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Sensors, Signals and Sense-making in Energy Conversations
- 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
- 9. 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
- 10. 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
- 11. Amplify the Network Effect “Smart meter” March 2010 “Smart meter” November 2010 “Smart meter” September 2010
- 12. 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.
- 13. Network of Energy Semantics Volume, Rhythm, Syntax
- 14. 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
- 15. 3.1 3.1.1 Daily Time Shots 2011.Mar.13 Network of Energy Semantics
- 16. Network of Energy Semantics
- 17. 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.
- 18. #California h=p://www.nexp.org/energy/hashtags September 19-‐25, 2011 5000 most frequently occurring hashtags
- 21. 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
- 23. Level 3 Global Green Tech Business Ecosystem Ecosystems for Transformation Level 3 -‐ Innova<on Ecosystems Dataset, July 2010 Nodes inflated by out-‐degree
- 24. 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
- 25. 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
- 26. Complex Decision Systems Balance Human & Automated Roles
- 27. 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
- 28. 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
- 29. 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