal of Hydro-environment Research.
Journal of Hydro-environment Research.
2012 Fielding, K. S., Spinks, A., Russell, S., McCrea, R., Stewart, R., & Gardner, J.
An experimental test of voluntary strategies to promote urban water demand management.
Journal of environmental management.
2011 Gato-Trinidad, S., Jayasuriya, N., & Roberts, P.
Understanding urban residential end uses of water. Water Science & Technology, 64(1), 36-42.
2011 Willis, R. M., Stewart, R. A., Giurco, D. P., Talebpour, M. R., & Mousavinejad, A.
End use water consumption in households: impact of socio-demographic factors and efficient devices.
Journal of Cleaner Production.
2010 Beal, C.D., Stewart, R.A., Huang, T.
South East Queensland Residential End Use Study: Baseline Results – Winter 2010.
Urban Water Security Research Alliance Technical Report No. 31
2009 Willis, R., Stewart, R.A., Panuwatwanich, K., Capati, B. and Giurco, D.
Gold Coast Domestic Water End Use Study AWA Water, 36(6): 84-90.
2009 Willis, R., Stewart, R.A., Talebpour, M.R., Mousavinejad, A., Jones, S. and Giurco, D.
Revealing the impact of socio-demographic factors and efficient devices on end use water consumption: case of Gold Coast Australia.
Proceedings of the 5th IWA Specialist Conference 'Efficient 2009', eds. International Water Association (IWA) and Australian Water Association, Sydney, Australia.
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SMART METERS
STATE-OF-THE ART CASE STUDIES 2008 Mead, N., & Aravinthan, V. Investigation of household water consumption using
smart metering system. Desalination and Water Treatment,11(1-3), 115-123.
2007 Heinrich, M. Water End Use and Efficiency Project (WEEP) - Final Report.
BRANZ Study Report 159, Branz, Judgeford, New Zealand.
2005 Kowalski, M., Marshallsay, D.,
Using measured micro-component data to model the impact of water conservation strategies on the diurnal consumption profile.
Water Science and Technology: Water Supply 5 (3-4), 145-150.
2005 Roberts, P. Yarra Valley Water 2004 residential end use measurement study.
Final report, June 2004.
2004 Mayer, P. W., DeOreo, W. B., Towler, E., Martien, L., & Lewis, D.
Tampa water department residential water conservation study: the impacts of high efficiency plumbing fixture retrofits in single-family homes.
A Report Prepared for Tampa Water Department and the United States Environmental Protection Agency.
2003 Loh, M. and Coghlan, P. Domestic water use study in Perth, Western Australia 1998 to 2000.
Water Corporation of Western Australia.
1999 Mayer, P.W. and DeOreo, W.B.
Residential End Uses of Water Aquacraft, Inc. Water Engineeringand Management, Boulder, CO.
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SMART METERS
# STATE-OF-THE ART CASE STUDIES_sensors
Sensor resolution (pulses/L)
Logg
er r
eso
luti
on
(s)
34.2 72 *
1
5
10
* = not specified
6
5
1
1
1
1
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SMART METERS
# STATE-OF-THE ART CASE STUDIES_sensors
Sensor resolution (pulses/L)
Logg
er r
eso
luti
on
(s)
34.2 72 *
1
5
10
* = not specified
6
5
1
1
1
1
1pulse every 0.014 L
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SMART METERS
USA-1 UK-1 AUS-11
NZ - 1
# STATE-OF-THE ART CASE STUDIES_location
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SMART METERS
STATE-OF-THE ART CASE STUDIES_time length
Minimum: 4 weeks
Maximum 2 years
* Kowalski and Marshally (2005) is an ongoing project in UK since 2003
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DATA TRANSFER
Manual download (in situ or ex situ) to PC: most used Wireless home internet network 3G mobile network
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SMART METERS IN sH2O
sH2O CASE STUDY_UK
2500 meters since 2011 15 min reading interval 5 districts: 2 in London, 1 in Reading, 1 in Swindon 5000 properties
sH2O CASE STUDY_Swiss they will be installed during the first year of sH2O
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USERS PROFILE MODELING
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END USES DATA
DIRECT MEASUREMENT of flows for end uses DISAGGREGATION ALGORITHMS
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DISAGGREGATION ALGORITHMS
HydroSense
Froehlich et al. , 2009, 2011
_ probabilistic-based classification approach _ matching the “most likely sequence of valve events” _ PRESSURE SENSORS: high number of sensors needed for calibration _ accuracy > 90%
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DISAGGREGATION ALGORITHMS
HydroSense
Froehlich et al. , 2009, 2011
_ probabilistic-based classification approach _ matching the “most likely sequence of valve events” _ PRESSURE SENSORS: high number of sensors needed for calibration _ accuracy > 90%
NOT EASILY FEASIBLE and ACCEPTED by
users
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DISAGGREGATION ALGORITHMS
Trace Wizard
Trace Wizard, 2003. Trace Wizard Water Use Analysis Tool. Users Manual. Aquacaft, Inc.
_user choses wich devices are used in the house _ flow boundaries condition must be inserted (e.g. maximum and minimum flow) _ need for expert analyst for high accuracy _ only two simultaneous events can occur
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DISAGGREGATION ALGORITHMS
Trace Wizard
Trace Wizard, 2003. Trace Wizard Water Use Analysis Tool. Users Manual. Aquacaft, Inc.
_user choses wich devices are used in the house _ flow boundaries condition must be inserted (e.g. maximum and minimum flow) _ need for expert analyst for high accuracy _ only two simultaneous events can occur
TIME AND RESOURCES
INTENSIVE
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DISAGGREGATION ALGORITHMS
Identiflow
_similar to Trace Wizard _ higher accuracy _ it considers many physical features of water using devices (volume, flow rate, duration, etc…)
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DISAGGREGATION ALGORITHMS
Identiflow
_similar to Trace Wizard _ higher accuracy _ it considers many physical features of water using devices (volume, flow rate, duration, etc…)
HIGH DEPENDENCY
ON DEVICES FEATURE
DIFFICULT TO RECOGNISE
NEW DEVICES
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DISAGGREGATION ALGORITHMS
New algorithm proposed in Nguyen, K. A., Zhang, H., & Stewart, R. A., 2013
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DISAGGREGATION ALGORITHMS
New algorithm proposed in Nguyen, K. A., Zhang, H., & Stewart, R. A., 2013
HIDDEN MARKOV MODEL
DYNAMIC TIME WARPING
TIME-OF-DAY PROBABILITY
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DISAGGREGATION ALGORITHMS
New algorithm proposed in Nguyen, K. A., Zhang, H., & Stewart, R. A., 2013
HIGHER ACCURACY if compared to existing tools (Trace Wizard), apart from some uses (irrigation, toilet)
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USER MODELING
“drivers for indoor use include household composition, presence of water saving devices and a range of socio-economic factors”
“The success of household water demand management strategies is dependent on how well we understand how people think about
water and water use»
(Jorgensen et al., 2009)
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USER MODELING
The aim is to “improve the understanding of the end uses of water and to assist where to focus water conservation efforts”
«DESCRIPTIVE STUDIES»
e.g. Gato-Trinidad, 2011 _daily usage is: 66% indoor use, 29% outdoor use, 5% leakage _indoor use: 31% shower, 26% laundry, 19% toilet flushing, 24% others _higher daily water consumption in summertime (also indoor) _50% saving could be possible by using front loaders machines in spite of top loaders
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USER MODELING
The aim is to understand the aim of variables of the same domain on water consumption
«SINGLE VARIABLE DOMAIN STUDIES»
e.g. Fox, 2009 Univariate and multivariate analysis for “Classifying households for water demand forecasting using physical property characteristics” FINDINGS: _ significant difference depending on household size (number of bedrooms), architectural type and garden presence _ not importan difference due to garden aspect or age
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USER MODELING
The aim is to understand the aim of variables of different domains on water consumption
«MULTIPLE VARIABLE DOMAIN STUDIES»
e.g. Willis, 2011 _ explore relationship between stock efficiency and water end use _ assess the influence of socio-demographic factors on water consumption FINDINGS: _ apart from irrigation, the lower socio-economic groups tend to use slightly more water _ general decrease in consumption per capita as family size increases (apart from clothes washer and toilet) _ combined household efficiency savings can be up to 30% _ payback times: 2 years for showerheads, 7 years for washing machines, 21 years for RWT
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USER MODELING
The aim is to forecast residential water demand
«DEMAND FORECASTING MODELS»
e.g. Bennet, 2013 _ ANN are used to model and forecast residential water demand FINDINGS: _ household income, number of adults, number of children, number of teenagers, and appliance stock efficiency regarding toilet, shower and clothes washer end uses were the predominant determinants
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RESPONSE TO WDM STRATEGIES
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WDM STRATEGIES
PRICE CONTROL WATER USE RESTRICTION INCENTIVES for water saving devices INFORMATION CAMPAIGNS
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WDM STRATEGIES
Fielding, 2013
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ROOM FOR IMPROVEMENT
Data transfer faster and more immediate
Data disaggregation algorithm
less human intervention demanding higher accuracy resolution level?
Input selection for users profiling