Sharing and Communication around Household Energy Consumption

1. Sharing and Communication around Household Energy Consumption Tawanna Dillahunt Advisor: Jennifer Mankoff HCI Institute Carnegie Mellon University

2. U.S. households consume over 21.7% of total U.S. energy and generate over 21.1% of total U.S. carbon emissions [Gardner, et. al, 2008] Removed picture due to file size

3. Low-Income Households • 30% of U.S. households make < $30K/year [US Census, 2009] • Spend greater percentages of income on energy than affluent households [Cooper et al., 1983] • Median consumption almost as much as affluent households [Shui 2002] Removed picture due to file size

4. Research Gap • Low-income individuals are among those more likely to live in rental housing [Belsky and Drew, 2007; McArdle, 2009] • Renters constitute 30% of U.S. households [Current Housing Reports, 2008] • Few studies (at the time) targeted low-income households and renters [Chetty, et. al, 2008]

5. Research Questions • What are the dynamics of low-income households in terms of energy consumption? • How can household electricity monitoring devices most effectively work within the dynamics of a low-income household? • Can we mine energy monitoring data in order to provide advice about inefficiencies in energy use?

6. Thesis Statement Eco-visualizations designed to allow individuals to compare their consumption with others, to provide advice about inefficiencies and to actively engage around actions that affect energy consumption will: • encourage social interaction • raise awareness of energy conservative behaviors • help residents to negotiate energy use issues with stakeholders (landlords, housemates, community members)

7. What are the dynamics of low-income households in terms of energy consumption?

8. Qualitative Studies of Energy Use Study 1 Energy Use in Low-Income Households [Dillahunt, et. al, Ubicomp 2009] Study 2 Conflicts Between Landlords and Tenants [Dillahunt, et. al, Ubicomp 2010]

9. Study 1 • Do prior findings generalize to this community? • Motivations for saving energy? • Existing barriers? • How can we enhance technology to serve low-income communities?

10. StudyDesign • Photo-elicitation study • [Clark-IbáÑez, 2004] • Camera • Pen and Notebook to write about experiences Removed picture due to file size “Take pictures of objects and/or scenarios that make you think about personal energy use or anything that makes you think about energy”

11. StudyDesign • 26 participants across two locations • 15 NC participants • 11 PA participants • Diverse payment structures • Pay energy in full • Receive stipend • Pay no energy • Receive allocation Removed picture due to file size

12. Findings • Participants received very little feedback • Saving energy occurred even if participants did not pay for energy (prior habits) • Key factors leading to environmental behaviors in low-income households • External barriers • Future generations • Religious beliefs • Conflict between landlords and tenants around energy consumption Removed picture due to file size

13. Findings • Participants received very little feedback • Saving energy occurred evenwhenparticipants did not pay for energy (prior habits) • Key factors leading to environmental behaviors in low-income households • External barriers • Future generations • Religious beliefs • Conflict between landlords and tenants around energy consumption Removed picture due to file size

14. Findings • Participants received very little feedback • Saving energy occurred even if participants did not pay for energy (prior habits) • Key factors leading to environmental behaviors in low-income households • External barriers • Future generations • Religious beliefs • Conflict exists between landlords and tenants around energy consumption Removed picture due to file size

15. Study 2 • Interviewed landlords to get a balanced perspective • Story-telling and role play sessions to understand both perspectives Removed picture due to file size Removed picture due to file size

16. Sources of Conflict

17. Sources of Conflict

18. Sources of Conflict

19. Sources of Conflict Summary

20. Conflict Resolution

21. Conflict Resolution

22. Conflict Resolution

23. Conflict Resolution

24. Solution • Sensing technologiesand social computing • can play a role in conflict resolution because of their • abilities to provide new information and improve • communication of information

25. Opportunities • Sensing technologies produce new information • Social technologies facilitate sharing • Both technologies influence action Removed picture due to file size

26. How can household electricity monitoring devices most effectively work within the dynamics of a low-income household?

27. Key Factors to Resolve Conflict • Sharing energy-related information led to community action • Better communication (i.e., alerting landlords to household inefficiencies, informing tenants of ways to save energy) • Negotiation

28. Thesis Statement Eco-visualizations designed to allow individuals to compare their consumption with others and to actively engage around actions that affect energy consumption will: • encourage social interaction • raise awareness of energy conservative behaviors • help residents to negotiate energy use issues with stakeholders (landlords, housemates, community members)

29. Research Goals To develop a tool for supporting comparisons and social collaboration Identify how sharing and collaboration affect energy consumption and communication within communities Longitudinal deployment across low-income households of real-time energy monitoring devices to aid in data collection

30. Method • Flesh out usability details • Website? • Mobile? • Kiosk? • Tool development • Deployment of tool and The Energy Detective (TED) Removed picture due to file size

31. Website Design

32. Main Office (Mezzanine) Build B Build A Longitudinal Deployment Removed picture due to file size

33. Social vs. Non-Social (Option 1) • Between subject design with two groups • Social • Non-social • Independent Variables • Website comparison and social/discussion features • Dependent Variables • Social interaction • Raised awareness • Negotiation (# of issues reported, interaction with landlord, etc.)

34. Individual vs. Community (Option 2) • Between subject design with two groups • Individual • Community • Independent Variables • PC versus Kiosk • Dependent Variables • Social interaction • Raised awareness • Negotiation (# of issues reported, interaction with landlord, etc.)

35. Personal vs. Group Incentive (Option 3) • Between subject design with two groups • Individual • Group • Independent Variables • Group incentive versus individual incentive • Dependent Variables • Social interaction • Raised awareness • Negotiation (# of issues reported, interaction with landlord, etc.)

36. Quantitative Measures • Encourage social interaction • # of comments, questions asked, questions answered • # of posts to landlords • How frequently do participants access the intervention (kiosk/mobile/website) and how long they interact? • Raise awareness of energy conservative behaviors • Total energy consumption each month • Number of actions “done” or committed to • Negotiation Measures • Issues reported/issues addressed over time

37. Qualitative Measures (Pre/Post) • Encourage social interaction • Reported interaction with household members, neighbors, landlords • Discussion about intervention and data • Frequency and span of discussions • Raise awareness of energy conservative behaviors • Environmental attitudes • Environmental awareness • Attend outside education events (1st timers) • Negotiation Measures • Issues reported and addressed over time • Was information from intervention used in landlord discussions? • # of successful negotiations

38. Technology Considerations • Mobile vs. laptop or netbook vs. Kiosk • Mobile makes visualizations harder (push not pull) • PC is less common in low-income households, requires individuals to access the pc for information • Kiosk is less accessible but may help to increase social interaction • Fully networked machine vs. limited functionality • How does adding an internet pc change households?

39. Can we mine energy monitoring data in order to provide advice about inefficiencies in energy use?

40. Energy Data Analysis 1: Collect Baseline Data 2: Gather Data • Similar buildings, differing infrastructure • Department of Energy data on averages 3: Data Analysis • Machine learning: automate interpretation 4: Data Interpretation • Help people interpret with a coherent visualization

41. Pilot Work • Craigslist plug-in • Automate the interpretation of how efficient/inefficient apartments for rent are (like a walk-ability score but for energy) • Location • Apartment Size • Year built • Type of heating and cooling • Types and ages of appliances • Etc. • Compare plug-in estimates with baseline data to show accuracy

42. Conclusions

43. Main Contributions • A tool for supporting comparison and collaboration • A model to provide advice about inefficiencies • Design recommendations • Demonstrated results of integrating social computing and ubiquitous computing technologies around energy consumption

44. Schedule Jun Apr May June July Aug Sep Oct Nov Dec Jan Feb Mar Proposal Prep Doc & Pres Done! Tool Development TED Deployment Gather baseline data Data Analysis (surveys, interaction info….) Dissertation Writing Defense!

45. Thank You Tawanna Dillahunt tdillahu@cs.cmu.edu Sponsors

46. Questions & Feedback • What options are more interesting? • Feasibility of providing advice about inefficiencies in energy (based on energy monitoring data) • How to minimize risk? • What if no one interacts with the interventions? • Mobile • Website • Kiosk