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From smart meters to smart behaviour
 

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.

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 From smart meters to smart behaviour Presentation Transcript

    • 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
    • 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-Social behaviour
    • 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
    • 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
    • ! 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.comgreentechadvocates.com
    • 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 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
    • •  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
    • Making the invisible visible
    • Feedback §  What’s the optimal level of detail ? §  What feedback is suitable for what type of consumer? §  What feedback tools? What visualisations?
    • Behaviour change models http://www.enablingchange.com.au/7_doors_page.html information personalised drivers tools feedback conveniencesocial/ competitions behaviour change
    • 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
    • www.decarbonet.eu/ Stay tuned