Presented @ AGU Fall Meeting 2015 - Awarded as Outstanding Student Presentation in the Hydrology Section

1. Modelling and managing urban water demand
through smart meters: Benefits and challenges
from current research and emerging trends
Andrea Cominola, Matteo Giuliani, Andrea Castelletti,
Dario Piga, Andrea E. Rizzoli

2. US
246.2
Urban population in millions
81%
Urban percentage
Mexico
84.392
77%
Colombia
34.3
73%
Brazil
162.6
85%
Argentina
35.6
90%
Ukraine
30.9
68%
Russia
103.6
73%
China
559.2Urban population in millions
42%Urban percentage
Turkey
51.1
68%
India
329.3
29%
Bangladesh
38.2
26%
Philippines
55.0
64%
Indonesia
114.1
50%
S Korea
39.0
81%
Japan
84.7
66%
Egypt
33.1
43%
S Africa
28.6
60%
Canada
26.3
Venezuela
26.0
Poland
23.9
Thailand
21.5
Australia
18.3
Netherlands
13.3
Peru
21.0
Saudi Arabia
20.9
Iraq
20.3
Vietnam
23.3
DR Congo
20.2
Algeria
22.0Morocco
19.4
Malaysia
18.1
Burma
16.5
Sudan
16.3
Chile
14.6
N Korea
14.1
Ethiopia
13.0
Uzbekistan
10.1
Tanzania
9.9
Romania
11.6
Ghana
11.3
Syria
10.2
Belgium
10.2
80%
94%
62%
33%
89%
81%
73%
81%
67%
27%
33%
65%
60%
69%
32%
43%
88%
62%
16%
37%
25%
54%
49%
51%
97%
Nigeria
68.6
50%
UK
54.0
90%
France
46.9
77%
Spain
33.6
77%
Italy
39.6
68%
Germany
62.0
75%
Iran
48.4
68%
Pakistan
59.3
36%
Cameroon
Angola
Ecuador
Ivory
Coast
Kazakh-
stan
Cuba
Afghan-
istan
Sweden
Kenya
Czech
Republic
9.5
9.3
8.7
8.6
8.6
8.5
7.8
7.6
7.6
7.4
Mozam-
bique
Hong
Kong
Belarus
Tunisia
Hungary
Greece
Israel
Guate-
mala
Portugal
Yemen
Dominican
Republic
Bolivia
Serbia &
Mont
Switzer-
land
Austria
Bulgaria
Mada-
gascar
Libya
Senegal
Jordan
Zimbabwe
Nepal
Denmark
Mali
Azerbaijan
Singapore
El
Salvador
Zambia
Uganda
Puerto
Rico
Paraguay
UAE
Benin
Norway
New
Zealand
Honduras
Haiti
Nicaragua
Guinea
Finland
Uruguay
Lebanon
Somalia
Sri Lanka
Cambodia
Slovakia
Costa Rica
Palestine
Kuwait
Togo
Chad
Burkina
Ireland
Croatia
Congo
Niger
Sierra Leone
Malawi
Panama
Turkmenistan
Georgia
Lithuania
Liberia
Moldova
Rwanda
Kyrgyzstan
Oman
Armenia
Bosnia
Tajikistan
CAR
Melanesia
Latvia
Mongolia
Albania
Jamaica
Macedonia
Mauritania Laos
Gabon
Botswana
Slovenia
Eritrea
Estonia
Gambia
Burundi
Papua New Guinea
Namibia
Mauritius
Guinea-Bissau
Lesotho E Timor
Bhutan
Swaziland
Trinidad & Tobago
The earth reaches a momentous
milestone: by next year, for the first time
in history, more than half its population
will be living in cities. Those 3.3 billion
people are expected to grow to 5 billion
by 2030 — this unique map of the world
shows where those people live now
At the beginning of the 20th
century, the world's urban
population was only 220
million, mainly in the west
By 2030, the towns and
cities of the developing
world will make up 80%
of urban humanity
The new urban world
Urban growth, 2005—2010
Predominantly urban
75% or over
Predominantly urban
50—74%
Predominantly rural
25—49% urban
Predominantly rural
0—24% urban
Cities over 10 million people
(greater urban area)
Key
Tokyo
33.4
Osaka
16.6
Seoul
23.2
Manila
15.4
Jakarta
14.9
Dacca
13.8
Bombay
21.3
Delhi
21.1 Calcutta
15.5
Karachi
14.8
Shanghai
17.3
Canton
14.5
Beijing
12.7
Moscow
13.4
Tehran
12.1
Cairo
15.9
Istanbul
11.7
London
12.0
Lagos
10.0
Mexico
City
22.1
New York
21.8
Sao Paulo
20.4
LA
17.9
Rio de
Janeiro
12.2
Buenos
Aires
13.5
3,307,950,000The world’s urban population — from a total of 6,615.9 million SOURCE: UNFPA GRAPHIC: PAUL SCRUTONAfrica Asia Oceania Europe
0.1%
Eastern Europe
-0.4%
Arab States
Latin America
& Caribbean North America
3.2%
2.4%
1.3%
2.8%
1.7%
1.3%
Urban population is growing
Source: United Nations Population Fund, 2007

3. 2000 2030 2050
+130%
Source: United Nations. Department of Economic and Social Affairs. Population Division, 2010
Leflaive, X., et al. (2012), "Water", in OECD, OECD Environmental Outlook to 2050: The Consequences of Inaction, OECD Publishing, Paris.
Domesticwaterdemand
… and so urban water demand
41 megacities
worldwide

4. city/district scale
TECHNOLOGICAL (e.g., water efficient devices)
FINANCIAL (e.g., water price schemes, incentives)
LEGISLATIVE (e.g., water usage restrictions)
OPERATION & MAINTENANCE (e.g., leak detection)
EDUCATION (e.g., water awareness campaigns, workshops)
Water Demand Management Strategies (WDMS)

5. TECHNOLOGICAL (e.g., water efficient devices)
FINANCIAL (e.g., water price schemes, incentives)
LEGISLATIVE (e.g., water usage restrictions)
OPERATION & MAINTENANCE (e.g., leak detection)
EDUCATION (e.g., water awareness campaigns, workshops)
Water Demand Management Strategies (WDMS)
customized WDMS
?

7. Which stakeholders will benefit from
smart metering deployments?

8. UK_WATER SUPPLY UTILITY
15 million customers
2.6 Gl/day drinking water
3 billion $ revenue (2013-14)
water utilities
Stakeholders involved

9. water utilities economy
Stakeholders involved
2014 global water meter market worth $2.6 billion
2018 increased to just over $3 billion
Smart Water Networks Can Save Utilities Worldwide up to
$12.5 Billion Annually Concludes Global Survey and
White Paper Released by Sensus

10. water utilities economy consumers
Stakeholders involved

11. What is the current state-of-the-art
of urban Water Demand Management?

12. 1990
1994
50
30
10
1995
1999
2000
2004
2005
2009
2010
2015
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in a number of medium
to large cities worldwide to nearly continuously monitor water consumption at the single household
level. The availability of data at such very high spatial and temporal resolution advanced the ability in
characterizing, modeling, and, ultimately, designing user-oriented residential water demand manage-
ment strategies. Research to date has been focusing on one or more of these aspects but with limited
integration between the specialized methodologies developed so far. This manuscript is the first
comprehensive review of the literature in this quickly evolving water research domain. The paper
contributes a general framework for the classification of residential water demand modeling studies,
which allows revising consolidated approaches, describing emerging trends, and identifying potential
future developments. In particular, the future challenges posed by growing population demands, con-
strained sources of water supply and climate change impacts are expected to require more and more
integrated procedures for effectively supporting residential water demand modeling and management in
several countries across the world.
© 2015 Elsevier Ltd. All rights reserved.
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
number of people facing water shortage (McDonald et al., 2011b). In
such context, water supply expansion through the construction of
new infrastructures might be an option to escape water stress in
some situations. Yet, geographical or financial limitations largely
restrict such options in most countries (McDonald et al., 2014).
Here, acting on the water demand management side through the
Contents lists available at ScienceDirect
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft
Environmental Modelling & Software 72 (2015) 198e214
134 studies over the
last 25 years
A quick journey in the literature
Benefits and challenges of using smart meters for advancing
residential water demand modeling and management: A review
A. Cominola a
, M. Giuliani a
, D. Piga b
, A. Castelletti a, c, *
, A.E. Rizzoli d
a
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italy
b
IMT Institute for Advanced Studies Lucca, Lucca, Italy
c
Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerland
d
Istituto Dalle Molle di Studi sull'Intelligenza Artificiale, SUPSI-USI, Lugano, Switzerland
a r t i c l e i n f o
Article history:
Received 2 April 2015
Received in revised form
21 July 2015
Accepted 21 July 2015
Available online xxx
Keywords:
Smart meter
Residential water management
Water demand modeling
Water conservation
a b s t r a c t
Over the last two decades, water smart metering programs have been launched in
to large cities worldwide to nearly continuously monitor water consumption at
level. The availability of data at such very high spatial and temporal resolution a
characterizing, modeling, and, ultimately, designing user-oriented residential w
ment strategies. Research to date has been focusing on one or more of these as
integration between the specialized methodologies developed so far. This m
comprehensive review of the literature in this quickly evolving water researc
contributes a general framework for the classification of residential water dem
which allows revising consolidated approaches, describing emerging trends, and
future developments. In particular, the future challenges posed by growing popu
strained sources of water supply and climate change impacts are expected to re
integrated procedures for effectively supporting residential water demand modelin
several countries across the world.
© 2015 Elsevier L
1. Introduction
World's urban population is expected to raise from current
54%e66% in 2050 and to further increase as a consequence of the
unlikely stabilization of human population by the end of the cen-
tury (Gerland et al., 2014). By 2030 the number of mega-cities,
namely cities with more than 10 million inhabitants, will grow
over 40 (UNDESA, 2010). This will boost residential water demand
(Cosgrove and Cosgrove, 2012), which nowadays covers a large
portion of the public drinking water supply worldwide (e.g.,
60e80% in Europe (Collins et al., 2009), 58% in the United States
(Kenny et al., 2009)).
The concentration of the water demands of thousands or mil-
lions of people into small areas will considerably raise the stress on
finite supplies of available freshwater (McDonald et al., 2011a).
Besides, climate and land use change will further increase the
number of people facing water shortage (McDo
such context, water supply expansion through
new infrastructures might be an option to es
some situations. Yet, geographical or financia
restrict such options in most countries (McD
Here, acting on the water demand manageme
promotion of cost-effective water-saving te
economic policies, appropriate national and lo
education represents an alternative strategy
water supply and reduce water utilities' costs
In recent years, a variety of water demand
tegies (WDMS) has been applied (for a revi
Jeffrey, 2006, and references therein). Howev
of these WDMS is often context-specific and s
our understanding of the drivers inducing pe
save water (Jorgensen et al., 2009). Models
Environmental Modelling & Software
journal homepage: www.elsevier.com/locate/envsoft

13. 36%
43%
13%
6%
<1%
Smart meters deployment sites worldwide

14. quarterly / half yearly basis readings
1 kilolitre (=1m3)
Billed-based approach
Traditional water meters

15. Smart meters
Smart meters resolution: 72 pulses/L
(=72k pulses/m3 )
Data logging resolution: 5-10 s interval
Information on time-of-day for consumption
Smart water meters

16. Traditional vs Smart water meters
Oct Nov Dec
Waterconsumptionm3
?

17. Traditional vs Smart water meters
Oct
Waterconsumptionm3
?
Nov Dec

18. Are smart meters really useful?
DEMAND MANAGEMENT
_ technological
_ financial
_ legislative
_ operation and maintenance
_ education
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING

19. Including user modellingin the loop
CUSTOMIZED DEMAND
MANAGEMENT
_ technological
_ financial
_ legislative
_ operation and maintenance
_ education
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING
USER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS

20. Including user modellingin the loop
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental commitment
Toilet
Shower
Dishwasher
Washing machine
Garden
Swimming pool

21. How do we build this model?
The main challenges

22. Water consumption
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
MAIN CHALLENGES
_ Big data management

23. Water consumption
CUSTOMIZED DEMAND
MANAGEMENT
MAIN CHALLENGES
_ Big data management -> Data resolution
47%
12%
8%
7%
25%
001 minute
55%
1%
7%
9%
28%
005 minutes
37%
16% 7%
10%
30%
015 minutes
28%
26% 10%
9%
27%
030 minutes
31%
21% 11%
9%
27%
060 minutes
lowest
contribution
47%
12%
9%
8%
23%
Groundtruth
HPE
CDE CDE CDE CDE CDE CDE CDE
HPE HPE HPE HPE HPE HPEhighest
contribution
lowest
contribution
CDE (clothes dryer)
FRE (Furnace)
HPE (Heat pump)
FGE (Fridge)
EQE (Security/Network)
HPE
CDE CDE CDE
HPE HPEhighest
contribution

24. End-use water consumption characterization
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Toilet
Shower
Dishwasher
Washing machine
Garden
Swimming pool
MAIN CHALLENGES
_ Big data management -> Data resolution
_ Intrusiveness (single-point VS on-device sensors)

25. End-use water consumption characterization
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Toilet
Shower
Dishwasher
Washing machine
Garden
Swimming pool
MAIN CHALLENGES
_ Big data management -> Data resolution
_ Intrusiveness (single-point VS on-device sensors)
_ Costs
_ Privacy

26. Users’ psychographics
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental commitment

27. Users’ psychographics
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS
Age
Income level
Education level
Household composition
Water devices efficiency
Presence of garden/swimming pool
Environmental commitment
SURVEYS CAMPAIGNS – MAIN CHALLENGES:
_ Intrusiveness
_ Data update in time -> temporary occupancy, devices upgrades, …

28. Users’ response to external stimuli and WDMS
CUSTOMIZED DEMAND
MANAGEMENTUSER MODELLING
_ WATER CONSUMPTION
_ PSYCHOGRAPHIC DATA
_ RESPONSE TO WDMS

29. meter type data resolution
end-use
disaggregation
user profiling
WDMS and program
duration
Users’ response to external stimuli and WDMS

30. meter type
end-use
disaggregationdata resolution
WDMS and program
duration
Users’ response to external stimuli and WDMS
MAIN CHALLENGES
_ Few studies experimenting/testing WDMS

31. meter type
end-use
disaggregationdata resolution
WDMS and program
duration
Users’ response to external stimuli and WDMS
MAIN CHALLENGES
_ Few studies experimenting/testing WDMS
_ Short term experiments -> No rebound effect

32. SINGLE-POINT
Smart meters

33. SINGLE-POINT
Smart meters
Consumption data
REPOSITORY
• Privacy
• Security

34. SmartH2O
SINGLE-POINT
Smart meters
Consumption data
REPOSITORY
• Privacy
• Security
WATER DATA
END-USE ANALYTICS

35. SmartH2O
SINGLE-POINT
Smart meters
Consumption data
REPOSITORY
• Privacy
• Security
WATER DATA
END-USE ANALYTICS
CONSUMERS PORTAL
CUSTOMER SEGMENTATION

36. SmartH2O
SINGLE-POINT
Smart meters
Consumption data
REPOSITORY
• Privacy
• Security
WATER DATA
END-USE ANALYTICS
CONSUMERS PORTAL
ENGAGEMENT AND
BEHAVIOURAL CHANGE
CUSTOMER SEGMENTATION

37. LONDON | UK
Thames Water water supply utility
15 million customers served
3 Million smart meters installed by 2030
LOCARNO | CH
Società Elettrica Sopracenerina
multi-utility smart metering
(water, energy, gas)
400 smart water meters installed
VALENCIA | ES
EMIVASA water supply utility
2 million customers served
490,000 water smart meters currently installed
650,000 water smart meters installed by end 2015
The use cases

38. Discussion
_ To which extent are smart meters useful?
_ Are utilities really interested in saving water?
_ Is social influence relevant for water efficient behaviours?
_ How to monitor WDMS effect on the long term?
_ Water price elasticity?
_ Water and energy nexus?
_ Can gamification support behavioural change?

39. http://www.smarth2o-fp7.eu/
Andrea Cominola
andrea.cominola@polimi.it
Politecnico di Milano
Department of Electronics,
Information and Bioengineering
@smartH2Oproject
#SmartH2O
@NRMPolimi
@AndreaCominola
thank you