From smart meters
to smart behaviour
Harith Alani
http://people.kmi.open.ac.uk/harith/
@halani
harith-alani
@halani
Dagstu...
Social Web Communities - 2008
One year later ….
0"
0.2"
0.4"
0.6"
0.8"
1"
1" 5" 9" 13" 17" 21" 25" 29" 33" 37" 41" 45"
H.Index"
F2F"Degree"
F2F"Strength"
Physical-Cyber-S...
Table 1: Correlation Coefficients of dimensions
Dispersion Engagement Contribution Initiation Quality Popularity
Dispersion ...
Correlations
§  Between different behaviour
roles
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Churn Rate
FPR
TPR
0.0 0.2 0...
!
Composition and evolution of behaviour
macro level
micro level
And in the mean time …
GLOBAL
WARMING
Solar panels
Smart Meters
www.efergy.com
greenenergyoptions.co.uk
fastcompany.com
powerp.co.uk
www.energycircle.com
indiegogo.comgreent...
But the jury is still out
With Manfred’s perm
Fine, but what does this
have to do with behaviour?!
Need to change consumption behaviour
Nov 2012
•  Behaviour can be changed
•  Individual/community approaches
•  Multiple m...
•  Personal energy-saving targets
•  Community/social initiative lead to long-term change
•  Dynamic pricing schemes don’t...
Making the invisible visible
Feedback
§  What’s the optimal level of
detail ?
§  What feedback is suitable
for what type of
consumer?
§  What feedba...
Behaviour change models
http://www.enablingchange.com.au/7_doors_page.html
information
personalised drivers
tools
feedback...
Ø  Effectiveness of different strategies
Ø  Quantitative impact of change
Ø  Cause-effect indicators
Ø  Socio-demograp...
www.decarbonet.eu/
Stay tuned
From smart meters to smart behaviour
From smart meters to smart behaviour
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From smart meters to smart behaviour

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Short presentation at Dagstuhl seminar on Physical-Cyber-Social Computing, September 29 to October 4, 2013.
http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402

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From smart meters to smart behaviour

  1. 1. From smart meters to smart behaviour Harith Alani http://people.kmi.open.ac.uk/harith/ @halani harith-alani @halani Dagstuhl seminar on Physical-Cyber-Social Computing, 2013
  2. 2. Social Web Communities - 2008
  3. 3. One year later ….
  4. 4. 0" 0.2" 0.4" 0.6" 0.8" 1" 1" 5" 9" 13" 17" 21" 25" 29" 33" 37" 41" 45" H.Index" F2F"Degree" F2F"Strength" Physical-Cyber-Social behaviour
  5. 5. Table 1: Correlation Coefficients of dimensions Dispersion Engagement Contribution Initiation Quality Popularity Dispersion 1.000 0.277 0.168 0.389 0.086 0.356 Engagement 0.277 1.000 0.939** 0.284 0.151 0.926** Contribution 0.168 0.939** 1.000 0.274 0.086 0.909** Initiation 0.389 0.284 0.274 1.000 -0.059 0.513 Quality 0.086 0.151 0.086 -0.059 1.000 0.065 Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000 Behaviour analysis of online communities §  Bottom Up analysis §  Every community member is classified into a “role” §  Unknown roles might be identified §  Copes with role changes over time ini#ators   lurkers   followers   leaders   Structural, social network, reciprocity, persistence, participation Feature levels change with the dynamics of the community Associations of roles with a collection of feature-to-level mappings e.g. in-degree -> high, out-degree -> high Run rules over each user’s features and derive the community role composition Table 1: Correlation Coefficients of dimensions Dispersion Engagement Contribution Initiation Quality Popularity Dispersion 1.000 0.277 0.168 0.389 0.086 0.356 Engagement 0.277 1.000 0.939** 0.284 0.151 0.926** Contribution 0.168 0.939** 1.000 0.274 0.086 0.909** Initiation 0.389 0.284 0.274 1.000 -0.059 0.513 Quality 0.086 0.151 0.086 -0.059 1.000 0.065 Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000 Table 1: Correlation Coefficients of dimensions Dispersion Engagement Contribution Initiation Quality Popularity Dispersion 1.000 0.277 0.168 0.389 0.086 0.356 Engagement 0.277 1.000 0.939** 0.284 0.151 0.926** Contribution 0.168 0.939** 1.000 0.274 0.086 0.909** Initiation 0.389 0.284 0.274 1.000 -0.059 0.513 Quality 0.086 0.151 0.086 -0.059 1.000 0.065 Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000 Figure 7: Cumulative density functions of each dimension showing Figure 8: Boxplots of the feature distributions
  6. 6. Correlations §  Between different behaviour roles 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Churn Rate FPR TPR 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 User Count FPR TPR 0.00.20.40.60.81.0 TPR §  Between behaviour and activity §  Between behaviours and community health
  7. 7. ! Composition and evolution of behaviour macro level micro level
  8. 8. And in the mean time …
  9. 9. GLOBAL WARMING
  10. 10. Solar panels
  11. 11. Smart Meters www.efergy.com greenenergyoptions.co.uk fastcompany.com powerp.co.uk www.energycircle.com indiegogo.comgreentechadvocates.com
  12. 12. But the jury is still out
  13. 13. With Manfred’s perm
  14. 14. Fine, but what does this have to do with behaviour?!
  15. 15. Need to change consumption behaviour Nov 2012 •  Behaviour can be changed •  Individual/community approaches •  Multiple motivating factors •  Behaviour change is sustainable key findings •  Quantitative impact of specific changes •  Socio-demographic factors •  Gas vs electricity vs water •  Cost-effectiveness of interventions •  Longevity of change gaps August 2012
  16. 16. •  Personal energy-saving targets •  Community/social initiative lead to long-term change •  Dynamic pricing schemes don’t always work •  The “rebound effect” can emerge from short-term measures •  Role of technology, age, economic situation, culture, marketing, etc. •  Consumer ability to handle new technology, capital cost, trade-offs, and expected convenience
  17. 17. Making the invisible visible
  18. 18. Feedback §  What’s the optimal level of detail ? §  What feedback is suitable for what type of consumer? §  What feedback tools? What visualisations?
  19. 19. Behaviour change models http://www.enablingchange.com.au/7_doors_page.html information personalised drivers tools feedback conveniencesocial/ competitions behaviour change
  20. 20. Ø  Effectiveness of different strategies Ø  Quantitative impact of change Ø  Cause-effect indicators Ø  Socio-demographic factors Ø  Longevity of change 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Churn Rate FPR TPR
  21. 21. www.decarbonet.eu/ Stay tuned
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