Conversation with my Washing Machine: An in-the-wild Study of Demand Shifting with

Self-generated Energy

Jacky Bourgeois, Janet van der Linden, Gerd Kortuem,

Blaine A. Price and Christopher Rimmer

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In collaboration with

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Electricity generation with solar panels alters people’s relationship with energy

“Energy farmers”

Local Energy Generation is Complex

• Self-generated energy is used locally or is exported to grid

• Additional energy is imported from the grid if required

• Import costs are higher than export payments received

• Generation incentive payments vary by country

“optimizing” energy use in the home is complicated

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Solar Photovoltaic (PV)

Generation

ExportTo the grid

ImportFrom the grid

Self-consumption

Local Energy Generation is Complex

“Energy Gap”: Consumption and local generation are out of sync

• Generation and consumption vary during day

• Generation and consumption vary by weather and season

• Typically generation peaks around midday, consumption peaks in early evening

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Electricity Profile of household #12 on 7 May 2013 (Consumption vs Generation)

Previous Research

• Most ubicomp and HCI energy research has focused on consumption and demand reduction

• “Double-dividend of solar generation” [Keirstead 2007]: households adopt new energy saving practices

• “Looking out of the window” [Price et al 2013]: householders estimate weather impact to shift demand

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What role can Ubicomp technology play in enabling or supporting new

energy practices in households with solar generation?

Specifically: demand shifting

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Case Study: Doing Laundry with Washing Machine

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Laundry practices and washing machine use is good case study:

• Everyone needs to wash clothes

• Involves whole family

• Temporal constraints (deadlines)

• Environmental impact

• Emerging demand-shifting practices

by Gloria Garcia

“In-the-Wild” Study with Households

Objective

• Understand household practices

• Explore design alternatives for in-home technology

Scope

• 8 Months

• 18 households

• 64 participants

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Study Methodology

• Home instrumentation

• Participatory energy data analysis

• Design and deployment of technology interventions

• Qualitative studies:

• Home visits

• Interviews & focus groups

• Thematic analysis

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Study Methodology: Energy Data

• 20M data points over 2 years

• Household electricity generation

• Household electricity import

• Household electricity export

• Washing machine use (timing and electricity consumption)

• Other appliances (timing and electricity consumption)

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Fixing technology installation presented an opportunity for qualitative data gathering

Four Technology Interventions

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#1 Delayed Energy Feedback via Email

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#1 Delayed Energy Feedback via Email

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• Participants received email with summary energy report few days after they have used the washing machine

• Report outlines:• Predicted solar energy

generation for next 5 days• Past daily generation and

washing machine use

• Idea: enables householders to reflect on behavior and plan future washing machine use

#1 Delayed Energy Feedback via Email: Findings

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• Users did not engage with energy reports, neither in a positive nor negative way

• Interpretation:

• the gulf between email and real family life is too large

• Planning of washing machine use is not something that is done on the computer

#2 Real-time Feedback via SMS Text Messages

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#2 Real-time Feedback via SMS Text Messages

• Participants received SMS a few minutes after washing machine use

• 'You ran your washing machine at 15:45 today (3.7% green). You could have achieved 43.6% by starting it at 10:34.‘

• 'Congratulations! You ran your washing machine at 13:48 today (65% green). The expected maximum for today was 71%.'

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#2 Real-time SMS Feedback: Findings

• “Just saying ‘your washing used 63 percent of solar’, that’s in itself is not really useful to us.”

• “unless you’re going to keep all these text message and analysethem, you are not going to get that information.”

• “It’s like shooting in the dark!”

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#3 Proactive Suggestions via SMS Text Messages

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#3 Proactive Suggestions via SMS Text Messages

• Participants received a SMS message at a time they had chosen. This message:

• Suggests best time of day to run washing machine during the next 36 hours

• This involved predicting solar energy generation for each hour of a day and uses past weather and generation data, and local weather forecast

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#3 Proactive Suggestions: Findings

• Very positive response from participants

• Some participants followed suggestions

• Even if participants did not follow the suggestions they appreciated that the information was there for them

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#3 Proactive Suggestions: Findings

• Huge diversity across households – where each family wanted to receive their proactive message at a different time

• Many requests for changes to mobile phone numbers for the messages, thus involving more members of the household

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#4 Embedded Control

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#4 Embedded Control

• Display and interactive control near the washing machine which was actually controlling the machine and receiving feedback (Zigbee)

• Shows best time to use washing machine

• User can select auto-start at best time

• User can select constraints for start and end time

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#4 Embedded Control: Findings

• Mostly positive reactions• Actionable information at

right time and right place

• Participants suggested many refinements:• Start time should

continuously adapt to current weather

• The system should pause the washing machine when a cloud passes

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#4 Embedded Control: Findings

• New laundry practices:

• load machine in the morning, set to auto-start, leave for work

• Appropriation:

• Participants used Information about best start time to manually control other appliance (dish washer)

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Conclusion

1. Technology support for demand-shifting is viable and effective

• Supporting emerging practices, not behavior change

2. Engagement and utility increased from

• decontextualized information -> embedded contextual control (i.e. email -> washing machine display)

• retroactive feedback -> proactive suggestions

3. Decisions about timing of washing machine use is negotiated through “conversations with my washing machine“

4. Future work: from one appliance to many appliances

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Conversation with my Washing Machine: An in-the-wild Study of Demand Shifting with

Self-generated Energy

Jacky Bourgeois, Janet van der Linden, Gerd Kortuem,

Blaine A. Price and Christopher Rimmer

In collaboration with