Data-driven behavioural modelling of residential water consumption to inform water demand management...

Data-driven behavioural modelling of residential water consumption to inform water demand management strategies

M. Giuliani, A. Cominola, A. Alsahaf, A. Castelletti, M. Anda

EGU General Assembly 2016

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%

Dom

estic

water

dem

and

41 megacities worldwide

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

… and so residential water demand

city/district scale

Water demand management strategies

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)

city/district scale

Water demand management strategies

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)

customized WDMS

What is the current state-of-the-art of residential 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

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


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.


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).






134 studies over the last 25 years

A quick journey in the literature

first smart meters deployment

quarterly / half yearly basis readings

1 kilolitre (=1m3)

Traditional water meters

Traditional vs Smart water 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

36%

43%

13%6%

<1%

Smart meters deployment sites worldwide

134 studies over the last 25 yearsCominola et al. (2015), Benefits and challenges of using smart meters for advancing residential water demand

modeling and management: A review, Enviornmental Modelling & Software.

CUSTOMIZED DEMAND MANAGEMENT

CONSUMERS’ COMMUNITYWATER

CONSUMPTION MONITORING

BEHAVIORAL USER MODELLING

_ SMART METERED WATER CONSUMPTION

_ PSYCHOGRAPHIC DATA

_ RESPONSE TO WDMS

_ technological

_ financial

_ legislative_ operation and maintenance_ education

Smart meters potential for WDMS







_ RESPONSE TO WDMS

_ technological

_ financial


3-step behavioral modelling procedure

SMART METERED WATER CONSUMPTION

Users’ consumption class (label)

USERS PROFILING

PREDICTED CONSUMPTION PROFILE

HOUSEHOLD and CONSUMERS’ PSYCHOGRAPHIC DATA Relevant consumption

determinants subset

BEHAVIORAL MODEL

z

FEATURE EXTRACTION

Case Study Application

Source: H2ome smart project (Anda et al., 2013)

Pilbara

Kimberley

3-months resolution water consumption readings (Aug 2010 – Feb 2012)

Approx. 730 households

27 user and household features

Dataset

Case study application

Dataset

Years of occupancy

House responsibility

# occupants

Resident type

Land use

House type

# toilets

Washing machine type

Toilet type

Shower type

Dishwasher presence

Garden area

Watering method

Watering time

Mulch usage

Native plant presence

Average max temperature

Average min temperature

Average daily precipitation

Pool presence

Pool cover usage

Spa presence

Town

Suburb

Metering period start

Metering period end

Metering period length

Users’ and households’ features

USERS PROFILING FEATURE EXTRACTION

Chi-square scoreInformation GainFast Correlation Based FilterCorrelation Feature SelectionBayesian Logistic RegressionSparse Bayesian Multinomial Logistic RegressionIterative Input Variable Selection

Naïve Bayes ClassifierJ48 Decision Tree algorithmExtremely Randomized Trees

BEHAVIORAL MODEL

Cominola et al. (2015), Modelling residential water consumers’ behaviors by feature selection and feature weighting, In Proceedings of the 36th IAHR world congress

K-means clustering (k=4)

Algorithms

USERS PROFILING FEATURE EXTRACTION

Chi-square scoreInformation GainFast Correlation Based FilterCorrelation Feature SelectionBayesian Logistic RegressionSparse Bayesian Multinomial Logistic RegressionIterative Input Variable Selection

Naïve Bayes ClassifierJ48 Decision Tree algorithmExtremely Randomized Trees

BEHAVIORAL MODEL

K-means clustering (k=4)

Algorithms

An evaluation framework for input variable selection algorithms forenvironmental data-driven models

Stefano Galelli a, *, Greer B. Humphrey b, Holger R. Maier b, Andrea Castelletti c,Graeme C. Dandy b, Matthew S. Gibbs b, d

a Pillar of Engineering Systems and Design, Singapore University of Technology and Design, 20 Dover Drive, 138682, Singaporeb School of Civil, Environmental, and Mining Engineering, University of Adelaide, SA, 5005, Australiac Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza L. da Vinci, 32, 20133, Milan, Italyd Department of Environment, Water and Natural Resources, GPO Box 2384, Adelaide, SA, 5001, Australia


Article history:Received 18 May 2014Received in revised form14 August 2014Accepted 14 August 2014Available online

Keywords:Input variable selectionData-driven modellingEvaluation frameworkLarge environmental datasetsArtificial neural networks

a b s t r a c t

Input Variable Selection (IVS) is an essential step in the development of data-driven models and isparticularly relevant in environmental modelling. While new methods for identifying important modelinputs continue to emerge, each has its own advantages and limitations and no single method is bestsuited to all datasets and modelling purposes. Rigorous evaluation of new and existing input variableselection methods would allow the effectiveness of these algorithms to be properly identified in variouscircumstances. However, such evaluations are largely neglected due to the lack of guidelines or precedentto facilitate consistent and standardised assessment. In this paper, a new framework is proposed for theevaluation and inter-comparison of IVS methods which takes into account: (1) a wide range of datasetproperties that are relevant to real world environmental data, (2) assessment criteria selected to high-light algorithm suitability in different situations of interest, and (3) a website for sharing data, algorithmsand results (http://ivs4em.deib.polimi.it/). The framework is demonstrated on four IVS algorithmscommonly used in environmental modelling studies and twenty-six datasets exhibiting different typicalproperties of environmental data. The main aim at this stage is to demonstrate the application of theproposed evaluation framework, rather than provide a definitive answer as to which of these algorithmshas the best overall performance. Nevertheless, the results indicate interesting differences in the algo-rithms' performance that have not been identified previously.


Software and data availability

SoftwareName of software: PMIS_PCIS, IIS, GA_ANN.Developers (PMIS_PCIS, GA_ANN): Greer B. Humphrey,

Holger R. Maier, GraemeC. Dandy, Matthew S.Gibbs.

Developers (IIS): Stefano Galelli, Andrea Castelletti.Year first available: 2014.Hardware required: PC or MAC.Software required: R (PMIS_PCIS and GA_ANN), MatLab (IIS).Program language: R (PMIS_PCIS and GA_ANN), MatLab (IIS).

Program size: 41 KB (PMIS_PCIS), 135 KB (IIS), 172 KB(GA_ANN).

DataName of dataset: IVS Framework datasets.Developers: Greer B. Humphrey.Form of repository: zipped files.Size of archive: 239.3 MB.Access form: public Dropbox folder.Contact address: Pillar of Engineering Systems and Design,

Singapore University of Technology andDesign, 20 Dover Drive, Singapore 138682.

Telephone: þ 65 6499 4786.E-mail: [email protected]: http://ivs4em.deib.polimi.it.Availability: software and data are available on the IVS

framework website.Cost: free of charge.

* Corresponding author. Tel.: þ65 6499 4786.E-mail address: [email protected] (S. Galelli).






http://ivs4em.deib.polimi.it/

F-sc

ore

Relative contribution

Results: feature extraction

Relative contribution

Fsco

re

ET Largest Class Random

Accuracy 0.75 0.56 0.44

F-score 0.48 0.18 0.25

# users correctly profiled

total # usersTrue Positive

True Positive + False Negative

True Positive

True Positive + False Positive

Results: behavioral model

Take home points

• Smart-meters can improve our understanding of residential water consumption behaviors at very high spatial and temporal resolution

• Feature extraction algorithms can identify key users’ featuresdetermining the observed water consumption behaviors

• The combination of smart meters and machine learning techniqueshas the potential for supporting the development of data-drivenbehavioral models

LONDON | UKThames Water water supply utility

15 million customers served

2.6 Gl/day drinking water distributed

Development plan: 3 Million smart meters installed by 2030

LOCARNO | CHSocietà Elettrica Sopracenerina

power supply utility, 80 thousand customers served

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

Almost 400 smart water meters installed

VALENCIA | ESEMIVASA water supply utility

2 million customers served

490,000 water smart meters currently installed

Development plan: 650,000 water smart meters installed by end 2015

Ongoing research

_ technological

_ financial








_ RESPONSE TO WDMS

Hour the day0 5 10 15 20 25

Normalized household consumption

0

0.05

0.1

0.15

WATER DATA END_USE ANALYTICS

47%

12%

9%

8%

23%

HPE

CDE CDE CDE CDE CDE CDE CDE

HPE HPE HPE HPE HPE HPEhighest contribution

lowest contribution

garden

shower

toilet

faucet

dishwasher

Ongoing research

_ technological

_ financial








_ RESPONSE TO WDMS

Ongoing research

CONSUMER PORTAL

ENGAGEMENT AND BEHAVIOURAL CHANGE

Ongoing research

Matteo [email protected]

@smartH2Oproject@NRMPolimi

@MxgTeo

Thank You

The event will be held on August 22-25 in the Monte Verità, Switzerland.More Info: www2.idsia.ch/cms/smartwater/

DropTheQuestion available on Google Play

Data-driven behavioural modelling of residential water consumption to inform water demand management...

Engineering

Transcript of Data-driven behavioural modelling of residential water consumption to inform water demand management...