water projects, case studies and experiences

Water projects, case studies and experiencesPiero Fraternali Giorgia Baroffio

Politecnico di Milanopiero.fraternali | giorgia.baroffio@polimi.it

OUR COMMON PROBLEM: THE EXAMPLE OF: LONDON

2

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

Demand management

DEMAND MANAGEMENT

CAPACITY EXPANSION

2010 2040

consortium cluster

WATER DEMAND MANAGEMENT STRATEGIES

5

DEMAND MANAGEMENT

_ technological_ financial_ legislative_ operation and maintenance_ education

CONSUMERS’ COMMUNITY

WATER CONSUMPTION

MONITORING

INCLUDING USER MODELLING IN THE LOOP

6

DEMAND MANAGEMENT

CONSUMERS’ COMMUNITY

WATER CONSUMPTION

MONITORING

USER MODELLING

_ technological_ financial_ legislative_ operation and maintenance_ education

_ Water consumption drivers identification_ Water consumption level forecast at the customer scale

FEATURE EXTRACTION-BASED USER PROFILING

7

HOUSEHOLD WATER CONSUMPTION

Users’ consumption class (label)

USERS CLUSTERING

FEATURE EXTRACTION

MODEL LEARNING

Users’ consumption class (label)

forecast

HOUSEHOLD and CONSUMERS’ PSYCHOGRAPHICS

Relevant consumption determinants subset

The platform

21

Gamified Consumer Portal

Serious games for engagement and behavior change

Status

• Today– Gamified Consumer Portal in alpha test – 400 households in Canton Ticino (CH), AMI at 1 hr resolution

• End of 2015– Deployment in Valencia with Emivasa, Grupo Aguas de Valencia.– 650'000 smart meter in 2015 (490'000 already deployed)– Access to 2500+ smart meter at 1h resolution

• 2016– Deployment in London with Thames Water

• Long term investment: 3 M smart meter before 2030 (40’000 in 2015)

thank you

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

@smartH2Oproject #SmartH2O@AndreaCominola@NRMPolimi

piero.fraternali | giorgia.baroffio@polimi.it

Politecnico di Milano Department of Electronics,

Information and Bioengineering

K-means clustering (k=3)

FEATURE EXTRACTION-BASED USER PROFILING

11

USERS CLUSTERING

FEATURE EXTRACTION

MODEL LEARNING

_ FEATURE WEIGHTING: Chi-square scoreInformation Gain

_ FEATURE SELECTION: Fast Correlation Based FilterCorrelation Feature SelectionBayesian Logistic RegressionSparse Bayesian Multinomial Logistic Regression

Users’ consumption label prediction

_ Naïve Bayes Classifier_ j48 Decision Tree algorithm

Source: Zhao et al, 2010; Yu and Liu, 2003; Guyon et al., 2002; Cawley et al., 2007; Duda and Hart, 1973; Quinlan, 1993.