water projects, case studies and experiences
TRANSCRIPT
Water projects, case studies and experiencesPiero Fraternali Giorgia Baroffio
Politecnico di Milanopiero.fraternali | [email protected]
OUR COMMON PROBLEM: THE EXAMPLE OF: LONDON
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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
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DEMAND MANAGEMENT
_ technological_ financial_ legislative_ operation and maintenance_ education
CONSUMERS’ COMMUNITY
WATER CONSUMPTION
MONITORING
INCLUDING USER MODELLING IN THE LOOP
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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
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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
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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 | [email protected]
Politecnico di Milano Department of Electronics,
Information and Bioengineering
K-means clustering (k=3)
FEATURE EXTRACTION-BASED USER PROFILING
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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.