Sharing and Communication around Household Energy Consumption

Sharing and Communication around Household Energy Consumption

Tawanna DillahuntAdvisor: Jennifer MankoffHCI InstituteCarnegie Mellon University

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

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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]


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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]

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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?

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Thesis StatementEco-visualizations designed to allow individuals tocompare their consumption with others, to provide

advice about inefficiencies and to actively engage around

actions that affect energy consumption will:

1. encourage social interaction2. raise awareness of energy conservative behaviors 3. help residents to negotiate energy use issues with

stakeholders (landlords, housemates, community members)

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

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Qualitative Studies of Energy UseStudy 1

Energy Use in Low-Income Households [Dillahunt, et. al, Ubicomp 2009]

Study 2Conflicts Between Landlords and Tenants [Dillahunt, et. al, Ubicomp 2010]

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• Do prior findings generalize to this community?

• Motivations for saving energy?• Existing barriers?• How can we enhance technology

to serve low-income communities?

Study 1

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Photo-elicitation study

[Clark-IbáÑez, 2004]– Camera– Pen and Notebook

to write about experiences

“Take pictures of objects and/or scenarios that make you think about personal energy use or anything that makes you think

about energy”

Study Design


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• 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

Study Design


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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


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even when participants did not pay for energy (prior habits)


• Conflict between landlords and tenants around energy consumption


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even if participants did not pay for energy (prior habits)


• Conflict exists between landlords and tenants around energy consumption


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Study 2

• Interviewed landlords to get a balanced perspective

• Story-telling and role play sessions to understand both perspectives



Sources of ConflictTENANTS 1PHOTOS +

INTERVIEWS

LANDLORDSINTERVIEWS

TENANTS 2ROLE-PLAYING

Expectations ✔

Money ✔ ✔

Power Imbalance

✔ ✔


INTERVIEWS

LANDLORDSINTERVIEWS


Expectations ✔

Money ✔ ✔

Power Imbalance

✔ ✔


INTERVIEWS

LANDLORDSINTERVIEWS


Expectations ✔

Money ✔ ✔

Power Imbalance

✔ ✔

Sources of Conflict Summary

TENANTS 1PHOTOS +

INTERVIEWS

LANDLORDSINTERVIEWS


Expectations ✔

Money ✔ ✔

Power Imbalance

✔ ✔

Conflict ResolutionTENANTS (1&2)

PHOTOS + INTERVIEWS, ROLE-PLAYING

LANDLORDSINTERVIEWS

Knowledge ✔ ✔

Communication/Negotiation

✔ ✔

Community Action ✔



LANDLORDSINTERVIEWS

Knowledge ✔ ✔


✔ ✔

Community Action ✔



LANDLORDS

INTERVIEWS

Knowledge ✔ ✔


✔ ✔

Community Action

✔

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Conflict ResolutionSTUDIES 1&2

TENANTSSTUDY 2LANDLO

RDSKnowledge ✔ ✔


✔ ✔

Community Action

✔

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Solution

Sensing technologies and social computing

can play a role in conflict resolution because of their

abilities to provide new information and improve

communication of information

Opportunities• Sensing technologies produce new

information• Social technologies facilitate sharing• Both technologies influence action


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

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Key Factors to Resolve Conflict

1. Sharing energy-related information led to community action

2. Better communication (i.e., alerting landlords to household inefficiencies, informing tenants of ways to save energy)

3. Negotiation

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Thesis StatementEco-visualizations designed to allow individuals tocompare 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)

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Research GoalsTo 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

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Method• Flesh out usability

details–Website?–Mobile?– Kiosk?

• Tool development• Deployment of tool

and The Energy Detective (TED)


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Website Design

Tawanna Dillahunt

Feedback results to date...

32Longitudinal Deployment

Build A Build BMain Office(Mezzanine)


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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.)

Tawanna Dillahunt

Saving as an individual or saving as a group...

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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.)

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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.)

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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

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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

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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?

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

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Energy Data Analysis

1: Collect Baseline Data2: 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

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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

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Conclusions

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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

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ScheduleApr May June July Aug Sep Oct Nov Dec Jan Feb Mar

Proposal Prep

TED Deployment

Defense!

Doc & Pres Done!

Gather baseline data

Jun

Data Analysis (surveys, interaction info….)

Tool Development

Dissertation Writing

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Thank You

Tawanna [email protected]

Sponsors

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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

Documents

Sharing and Communication around Household Energy Consumption