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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
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