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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