SMART WATER METERING AND END USE

STUDIES – EXPERIENCES FROM AUSTRALIA

Dr Cara Beal1,2

1. Griffith School of Engineering, Griffith University, Gold Coast City, QLD, Australia

2. Project Manager, Smart Water Research Centre, Griffith University, QLD, Australia

13th September, 2013 presentation for

University of Brighton, UK

Overview of presentation

• Project 1: South-east Queensland residential end use study

• Project 2: Water-energy nexus at the end use level

• Project 3: Rain tank pump energy intensity study at an end use level

• Project 4: Smart metering for rapid post-meter leakage detection and

water loss management

• Project 5: Potable savings and economic cost assessment of

contemporary residential supply schemes

• Project 6: Autonomous and intelligent system for residential water end

use classification

• Project 7: Water security through scarcity pricing and reverse osmosis

– an augmented systems dynamic approach

• State of Smart Metering and Intelligent Networking in Australia

Background

• Water end use studies are becoming more commonplace in

Australia and overseas in the quest to better understand urban

water consumption and demand strategies

• Smart metering technology is rapidly developing – used in end

use studies (both energy and water)

3

Why is smart meter data useful?

AAAI Presentation – Griffith University

Smart metering & end-use

study approach

Water end use analysis

Washing Machine

Shower

Toilet

full

flush

Tap

Leak

Toilet

half

flush

!

Sunshine Coast

Brisbane

Ipswich

Gold

Coast

Project 1

SEQREUS

Sample periods and climate conditions

0

10

20

30

40

50

60

70

80

90

100

0

50

100

150

200

250

300

350

400

11

/01

/20

10

-1

7/0

1/2

01

0

15

/02

/20

10

-2

1/0

2/2

01

0

22

/03

/20

10

-2

8/0

3/2

01

0

26

/04

/20

10

-2

/05

/20

10

31

/05

/20

10

-6

/06

/20

10

5/0

7/2

01

0 -

11

/07

/20

10

9/0

8/2

01

0 -

15

/08

/20

10

13

/09

/20

10

-1

9/0

9/2

01

0

18

/10

/20

10

-2

4/1

0/2

01

0

22

/11

/20

10

-2

8/1

1/2

01

0

27

/12

/20

10

-2

/01

/20

11

31

/01

/20

11

-6

/02

/20

11

7/0

3/2

01

1 -

13

/03

/20

11

11

/04

/20

11

-1

7/0

4/2

01

1

16

/05

/20

11

-2

2/0

5/2

01

1

20

/06

/20

11

-2

6/0

6/2

01

1

25

/07

/20

11

-3

1/0

7/2

01

1

29

/08

/20

11

-4

/09

/20

11

3/1

0/2

01

1 -

9/1

0/2

01

1

7/1

1/2

01

1 -

13

/11

/20

11

12

/12

/20

11

-1

8/1

2/2

01

1

16

/01

/20

12

-2

2/0

1/2

01

2

20

/02

/20

12

-2

6/0

2/2

01

2

26

/03

/20

12

-1

/04

/20

12

30

/04

/20

12

-6

/05

/20

12

4/0

6/2

01

2 -

10

/06

/20

12

9/0

7/2

01

2 -

15

/07

/20

12

13

/08

/20

12

-1

9/0

8/2

01

2

17

/09

/20

12

-2

3/0

9/2

01

2

22

/10

/20

12

-2

8/1

0/2

01

2

26

/11

/20

12

-2

/12

/20

12

31

/12

/20

12

-6

/01

/20

13

4/0

2/2

01

3 -

10

/02

/20

13

11

/03

/20

13

-1

7/0

3/2

01

3

15

/04

/20

13

-2

1/0

4/2

01

3

20

/05

/20

13

-2

6/0

5/2

01

3

24

/06

/20

13

-3

0/0

6/2

01

3

29

/07

/20

13

-4

/08

/20

13

Ave

rage

we

ekl

y R

ain

fall

(mm

) an

d a

vera

ge

we

ekl

y m

ax T

em

pe

ratu

re (D

eg

C)

Ave

rage

we

ekl

y w

ate

r co

nsu

mp

tio

n (L

/p/d

)

L/p/d

Av rainfall

Av max temp

Two significant

flood events

“driest” period

for years

8 read periods of two week, continuous

datasets for end-use analysis

Jan 2010 September 2013

June,Winter2010

(n=252)

Dec -Feb,

Summer2010-11(n= 219)

June,Winter2011

(n=110)

Dec,Summer

2011(n=93)

March,Autumn

2012(n=85)

Sept,Spring2012

(n=80)

Dec,Summer

2012(n=80)

May,Autumn

2013(n=69)

Outdoor 7.0 4.8 6.7 17.6 24.0 53.3 51.6 25.4

Bathtub 1.8 1.8 1.9 1.0 1.2 2.8 1.6 2.5

Tap 27.4 27.4 25.1 21.2 18.2 20.6 18.3 18.0

Dish washer 2.5 1.9 2.2 1.9 1.6 2.1 1.8 1.8

Shower 42.7 36.2 49.9 40.8 39.2 48.1 46.7 33.4

Clothes Washer 31.0 26.5 31.8 26.5 29.0 29.0 24.9 26.8

Toilet 23.7 23.0 24.4 28.6 25.2 31.3 28.2 25.3

Leak 9.0 4.0 3.1 1.7 6.0 13.9 8.3 3.5

0

50

100

150

200

Ave

rage

wat

er c

on

sum

pti

on

(L/p

/d)

145.3 144.4137.6144.9

125.3

201.0

181.6

136.7

Water End Use Results – SEQ Total

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0

50

100

150

200

250

300

350

400

450

< 30 30 - 59 60 - 89 >90

Ave

rage

pe

op

le p

er

ho

use

ho

ld p

er

inco

me

ca

tego

ry

Ave

rage

to

tal h

ou

seh

old

co

nu

smp

tio

n (

L/h

h/d

)

Household income category ($ '000)

Per capita water use (left y-axis)

Average age per income category (years)

Persons per household (right y-axis)

65504754

Household occupancy, age and income

$$

AAA or 3Stars

AA or 2Stars

A or 1Star

Standard Old

L/hh/d 25.5 40.4 66.4 76.8 102.4

0

20

40

60

80

100

Sho

we

r co

nsu

mp

tio

n (

L/h

h/d

)

A AB C DC

Homes with RWTHomes without

RWT

Mean 123.7 146.2

100

120

140

160

Tota

l ho

use

ho

ld w

ate

r co

nsu

mp

tio

n

(L/p

/d)

A B

<9 L/min

>20 L/min

Low(0 to 2stars)

Medium(3 or 3+stars)

High(>=4 stars)

L/hh/d 82.6 77.5 58.4

0

20

40

60

80

100

Clo

the

s w

ash

er

con

sum

pti

on

(L/h

h/d

)

A AB B

~131 L/wash

~68 L/wash

Inefficient 1 to 2 Stars 3 to 6 Stars

L/hh/d 54.5 40.7 19.0

0

20

40

60

80

100

Tap

co

nsu

mp

tio

n(L

/hh

/d)

A B C

>16L/min

<4.5 - 9L/min

Stock Efficiency and Water Consumption

Socio-demographics and End Use

0 >=1

mean 36.5 46.6

20

30

40

50

60

Ave

rage

sh

ow

er

wat

er

use

(L/

p/d

)

Number of teenagers

A B

(a)

20 - 40 41 - 60 61 - 70 >70

mean 42.5 40.4 32.4 25.8

0

20

40

60

Ave

rage

sh

ow

er

wat

er

use

(L/

p/d

)

Average age of survey respondent (years)

A AB

(b)

AB B

<30 30 - 60 60 - 90 >90

mean 28.2 27.7 18.5 20.8

10

20

30

Ave

rage

to

ilet

wat

er

use

(L/

p/d

)

Household income category ($ ,000)

A B

(a)

BA

20 - 40 41 - 60 61 - 70 >70

mean 18.1 24.2 25.2 26.2

10

20

30

Ave

rage

to

ilet

wat

er

use

(L/

p/d

)

Average age of survey respondent (years)

A B

(b)

ABB

*

Perceptions of water use – reality very different!

• Clothes washing – am peak

• DW & bath events - pm peak

• Showers - both peaks

•Later afternoon peak during summer

• Irrigation occurring during the day especially in winter -non compliance with PWCM

0

2

4

6

8

10

12

14

16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Ave

rage

su

mm

er

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Time (hours)

Irrigation Bathtub Tap Dishwasher

Shower Clotheswasher Toilet Leak

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Ave

rage

win

ter

20

10

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Time (hours)

Irrigation Bathtub TapDishwasher Shower ClotheswasherToilet Leak

Winter 2010

Summer 2010-11

Average Day Diurnal Pattern Analysis

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Ave

rage

win

ter

20

11

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Time (hours)

Irrigation Bathtub Tap

Dishwasher Shower Clotheswasher

Toilet Leak

Winter 2011

0.0

2.5

5.0

7.5

10.0

12.5

15.0

Ave

rage

dai

ly d

iurn

al c

on

sum

pti

on

(L/

p/h

/d)

Time

Less Than 3 Star Efficiency Greater Than 3 Star Efficiency

Stock Efficiency and Peak Flow Reductions

• Water-efficient homes were found to have a reduced average peak hourly consumption of between 2.5 L/p/h/d (18.%) and 3.5 L/p/h/d (19.3%)

• Implications for water distribution infrastructure: - reduce costs / deferral - network modelling based on peaking factors

100

200

300

400

500

600

700

800

Ave

rage

dai

ly h

ou

seh

old

wat

er c

on

sum

pti

on

(L

/hh

/d)

Time (months)

Average daily total consumption in SEQ

Average consumption across the measured period

ASCE Water Resources

Planning & Management

Journal Paper - 2013 • Calculate peak day (PD) to average day (AD) ratios

• Estimate peaking factors – used in planning and design of water distribution

infrastructure e.g. pipe diameter sizing

Average and Peak Demand Analysis

• Internal end uses - CW, shower, drive “small” peaks (peaking factors <1.5)

• External end uses – irrigation, drive large peaks (factors > 1.5)

• Lower peaking factors and less occurrence compared with historical values

– infrastructure optimisation

Average and Peak Demand Analysis

100

200

300

400

500

600

700

800

Ave

rage

dai

ly h

ou

seh

old

wat

er

con

sum

pti

on

(L

/hh

/d)

Time (months)

Average daily total consumption in SEQ

Average consumption across the measured period

PD/AD 1.5PD/AD

1.2

PD/AD2.05

(a) 30/12/10 10/04/11 02/07/11(c) (d)(i)

(ii)

(i)

(ii)

(i)

(ii)

PD/AD1.3

07/01/11(b)

(i)

(ii)

(e)(i)

(ii)

14 – 28 June 2010

BaselineData

TOIL 34

CW42

SHOW58

TAP 27

EX13

0

4

8

12

16

1 3 5 7 9 11 13 15 17 19 21 23

Ave

rage

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Hour of day

EX

BATH

TAP

SHOW

CW

TOIL

0

5

10

15

20

25

1 3 5 7 9 11 13 15 17 19 21 23

Ave

rage

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Hour of day

EX

BATH

TAP

SHOW

CW

TOIL

0

4

8

12

16

20

1 3 5 7 9 11 13 15 17 19 21 23

Ave

rage

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Hour of day

EXTAPSHOWCWTOILLEAK

TOIL 34

CW57

SHOW41

TAP 23

EX12

TOIL 24

CW31SHOW

43

TAP 28

0

2

4

6

8

10

12

14

1 3 5 7 9 11 13 15 17 19 21 23

Ave

rage

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Hour of day

EX

BATH

TAP

DW

SHOW

CW

TOIL

LEAK

TOIL37

CW56

SHOW61

TAP24

0

10

20

30

40

50

60

1 3 5 7 9 11 13 15 17 19 21 23

Ave

rage

dai

ly d

iurn

al

con

sum

pti

on

(L/

p/h

/d)

Hour of day

EX

TAP

SHOW

CW

TOIL

TOIL31

CW76

SHOW53

TAP26

EX255

Evaluating post-drought bounce-back of water

use – where is it occurring in the home?

0

10

20

30

40

50

60

70

80

90

100

0

50

100

150

200

250

300

350

400

11

/01

/20

10

- 17

/01

/20

10

15

/02

/20

10

- 21

/02

/20

10

22

/03

/20

10

- 28

/03

/20

10

26

/04

/20

10

- 2/0

5/2

01

0

31

/05

/20

10

- 6/0

6/2

01

0

5/0

7/2

01

0 - 1

1/0

7/2

01

0

9/0

8/2

01

0 - 1

5/0

8/2

01

0

13

/09

/20

10

- 19

/09

/20

10

18

/10

/20

10

- 24

/10

/20

10

22

/11

/20

10

- 28

/11

/20

10

27

/12

/20

10

- 2/0

1/2

01

1

31

/01

/20

11

- 6/0

2/2

01

1

7/0

3/2

01

1 - 1

3/0

3/2

01

1

11

/04

/20

11

- 17

/04

/20

11

16

/05

/20

11

- 22

/05

/20

11

20

/06

/20

11

- 26

/06

/20

11

25

/07

/20

11

- 31

/07

/20

11

29

/08

/20

11

- 4/0

9/2

01

1

3/1

0/2

01

1 - 9

/10

/20

11

7/1

1/2

01

1 - 1

3/1

1/2

01

1

12

/12

/20

11

- 18

/12

/20

11

16

/01

/20

12

- 22

/01

/20

12

20

/02

/20

12

- 26

/02

/20

12

26

/03

/20

12

- 1/0

4/2

01

2

30

/04

/20

12

- 6/0

5/2

01

2

4/0

6/2

01

2 - 1

0/0

6/2

01

2

9/0

7/2

01

2 - 1

5/0

7/2

01

2

13

/08

/20

12

- 19

/08

/20

12

17

/09

/20

12

- 23

/09

/20

12

22

/10

/20

12

- 28

/10

/20

12

26

/11

/20

12

- 2/1

2/2

01

2

31

/12

/20

12

- 6/0

1/2

01

3

4/0

2/2

01

3 - 1

0/0

2/2

01

3

11

/03

/20

13

- 17

/03

/20

13

15

/04

/20

13

- 21

/04

/20

13

20

/05

/20

13

- 26

/05

/20

13

24

/06

/20

13

- 30

/06

/20

13

29

/07

/20

13

- 4/0

8/2

01

3

Ave

rage

we

ekl

y R

ain

fall

(mm

) an

d a

vera

ge w

ee

kly

max

Te

mp

era

ture

(D

eg

C)

Ave

rage

we

ekl

y w

ate

r co

nsu

mp

tio

n (

L/p

/d)

L/p/d

Av rainfall

Av maxtemp

Indoor138

L/p/d(95%)

Outdoor7 L/p/d

(5%)

Indoor121

L/p/d(96%)

Outdoor5 L/p/d

(4%)

Indoor138

L/p/d(95%)

Outdoor7 L/p/d

(5%)

Indoor120

L/p/d(87%)

Outdoor18 L/p/d

(13%)

Indoor120

L/p/d(83%)

Outdoor24 L/p/d

(17%) Indoor148

L/p/d(73%)

Outdoor53 L/p/d

(27%)

Indoor130

L/p/d(71%)

Outdoor52 L/p/d

(29%)

Indoor112

L/p/d(82%)

Outdoor25 L/p/d

(18%)

0

10

20

30

40

50

60

450

550

650

750

850

950

1050

1150

Ob

serv

ed

Max T

em

p (

°C)

Da

ily

Pro

du

cti

on

(M

L/d

ay)

l

NO RESTRICTIONS

LOW

LEV

EL 1

LOW

LEV

EL 2

MED

IUM

LEV

EL 3

MED

IUM

LEV

EL 4

HIG

H L

EVEL

5

EXTR

EME

LEVE

L 6

HIG

H L

EVEL

MED

IUM

LEV

EL

PERMANENT WATER CONSERVATION

MEASURES (PWCM)

NO

RES

TRIC

TIO

NS

0.00

0.20

0.40

0.60

Corr

elat

ion

co-

effic

ient

Water-energy nexus assessments

Basecase

SolarHWS

Solar +water

efficientCW

Solar + shower ↓ 37°C

Solar +lowflow

showerhead

Solar +tap

aerators

Solar +energy

efficientDW

CW 251.7 211.0 27.7 27.7 27.7 27.7 27.7

DW 82.0 82.0 82.0 82.0 82.0 82.0 59.4

Taps 464.0 244.0 244.0 244.0 244.0 151.3 151.3

Shower 821.0 345.0 345.0 303.0 112.4 112.4 112.4

Total 1618.7 882.0 698.7 656.7 466.1 373.5 350.9

1

10

100

1000

10000

An

nu

al a

vera

ge e

ne

rgy

con

sum

pti

on

-SE

B

HW

S (k

Wh

/p/y

)

Cumulative reduction as

each scenario applied

46% 57% 60% 72% 77% 79% % total

reduction

Scenario Water

reduction

(%)

Energy

reduction

(%)

Solar HWS (EB) - 46

Water-efficient

shower head

37 63

Water-efficient

clothes washer

27 87

Tap aerators 27 38

Shower temp

reduced to 37C

- 13

Energy-efficient

dish washer

- 28

% individual savings (person/year)

This type of data can underpin sustainable development policy / building codes

Project 2

Project 3

Rain tank pump energy intensity at an end use

level

Modified

Actaris CT-5

water meters

Aegis wireless

DataCell-R loggers

EDMI Mk7c (0.1Wh/p)

electricity meters

Mains meter & logger

Rainwater tank pump

& switch system

Individual end

use event

Event

volume

(L)

Event energy

(Wh)

Event energy

intensity

(Wh/L)

Event GHG

intensity

(kg CO2-e/L)*

Long irrigation 450.30 467.20 1.037 0.00108

Short irrigation 13.13 13.60 1.040 0.00109

Clothes washer

(cold water wash) 118.16 128.80 1.090 0.00114

Full flush toilet 7.50 11.40 1.520 0.00159

Half flush toilet 4.30 7.20 1.670 0.00175

e.g. Half flush toilet event

water-energy mapping

Smart metering for rapid post-meter

leakage detection and management

With smart meters we can significantly reduce post-meter

leakage and better account for water loss across the network

Project 4

Using 20,000 meters in Town of

Hervey Bay (1 hr pulse, 5L resolution)

Designing an autonomous and intelligent system for residential

water end-use classification, customer feedback and enhanced

urban water management

?

Autonomous and intelligent system for

residential water end use classification

Project 5

0 10 20 30 40 50 600

2

4

6

8

10

12

14

16

18

20

Time (s)

Flo

w r

ate

(L

/min

)

1

3

4

5

5

2

1: Shower event 2: tap event 3: full toilet flush event4: tap event5: half toilet flush event

Expert system must be able to handle single events, multiple combined

events, new technologies introduced, different behaviours and be able

to self learn to adapt to new regions.

This is a complex pattern recognition

problem but can be done?

Project 5 cont.

Water security through scarcity

pricing and reverse osmosis – an

augmented systems dynamic

approach

Population

PopulationGrowth Rate

+

WaterDemand

+

WaterShortage

+

WaterSupply

-

+

DecreasedRainfall

IncreasedTemperature

+

TraditionalWater

Resources

DesalinationPlants+

-

+

-

-

+

+

+

Pricing

-

+

-

+

System dynamics modelling Desalination expanded supply and

Temporary drought pricing

Project 6

Assessment of contemporary

residential supply schemes

Base case unit cost ($/kL) results

Total resource cost perspective

1.59

4.06

10.38

7.22

2.13

3.71

7.45

6.06

3.73 3.55

6.33

5.20

2.823.55 3.40

0.00

2.00

4.00

6.00

8.00

10.00

12.00

Scheme type

Un

it co

st (

$/k

L)

S1 1.59 4.06 10.38 7.22

S2 2.13 3.71 7.45 6.06

S3 3.73 3.55 6.33 5.20

S4 2.82 3.55 3.40

Scheme 1 Scheme 2 Scheme 3 Scheme 4

S1: adjusted potable savings; S2: unadjusted potable savings; S3: current alternative source demand; S4: historical demand

IPRWT

Dual

supply Hybrid

Desalination

Project 7

State of Smart Metering and Intelligent Water

Networks in Australia

•On-line survey

•In-depth interviews

•Building case studies for implementing smart metering projects

– small pilots whole-of-community roll-outs

COST SAVINGS

Reduced OPEXReduced manual

meter readsReduce customer

complaint handlingCAPEX deferral

“Water supplied to town

had reduced by almost

834 ML (2010-2012),

resulting in a $3M savings

for water supplied”

“By reducing monthly peak

demand by 10%, can defer

$100M infrastructure for 4 years,

representing savings of $20M

NPV”

“Bulk water reduced

by 3,800 ML”

“Deferring $20M WTP upgrade for 7 years,

representing capital efficiency savings of $7.9M.

Deferring $5M pipeline upgrade for 5 years,

representing capital efficiency savings of $1.6M.”

“approx. 270 queries/yr due

to inaccurate billing, down

to almost none”

“Residential water use

reduced by 11% to 310 kL /

year in 2011-12”

Cost savings / increased revenue

CUSTOMER SATISFACTION

Reduced water bill due to leak alerts

Informative and personalised billing

Instant verification of water bill queries

Eliminate need to access property

“Customer billing now includes trending

data and comparative benchmarks for

water usage for average households”

The customer benefits...

TECHNICAL

Technology became out-dated and easily

damaged

Compatibility of meter –communication systems

Difficulties with customer portal –privacy concerns

Variability in walk / drive by signals

LIMITED KNOWLEDGE BASE

Lack of know-how of suitable technologies: “what, where & why”?

Few existing business cases showing

quantifiable outcomes

Limited industry knowledge & experience

in rolling out projects

Challenges and limitations

• Well over 150,000 meters currently installed or planned

• Appears to be a business case for deployment of smart

metering technology

• The value of smart metering and the specific business case

drivers are highly contextual to location.

• There is a limited knowledge of the capabilities of current

and future technology in the smart water metering space

• System only as “smart” or “intelligent” as the know-how of

the user

• A need for an agreed, standardised set of definitions

Key points from survey:

Dr. Cara Beal

c.beal@griffith.edu.au

Ph: 61-7-5552-7822

Acknowledgements Associate Professor Rodney Stewart, Dr Oz Sahin, Dr

Rachelle Willis, Tracy Britton, Reza Talebpour, Anas Makki,

Abel Silva Vieira, Khoi Nguyen, Ray Siems, Ram Gurung

Please email for technical reports/papers/info

Thank you for listening