Sensors, Signals and
Sense-making in
Human-Energy
Relationships
Martha	
  G	
  Russell	
  
	
  
Human	
  Sciences	
  &	
  ...
Wireless Networks
Powered by the Smart Grid
•  Sensors,	
  Signals	
  and	
  Sense-­‐making	
  in	
  Human-­‐Energy	
  	
 ...
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 L...
Wireless Networks
Powered by the Smart Grid
•  Accelera<ng	
  Transforma<on	
  	
  
–  Changing	
  Energy	
  Behavior	
  o...
Changing Energy
Behavior of Consumers

COLLECT	
  
&	
  
CAPTURE	
  

TECHNOLOGY	
  
PLATFORM	
  

PROGRAMS	
  

MODELING	...
Changing Energy
Behavior of Consumers

• 

•  Off	
  isn’t	
  “off”	
  
Stretching	
  monetary	
  incen<ves	
  
Measuring Impact of
Transformative Coalitions
Impact
over time

Measure & Track

Co-Create
Value

Shared	
  
Vision	
  
Tr...
The	
  Consumer	
  Conversa<on	
  
140	
  Characters	
  
	
  	
  

Blogs	
  	
  
Press	
  Releases	
  	
  
Web	
  Sites	
 ...
Amplify the Network Effect

“Smart	
  meter”	
  	
  
March	
  2010	
  

“Smart	
  meter”	
  
November	
  2010	
  	
  

“Sm...
Network of Energy Semantics
Anatomy	
  of	
  a	
  Tweetology	
  
•  Way	
  users	
  are	
  related	
  to	
  
messages	
  –...
Network of Energy Semantics

Volume,	
  Rhythm,	
  Syntax	
  
Network of Energy Semantics
Russell,	
  M.,	
  Flora,	
  J.A.	
  Strohmaier,	
  M.,	
  
Poschko,	
  J.,	
  Perez,	
  J.	
 ...
3.1
3.1.1

Daily Time Shots
2011.Mar.13

Network of Energy Semantics
Network of Energy Semantics
Network of Energy Semantics

This	
  dataset	
  was	
  constructed	
  as	
  a	
  part	
  of	
  the	
  "Social	
  Media	
  ...
#California

h=p://www.nexp.org/energy/hashtags	
  
September	
  19-­‐25,	
  2011	
  
5000	
  most	
  frequently	
  occurr...
Business Ecosystem
Transformation
Impact
over time

Actors &
Events

Shared	
  
Vision	
  
Transforma<on	
  
Coalitions

M...
Rela<onships	
  co-­‐create	
  value	
  
Level 3 Global Green Tech Business Ecosystem

Ecosystems for Transformation

Level	
  3	
  -­‐	
  Innova<on	
  Ecosystems	...
Quality of Decisions
•  Complex	
  Decisions	
  
–  Influenced	
  by	
  bias	
  
–  Lack	
  evalua<on	
  process	
  for	
  ...
Decision Support
•  Develop	
  crowd-­‐sourced	
  input	
  (Expert	
  flash	
  teams,	
  M.	
  Bernstein,	
  2013)	
  
and	...
Complex Decision Systems
Balance Human & Automated Roles
Quality of Decisions
•  Preserving	
  ambiguity	
  and	
  assump<ons	
  
•  Reducing	
  bias	
  
•  Balancing	
  roles	
  ...
Balance Biz – Personal Interests
Clarify Consumers’ Digital Rights

Consumer Bill
of Digital Rights
Information Disclosure...
Thank you
•  Seeking	
  Collaborators	
  
•  What	
  can	
  we	
  do	
  together	
  that	
  neither	
  of	
  us	
  
could	...
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 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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Sensors, Signals and Sense-making in Human-Energy Relationships

  1. 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. 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. 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. 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. 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. 6. Changing Energy Behavior of Consumers •  •  Off  isn’t  “off”   Stretching  monetary  incen<ves  
  7. 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. 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. 9. Amplify the Network Effect “Smart  meter”     March  2010   “Smart  meter”   November  2010     “Smart  meter”   September  2010    
  10. 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. 11. Network of Energy Semantics Volume,  Rhythm,  Syntax  
  12. 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. 13. 3.1 3.1.1 Daily Time Shots 2011.Mar.13 Network of Energy Semantics
  14. 14. Network of Energy Semantics
  15. 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. 16. #California h=p://www.nexp.org/energy/hashtags   September  19-­‐25,  2011   5000  most  frequently  occurring  hashtags    
  17. 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. 18. Rela<onships  co-­‐create  value  
  19. 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. 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. 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. 22. Complex Decision Systems Balance Human & Automated Roles
  23. 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. 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. 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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