Presentation of SmartH2O project components

1. Water projects, case studies and experiences
Piero Fraternali Giorgia Baroffio
Politecnico di Milano
piero.fraternali | giorgia.baroffio@polimi.it

2. OUR COMMON PROBLEM: THE EXAMPLE OF: LONDON
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UK_WATER SUPPLY UTILITY
15 million customers
2.6 Gl/day drinking water
3 billion $ revenue (2013-14)

3. Demand management
DEMAND MANAGEMENT
CAPACITY EXPANSION
2010 2040

4. consortium cluster

5. WATER DEMAND MANAGEMENT STRATEGIES
5
DEMAND MANAGEMENT
_ technological
_ financial
_ legislative
_ operation and maintenance
_ education
CONSUMERS’
COMMUNITY
WATER
CONSUMPTION
MONITORING

6. 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

7. 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

8. The platform
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9. Gamified Consumer Portal

10. Serious games for engagement and behavior
change

11. 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)

12. 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

13. K-means clustering (k=3)
FEATURE EXTRACTION-BASED USER
PROFILING
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USERS CLUSTERING FEATURE EXTRACTION MODEL LEARNING
_ FEATURE WEIGHTING:
Chi-square score
Information Gain
_ FEATURE SELECTION:
Fast Correlation Based Filter
Correlation Feature Selection
Bayesian Logistic Regression
Sparse 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.