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

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

Martha G Russell

Human Sciences & Technology Advanced Research Ins<tute HSTAR

mediaX at Stanford University

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

CSLI

Eng

EE Psy

Ed

SSP

SCIL

SCANs

PBLL

GSB

Ling CHIMe

Art

!!!!

Discovery Collaborations !Span Stanford Labs!

School of Education; Education and Learning Sciences

Digital Art Center

Graduate School Of Business

Communication Between Humans and Interactive Media

Stanford Center for Innovations in Learning

Project Based Learning Laboratory

Symbolic Systems Program

Engineering & Product

Design

Center for the Study Of Language & Information

Stanford Clinical Anatomy Scans

Computer Science

Psychology

Linguistics

Phil

Philosophy

Law Center for Legal Informatics

LIFE Learning in Informal and Formal Environments

CS

Electrical Engineering

SHL Stanford Humanities Lab

VHIL Virtual Human Interaction Lab

PBLL Work Technology & Organization

DVL Distributed Vision Lab

Des Stanford Joint Program in Design d.school

at S T A N F O R D U N I V E R S I T Y

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

Changing Energy Behavior of Consumers

10/21/13 5

ENERGY USE

TRANSFORMATION ENGINE

BEHAVIOR CHANGE

COLLECT &

CAPTURE

PERVASIVE SENSORS

SENSOR DEVELOPMENT

COMMUNICATION NETWORK

DATABASE

ANALYTICS

PROGRAMS

FOUNDATIONAL WORK

MEDIA PROGRAMS

POLICY PROGRAMS

COMMUNITY PROGRAMS

MODELING

ECONOMETRIC ESTIMATION

SEGMENTATION

MULTI-AGENT SIMULATION

TECHNOLOGY PLATFORM

PRESENT &

INFORM

WEB ENABLED DEVICES

SYSTEM

GROUP INDIVIDUAL

Changing Energy Behavior of Consumers

•  Off isn’t “off” •  Stretching monetary incen<ves

Actors & Events

Impact over time

Coalitions

Shared Vision

Transforma<on

Measure & Track

Interact & Feedback

Co-Create Value

9 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.

Measuring Impact of Transformative Coalitions

Paid Media

Web Sites

Press Releases

Blogs

The Consumer Conversa<on

140 Characters

#martharussell hap://mediax.stanford.edu/changeeb.html

h=p://www.nexp.org/energy/hashtags

“Smart meter” March 2010

“Smart meter” September 2010

“Smart meter” November 2010

Amplify the Network Effect

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.

Volume, Rhythm, Syntax Network of Energy Semantics


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

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..

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5


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.

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.


#California

h=p://www.nexp.org/energy/hashtags September 19-‐25, 2011

5000 most frequently occurring hashtags

Actors & Events

Impact over time

Coalitions

Shared Vision

Transforma<on

21 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.

Business Ecosystem Transformation

Rela<onships co-‐create value

Level 3 -‐ Innova<on Ecosystems Dataset, July 2010 Nodes inflated by out-‐degree

Ecosystems for Transformation Level 3 Global Green Tech Business Ecosystem

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

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

Complex Decision Systems Balance Human & Automated Roles

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

Balance Biz – Personal Interests Clarify Consumers’ Digital Rights

Consumer Bill of Digital Rights

Information Disclosure Con!dentiality of Information Security of Information Participation in Advertising Decisions Respect and Nondiscrimination Complaints and Appeals

Thank you

•  Seeking Collaborators •  What can we do together that neither of us could do alone?

•  Martha G Russell, PhD •  [email protected] •  650-‐646-‐1331

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

Education

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