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

Transcript

  • 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. Social Web Communities - 2008 3. One year later . 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. Table 1: Correlation Coecients 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 users features and derive the community role composition Table 1: Correlation Coecients 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 Coecients 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. 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. ! Composition and evolution of behaviour macro level micro level 8. And in the mean time 9. GLOBAL WARMING 10. Solar panels 11. Smart Meters www.efergy.com greenenergyoptions.co.uk fastcompany.com powerp.co.uk www.energycircle.com indiegogo.comgreentechadvocates.com 12. But the jury is still out 13. With Manfreds perm 14. Fine, but what does this have to do with behaviour?! 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. Personal energy-saving targets Community/social initiative lead to long-term change Dynamic pricing schemes dont 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. Making the invisible visible 18. Feedback Whats the optimal level of detail ? What feedback is suitable for what type of consumer? What feedback tools? What visualisations? 19. Behaviour change models http://www.enablingchange.com.au/7_doors_page.html information personalised drivers tools feedback conveniencesocial/ competitions behaviour change 20. Effectiveness of different strategies Quantitative impact of change Cause-effect indicators Socio-demographic factors 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. www.decarbonet.eu/ Stay tuned

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