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Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
Sensors, Signals and Sense-making in Human-Energy Relationships
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Sensors, Signals and Sense-making in Human-Energy Relationships

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Presentation by Martha G Russell to Wireless World Research Forum in Vancouver BC on October 21, 2013. Smart meters and related sensing technologies promise that energy information will change energy …

Presentation by Martha G Russell to Wireless World Research Forum in Vancouver BC on October 21, 2013. Smart meters and related sensing technologies promise that energy information will change energy use. However, information complexity, poorly designed interfaces, and lack of engagement jeopardize billion dollar infrastructure investments because sensors, signals and sense-making are not  designed to modify behavior and because context is ignored. Information and resources flow through human relationships when context and values are shared.  Using social media to harvest Twitter data about energy use and online press release type information about business innovation, social network analysis provides insights about issue framing, public engagement, and innovation ecosystems.  These signals are seen in the larger context of the Stanford ARPA-E Sensor and Behavior Initiative to develop a comprehensive human-centered solution that leverages the anticipated widespread diffusion of energy sensors to significantly reduce and shift energy use.

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  • 1. Sensors, Signals and Sense-making in Human-Energy Relationships Martha  G  Russell     Human  Sciences  &  Technology  Advanced  Research  Ins<tute   HSTAR     mediaX  at  Stanford  University  
  • 2. Wireless Networks Powered by the Smart Grid •  Sensors,  Signals  and  Sense-­‐making  in  Human-­‐Energy     •  The  issues  are  complex  and  require  collabora<on   –  Interdisciplinary   –  Industry-­‐university   –  Intersector   •  Humans  must  be  considered  at  every  stage  of  the   technology-­‐design-­‐implementa<on  processes  
  • 3. Stanford Clinical Anatomy Scans ! ! ! a t S T A N F O R D U! I V E R S I T Y N Discovery Collaborations ! Span Stanford Labs! Distributed Vision Lab SCANs DVL Electrical Engineering Computer Science Philosophy EE Psychology Psy CS Linguistics Communication Between Humans and Interactive Media Ling CHIMe Phil SHL VHIL Stanford Humanities Lab Graduate School Of Business GSB Virtual Human Interaction Lab Center for the Study Of CSLI Language & Information SCIL Art EngineeringEng & Product Design Law PBLL Work Technology & Organization Project Based Learning Laboratory PBLL SSP Symbolic LIFE Systems Program Center for Legal Informatics Ed Stanford Center for Innovations in Learning Digital Art Center School of Education; Education and Learning Sciences Des Stanford Joint Program in Design d.school Learning in Informal and Formal Environments
  • 4. Wireless Networks Powered by the Smart Grid •  Accelera<ng  Transforma<on     –  Changing  Energy  Behavior  of  Consumers   •  Context,  Choice  &  Control   •  Networks  of  Energy  Seman<cs   –  Transforming  Business  Ecosystems   •  Improving  the  Quality  of  Decision  Making   –  Preserving  ambiguity  and  assump<ons   –  Reducing  bias   –  Balancing  human-­‐made  decisions  with  automated  systems  
  • 5. Changing Energy Behavior of Consumers COLLECT   &   CAPTURE   TECHNOLOGY   PLATFORM   PROGRAMS   MODELING   10/21/13   ECONOMETRIC ESTIMATION COMMUNICATION NETWORK MEDIA PROGRAMS SEGMENTATION POLICY PROGRAMS PRESENT   &   INFORM   MULTI-AGENT SIMULATION ANALYTICS ENERGY   USE   FOUNDATIONAL WORK DATABASE   PERVASIVE   SENSORS   SENSOR DEVELOPMENT SYSTEM   COMMUNITY PROGRAMS   TRANSFORMATION   ENGINE    WEB  ENABLED   DEVICES   INDIVIDUAL   GROUP   BEHAVIOR   CHANGE   5  
  • 6. Changing Energy Behavior of Consumers •  •  Off  isn’t  “off”   Stretching  monetary  incen<ves  
  • 7. Measuring Impact of Transformative Coalitions Impact over time Measure & Track Co-Create Value Shared   Vision   Transforma<on   Actors & Events Coalitions Interact & Feedback Martha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared vision and network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011. 9
  • 8. The  Consumer  Conversa<on   140  Characters       Blogs     Press  Releases     Web  Sites   Paid  Media   #martharussell   hap://mediax.stanford.edu/changeeb.html   h=p://www.nexp.org/energy/hashtags  
  • 9. Amplify the Network Effect “Smart  meter”     March  2010   “Smart  meter”   November  2010     “Smart  meter”   September  2010    
  • 10. Network of Energy Semantics Anatomy  of  a  Tweetology   •  Way  users  are  related  to   messages  –  author,  receiver,   men<oned   –  RT  PG&E4me   •  Type  of  messages  -­‐   broadcast,  conversa<on   –  #mediaxstanford   –  @martharussell   •  Related  resource  –  content   and  reference  to  it,  term   disambigua<on   –  url,  bit.ly   –  Stanford  Ecolinguis<c  Ontology   •  June  Flora,  Carrie  Armel,  M  Russell   Claudia  Wagner  and  Markus  Strohmaier,  “The  Wisdom  in  Tweetonomies:  Acquiring  Latent  Conceptual  Structures  from  Social  Awareness   Streams,”  WWW2010,  April  26-­‐30,  2010,  Raleigh,  North  Carolina.  
  • 11. Network of Energy Semantics Volume,  Rhythm,  Syntax  
  • 12. Network of Energy Semantics Russell,  M.,  Flora,  J.A.  Strohmaier,  M.,   Poschko,  J.,  Perez,  J.  Yu,  J.,  Smith,  M.A.,   Rubens,  N.  (2013).  Seman<c  Analysis  of   Energy-­‐Related  Conversa<ons  in  Social   Media:  A  Twiaer  Case  Study.  in  L.Kahle  and   E.G.  Aaay,  Eds.,  Communica<ng   Sustainability  for  the  Green  Economy,  M.E.   Sharp,  Armonk,  NY..     2,472,900  tweets   18,338  hashtags  (w/  occurrence  of  3  or  more)  Date   Range:  3.Sep.2010  -­‐  Jan.3.2011  (4  months)  Data   Collec<on  Frequency:  daily  snapshots   Keywords:  keywords  related  to  energy  saving   behaviors  (see  keywords.txt).     h=p://mediax.stanford.edu/energydata.html  
  • 13. 3.1 3.1.1 Daily Time Shots 2011.Mar.13 Network of Energy Semantics
  • 14. Network of Energy Semantics
  • 15. Network of Energy Semantics This  dataset  was  constructed  as  a  part  of  the  "Social  Media   Analy<cs  for  Monitoring  and  Changing  Energy  Consump<on   Behavior"  ini<a<ve  of  the  Stanford  ARPAe  project.    For  more   details  please  refer  to:  M.  G.  Russell,  J.  Flora,  M.  Strohmaier,  J.   Poschko,  R.  Perez,  N.  Rubens.    Seman<c  Analysis  of  Energy-­‐ Related  Conversa<ons  in  Social  Media:  A  Twiaer  Case  Study.     Interna<onal  Conference  of  Persuasive  Technology  (Persuasive   2011),  Columbus,  OH,  USA,  Jun.2011.      The  ini<al  purpose  of  construc<ng  of  this  dataset  was  to  assist  in  understanding  the  role  of  social  media  in  changing  consumer’s   energy  behavior.    We  believe  it  could  be  useful  for  other  purposes  as  well,  and  therefore  are  releasing  it  publicly.    Data  was  acquired  on  a  daily  basis  by  u<lizing  the  NodeXL  Twiaer  Importer  module  *xl1,  which  captured  the  latest  messages   containing  energy  related  keywords  (see  keywords.txt).    The  eco-­‐linguis<c  keywords  used  to  collect  the  tweets  was  developed  at   Stanford  University  by  Drs.  June  Flora,  Carrie  Armel,  and  Martha  Russell,  under  sponsorship  from  the  US  Advanced  Research   Projects  Agency  for  Energy,  and  Media  X  at  Stanford  University.    
  • 16. #California h=p://www.nexp.org/energy/hashtags   September  19-­‐25,  2011   5000  most  frequently  occurring  hashtags    
  • 17. Business Ecosystem Transformation Impact over time Actors & Events Shared   Vision   Transforma<on   Coalitions Martha G. Russell, Kaisa Still, Jukka Huhtamaki, and Neil Rubens, “Transforming innovation ecosystems through shared vision and network orchestration,” Triple Helix IX Conference, Stanford University, July 13, 2011. 21
  • 18. Rela<onships  co-­‐create  value  
  • 19. Level 3 Global Green Tech Business Ecosystem Ecosystems for Transformation Level  3  -­‐  Innova<on  Ecosystems  Dataset,  July  2010   Nodes  inflated  by  out-­‐degree  
  • 20. Quality of Decisions •  Complex  Decisions   –  Influenced  by  bias   –  Lack  evalua<on  process  for  next  decision   –  Reflect  judgment  of  proximal  exper<se   –  Osen  require  reduc<on  of  variables  to   •  Make  #  of  considera<ons  manageable  by  group   •  Limit  vulnerability  of  missing/non-­‐available  data   •  Reflect  priori<es  of  decision  makers   –  Hard  to  change  because  assump<ons  about  key  variables   are  set  aside    
  • 21. Decision Support •  Develop  crowd-­‐sourced  input  (Expert  flash  teams,  M.  Bernstein,  2013)   and  make  available  on  assump<ons   •  Use  case  =  electric  vehicle  purchase  &  care   •  Create  guidelines  for  using  a  suite  of  crowd-­‐sourcing   tools  and  metrics  to  document/track  assump<ons   (Visual  Support  for  Complex  Decisions,  Basole,  Russell,  S<ll,  Huhtamäki,  under  review)   –  Use  case  =  electric  vehicle  incen<ves  in  communi<es   •  In  order  to  create  synergis<c  insights  about  events,   event  sequences  and  <ming     –  Use  case  =  policies  and  program  decisions  for  energy  use   reduc<on  
  • 22. Complex Decision Systems Balance Human & Automated Roles
  • 23. Quality of Decisions •  Preserving  ambiguity  and  assump<ons   •  Reducing  bias   •  Balancing  roles  of  humans  and  automated  systems  in   decisions   •  Clarifying  and  protec<ng  digital  human  rights  
  • 24. Balance Biz – Personal Interests Clarify Consumers’ Digital Rights Consumer Bill of Digital Rights Information Disclosure Confidentiality of Information Security of Information Participation in Advertising Decisions Respect and Nondiscrimination Complaints and Appeals
  • 25. Thank you •  Seeking  Collaborators   •  What  can  we  do  together  that  neither  of  us   could  do  alone?   •  Martha  G  Russell,  PhD   •  Martha.Russell@stanford.edu   •  650-­‐646-­‐1331  

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