Modelling and managing urban water demand through smart meters: Benefits and challenges from current research and emerging trends

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

US246.2

Urban population in millions

81%Urban percentage

Mexico84.392

77%

Colombia34.373%

Brazil162.685%

Argentina35.690%

Ukraine30.968%

Russia103.673%

China559.2

Urban population in millions

42%Urban percentage

Turkey51.168%

India329.329%

Bangladesh38.226%

Philippines55.064%

Indonesia114.150%

S Korea39.081%

Japan84.766%

Egypt33.143%

S Africa28.660%

Canada26.3

Venezuela26.0

Poland23.9

Thailand21.5

Australia18.3

Netherlands13.3

Peru21.0

Saudi Arabia20.9

Iraq20.3 Vietnam

23.3

DR Congo20.2

Algeria22.0Morocco

19.4

Malaysia18.1

Burma16.5

Sudan16.3

Chile14.6

N Korea14.1

Ethiopia13.0

Uzbekistan10.1

Tanzania9.9

Romania11.6

Ghana11.3

Syria10.2

Belgium10.2

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%

Nigeria68.650%

UK54.090%

France46.977%

Spain33.677%

Italy39.668%

Germany62.075%

Iran48.468%

Pakistan59.336%

Cameroon

AngolaEcuador

IvoryCoast

Kazakh-stan

Cuba

Afghan-istan

Sweden

Kenya

CzechRepublic

9.5

9.38.7

8.6

8.6

8.5

7.8

7.6

7.6

7.4

Mozam-bique

HongKong

Belarus

Tunisia

Hungary

Greece

Israel

Guate-mala

Portugal

Yemen

DominicanRepublic

Bolivia

Serbia &Mont

Switzer-land

Austria

Bulgaria

Mada-gascar

Libya

Senegal

Jordan

Zimbabwe

Nepal

Denmark

Mali

Azerbaijan

Singapore

ElSalvador

Zambia

Uganda

PuertoRico

Paraguay

UAE

Benin

Norway

NewZealand

Honduras

Haiti

Nicaragua

Guinea

Finland

Uruguay

Lebanon

Somalia

Sri Lanka

Cambodia

Slovakia

Costa Rica

Palestine

Kuwait

Togo

ChadBurkina

Ireland

Croatia

Congo

Niger

Sierra Leone

Malawi

Panama

Turkmenistan

Georgia

Lithuania

Liberia

Moldova

Rwanda

Kyrgyzstan

Oman

ArmeniaBosnia

Tajikistan

CAR

Melanesia

Latvia

Mongolia

Albania

Jamaica

Macedonia

Mauritania Laos

Gabon

Botswana

Slovenia

Eritrea

Estonia

Gambia

Burundi

Papua New Guinea

NamibiaMauritius

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 urban75% or over

Predominantly urban50—74%

Predominantly rural25—49% urban

Predominantly rural0—24% urban

Cities over 10 million people(greater urban area)

Key

Tokyo33.4

Osaka16.6

Seoul23.2

Manila15.4

Jakarta14.9

Dacca 13.8

Bombay21.3

Delhi21.1 Calcutta

15.5

Karachi14.8

Shanghai17.3

Canton14.5

Beijing12.7

Moscow13.4

Tehran12.1

Cairo15.9

Istanbul11.7

London12.0

Lagos10.0

MexicoCity22.1

New York21.8

Sao Paulo20.4

LA17.9

Rio deJaneiro

12.2

BuenosAires13.5 3,307,950,000

The world’s urban population — from a total of 6,615.9 million SOURCE: UNFPA GRAPHIC: PAUL SCRUTONAfrica Asia Oceania Europe0.1%

Eastern Europe-0.4%

Arab StatesLatin 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

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.

Dom

estic

water

dem

and

… and so urban water demand

41 megacities worldwide

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)

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

?

Which stakeholders will benefit from smart metering deployments?

UK_WATER SUPPLY UTILITY15 million customers2.6 Gl/day drinking water3 billion $ revenue (2013-14)

water utilities

Stakeholders involved

water utilities economy


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

water utilities economy consumers


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

1990 1994

50

30

10

1995 1999

2000 2004

2005 2009

2010 2015

Benefits and challenges of using smart meters for advancingresidential water demand modeling and management: A review

A. Cominola a, M. Giuliani a, D. Piga b, A. Castelletti a, c, *, A.E. Rizzoli da Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italyb IMT Institute for Advanced Studies Lucca, Lucca, Italyc Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerlandd 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 2015Received in revised form21 July 2015Accepted 21 July 2015Available online xxx

Keywords:Smart meterResidential water managementWater demand modelingWater 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 mediumto large cities worldwide to nearly continuously monitor water consumption at the single householdlevel. The availability of data at such very high spatial and temporal resolution advanced the ability incharacterizing, 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 limitedintegration between the specialized methodologies developed so far. This manuscript is the firstcomprehensive review of the literature in this quickly evolving water research domain. The papercontributes a general framework for the classification of residential water demand modeling studies,which allows revising consolidated approaches, describing emerging trends, and identifying potentialfuture 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 moreintegrated procedures for effectively supporting residential water demand modeling and management inseveral countries across the world.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

World's urban population is expected to raise from current54%e66% in 2050 and to further increase as a consequence of theunlikely 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 growover 40 (UNDESA, 2010). This will boost residential water demand(Cosgrove and Cosgrove, 2012), which nowadays covers a largeportion 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 onfinite supplies of available freshwater (McDonald et al., 2011a).Besides, climate and land use change will further increase the

number of people facingwater shortage (McDonald et al., 2011b). Insuch context, water supply expansion through the construction ofnew infrastructures might be an option to escape water stress insome situations. Yet, geographical or financial limitations largelyrestrict such options in most countries (McDonald et al., 2014).Here, acting on the water demand management side through thepromotion of cost-effective water-saving technologies, revisedeconomic policies, appropriate national and local regulations, andeducation represents an alternative strategy for securing reliablewater supply and reduce water utilities' costs (Gleick et al., 2003).

In recent years, a variety of water demand management stra-tegies (WDMS) has been applied (for a review, see Inman andJeffrey, 2006, and references therein). However, the effectivenessof these WDMS is often context-specific and strongly depends onour understanding of the drivers inducing people to consume orsave water (Jorgensen et al., 2009). Models that quantitativelydescribe how water demand is influenced and varies in relation toexogenous uncontrolled drivers (e.g., seasonality, climatic condi-tions) and demand management actions (e.g., water restrictions,pricing schemes, education campaigns) are essential to explorewater users' response to alternative WDMS, ultimately supporting

* Corresponding author. Department of Electronics, Information, and Bioengi-neering, Politecnico di Milano, Milan, Italy.

E-mail address: [email protected] (A. Castelletti).

Contents lists available at ScienceDirect

Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

http://dx.doi.org/10.1016/j.envsoft.2015.07.0121364-8152/© 2015 Elsevier Ltd. All rights reserved.

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 advancingresidential water demand modeling and management: A review

A. Cominola a, M. Giuliani a, D. Piga b, A. Castelletti a, c, *, A.E. Rizzoli da Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Milan, Italyb IMT Institute for Advanced Studies Lucca, Lucca, Italyc Institute of Environmental Engineering, ETH Zurich, Zurich, Switzerlandd 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 2015Received in revised form21 July 2015Accepted 21 July 2015Available online xxx

Keywords:Smart meterResidential water managementWater demand modelingWater 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 mediumto large cities worldwide to nearly continuously monitor water consumption at the single householdlevel. The availability of data at such very high spatial and temporal resolution advanced the ability incharacterizing, 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 limitedintegration between the specialized methodologies developed so far. This manuscript is the firstcomprehensive review of the literature in this quickly evolving water research domain. The papercontributes a general framework for the classification of residential water demand modeling studies,which allows revising consolidated approaches, describing emerging trends, and identifying potentialfuture 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 moreintegrated procedures for effectively supporting residential water demand modeling and management inseveral countries across the world.

© 2015 Elsevier Ltd. All rights reserved.

1. Introduction

World's urban population is expected to raise from current54%e66% in 2050 and to further increase as a consequence of theunlikely 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 growover 40 (UNDESA, 2010). This will boost residential water demand(Cosgrove and Cosgrove, 2012), which nowadays covers a largeportion 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 onfinite supplies of available freshwater (McDonald et al., 2011a).Besides, climate and land use change will further increase the

number of people facingwater shortage (McDonald et al., 2011b). Insuch context, water supply expansion through the construction ofnew infrastructures might be an option to escape water stress insome situations. Yet, geographical or financial limitations largelyrestrict such options in most countries (McDonald et al., 2014).Here, acting on the water demand management side through thepromotion of cost-effective water-saving technologies, revisedeconomic policies, appropriate national and local regulations, andeducation represents an alternative strategy for securing reliablewater supply and reduce water utilities' costs (Gleick et al., 2003).

In recent years, a variety of water demand management stra-tegies (WDMS) has been applied (for a review, see Inman andJeffrey, 2006, and references therein). However, the effectivenessof these WDMS is often context-specific and strongly depends onour understanding of the drivers inducing people to consume orsave water (Jorgensen et al., 2009). Models that quantitativelydescribe how water demand is influenced and varies in relation toexogenous uncontrolled drivers (e.g., seasonality, climatic condi-tions) and demand management actions (e.g., water restrictions,pricing schemes, education campaigns) are essential to explorewater users' response to alternative WDMS, ultimately supporting

* Corresponding author. Department of Electronics, Information, and Bioengi-neering, Politecnico di Milano, Milan, Italy.

E-mail address: [email protected] (A. Castelletti).

Contents lists available at ScienceDirect

Environmental Modelling & Software

journal homepage: www.elsevier .com/locate/envsoft

http://dx.doi.org/10.1016/j.envsoft.2015.07.0121364-8152/© 2015 Elsevier Ltd. All rights reserved.

Environmental Modelling & Software 72 (2015) 198e214

36%

43%

13%6%

<1%

Smart meters deployment sites worldwide

quarterly / half yearly basis readings

1 kilolitre (=1m3)

Billed-based approach

Traditional water meters

Smart meters

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

Smart water meters

Traditional vs Smart water meters

Oct Nov Dec

Wat

er c

onsu

mpt

ion

m3

?

Traditional vs Smart water meters

Oct

Wat

er c

onsu

mpt

ion

m3

?Nov Dec

Are smart meters really useful?

DEMAND MANAGEMENT

_ technological

_ financial_ legislative_ operation and maintenance_ education

CONSUMERS’ COMMUNITYWATER

CONSUMPTION MONITORING

Including user modelling in the loop

CUSTOMIZED DEMAND MANAGEMENT

_ technological

_ financial_ legislative_ operation and maintenance_ education

CONSUMERS’ COMMUNITYWATER

CONSUMPTION MONITORING

USER MODELLING

_ WATER CONSUMPTION_ PSYCHOGRAPHIC DATA_ RESPONSE TO WDMS

Including user modelling in the loop

CUSTOMIZED DEMAND MANAGEMENTUSER MODELLING


AgeIncome levelEducation levelHousehold compositionWater devices efficiencyPresence of garden/swimming poolEnvironmental commitment

ToiletShower

DishwasherWashing machine

GardenSwimming pool

How do we build this model?The main challenges

Water consumption



MAIN CHALLENGES

_ Big data management

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

HPE

CDE CDE CDE CDE CDE CDE CDE

HPE HPE HPE HPE HPE HPEhighest contribution

lowest contribution

47%

12%

9%

8%

23%

Groundtruth

HPE



lowest contribution

CDE (clothes dryer)

FRE (Furnace)HPE (Heat pump)

FGE (Fridge)EQE (Security/Network)

HPE



lowest contribution

End-use water consumption characterization



ToiletShower


GardenSwimming pool

MAIN CHALLENGES

_ Big data management -> Data resolution_ Intrusiveness (single-point VS on-device sensors)

End-use water consumption characterization



ToiletShower


GardenSwimming pool

MAIN CHALLENGES

_ Big data management -> Data resolution_ Intrusiveness (single-point VS on-device sensors)_ Costs_ Privacy

Users’ psychographics




Users’ psychographics




SURVEYS CAMPAIGNS – MAIN CHALLENGES:

_ Intrusiveness_ Data update in time -> temporary occupancy, devices upgrades, …

Users’ response to external stimuli and WDMS



meter type data resolution end-usedisaggregation user profiling WDMS and program

duration


meter type end-usedisaggregationdata resolution

WDMS and program duration


MAIN CHALLENGES

_ Few studies experimenting/testing WDMS

meter type end-usedisaggregationdata resolution

WDMS and program duration


MAIN CHALLENGES

_ Few studies experimenting/testing WDMS

_ Short term experiments -> No rebound effect

SINGLE-POINT Smart meters


Consumption dataREPOSITORY• Privacy• Security

SmartH2O



WATER DATA END-USE ANALYTICS

SmartH2O




CONSUMERS PORTAL

CUSTOMER SEGMENTATION

SmartH2O




CONSUMERS PORTALENGAGEMENT AND BEHAVIOURAL CHANGE

CUSTOMER SEGMENTATION

LONDON | UKThames Water water supply utility

15 million customers served3 Million smart meters installed by 2030

LOCARNO | CHSocietà Elettrica Sopracenerina

multi-utility smart metering (water, energy, gas)

400 smart water meters installed

VALENCIA | ESEMIVASA water supply utility 2 million customers served

490,000 water smart meters currently installed650,000 water smart meters installed by end 2015

The use cases

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?

http://www.smarth2o-fp7.eu/

Andrea [email protected]

Politecnico di MilanoDepartment of Electronics,

Information and Bioengineering

@smartH2Oproject #SmartH2O@NRMPolimi@AndreaCominola

thank you

Technology

Modelling and managing urban water demand through smart meters: Benefits and challenges from current research and emerging trends