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

2

DOMESTIC WATER END USER

User/household attributes

Age

Income level

Education level

Household composition

Water devices efficiency

Presence of garden/swimming pool

Environmental committment

3


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

4


End uses Toilet

Shower

Dishwasher

Washing machine

Garden

Swimming pool

5

USERS’ INTERACTIONS

6

WORK PHASES

STATE OF THE ART ASSESSMENT

DATA GATHERING USER PROFILES

MODELING RESPONSE TO WDM

STRATEGIES

MULTI-AGENT MODELS

7

DATA GATHERING

8

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

9

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

10

SMART METERS

SMART METERING TECHNOLOGIES

smart meters: one per dwelling (cost=10-100 $/piece)

11

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)

12

SMART METERS

smart meters

pressure sensors

costs - accuracy

easy to install acceptability by users

13

SMART METERS

smart meters

pressure sensors

costs - accuracy

easy to install acceptability by users

14

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.

15

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.

16

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

17

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

18

SMART METERS

USA-1 UK-1 AUS-11

NZ - 1

# STATE-OF-THE ART CASE STUDIES_location

19

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

20

DATA TRANSFER

Manual download (in situ or ex situ) to PC: most used Wireless home internet network 3G mobile network

21

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

22

USERS PROFILE MODELING

23

END USES DATA

DIRECT MEASUREMENT of flows for end uses DISAGGREGATION ALGORITHMS

24

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%

25


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

26


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

27


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

28


Identiflow

_similar to Trace Wizard _ higher accuracy _ it considers many physical features of water using devices (volume, flow rate, duration, etc…)

29


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

30


New algorithm proposed in Nguyen, K. A., Zhang, H., & Stewart, R. A., 2013

31



HIDDEN MARKOV MODEL

DYNAMIC TIME WARPING

TIME-OF-DAY PROBABILITY

32



HIGHER ACCURACY if compared to existing tools (Trace Wizard), apart from some uses (irrigation, toilet)

33

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)

34

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

35

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

36

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

37

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

38

RESPONSE TO WDM STRATEGIES

39

WDM STRATEGIES

PRICE CONTROL WATER USE RESTRICTION INCENTIVES for water saving devices INFORMATION CAMPAIGNS

40

WDM STRATEGIES

Fielding, 2013

41

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

Residential water users' modeling SOTA

Technology

Transcript of Residential water users' modeling SOTA