Demand Shifting Study with Solar Homes
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Best paper nominee, presented at Ubicomp 2014 Authors: Jacky Bourgeois, Janet van der Linden, Gerd Kortuem, Blaine A. Price and Christopher Rimmer Contact: jacky.bourgeois AT open.ac.uk Abstract: Domestic microgeneration is the onsite generation of low- and zero-carbon heat and electricity by private households to meet their own needs. In this paper we explore how an everyday household routine – that of doing laundry – can be augmented by digital technologies to help households with photovoltaic solar energy generation to make better use of self-generated energy. This paper presents an 8-month in-the-wild study that involved 18 UK households in longitudinal energy data collection, prototype deployment and participatory data analysis. Through a series of technology interventions mixing energy feedback, proactive suggestions and direct control the study uncovered opportunities, potential rewards and barriers for families to shift energy consuming household activities and highlights how digital technology can act as mediator between household laundry routines and energy demand-shifting behaviors. Finally, the study provides insights into how a “smart” energy-aware washing machine shapes organization of domestic life and how people “communicate” with their washing machine.
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Demand Shifting Study with Solar Homes
- 1. 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 1 In collaboration with
- 2. 2 Electricity generation with solar panels alters people’s relationship with energy “Energy farmers”
- 3. 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 3 Solar Photovoltaic (PV) Generation Export To the grid Import From the grid Self-consumption
- 4. 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 4 Electricity Profile of household #12 on 7 May 2013 (Consumption vs Generation)
- 5. 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 5
- 6. What role can Ubicomp technology play in enabling or supporting new energy practices in households with solar generation? Specifically: demand shifting 6
- 7. Case Study: Doing Laundry with Washing Machine 7 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
- 8. “In-the-Wild” Study with Households Objective • Understand household practices • Explore design alternatives for in-home technology Scope • 8 Months • 18 households • 64 participants 8
- 9. Study Methodology • Home instrumentation • Participatory energy data analysis • Design and deployment of technology interventions • Qualitative studies: • Home visits • Interviews & focus groups • Thematic analysis 9
- 10. 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) 10
- 11. Fixing technology installation presented an opportunity for qualitative data gathering
- 13. #1 Delayed Energy Feedback via Email 13
- 14. #1 Delayed Energy Feedback via Email 14 • 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
- 15. #1 Delayed Energy Feedback via Email: Findings 15 • 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
- 16. #2 Real-time Feedback via SMS Text Messages 16
- 17. #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%.' 17
- 18. #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 analyse them, you are not going to get that information.” • “It’s like shooting in the dark!” 18
- 19. #3 Proactive Suggestions via SMS Text Messages 19
- 20. #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 20
- 21. #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 21
- 22. #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 22
- 23. #4 Embedded Control 23
- 24. #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 24
- 25. #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 25
- 26. #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) 26
- 27. 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 27
- 28. 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
Editor's Notes
- Good Afternoon everyone I m Jacky, Phd student at both the Open University in the UK and Université de rennes 1 in France And I’m going to present you a study we’ve done with Janet van der Linden, Gerd Kortuem, Blaine Price and Christopher Rimmer in collaboration with eon, a major energy company.
- Increasingly people have solar panels on their roof and are effectively producers of electricity. Or electricity farmers. Also, there is evidence that people with such solar panels think differently about energy.
- However
- … And that peak of consumption is mostly made of interactive consumption, appliances that requires user interventions.
- So far Most ubicomp and HCI, energy research has focused on consumption and demand reduction Only few have looked at local generation, yet there are opportunities for supporting emerging practices Previous research has by and large assumed that by making people more aware they can change their behaviour. Whereas in this research we want to give people active tools to help them achieve these new practices. Keirstead observed what he called the Double Dividend in the context of local generation. This means that people not only produce green energy but also reduce what they are consuming. We also found in an earlier study that householders are creating simple manual ways, such as looking out of the window, to estimate weather impact because they want to shift their demand.
- we chose to focus on one appliance – the washing machine. It is a good example of an interactive appliance that has a central place in the household and the household routines. Doing the laundry is a household activity that has many subtle routines and habits and that vary highly between households.
- In that study, our objective was to… We conducted this study over 8 months, with 18 participating households around Milton Keynes in the UK All had solar panels on their roof for at least a year The households were quite different, from 2 to 5 members, stay at home versus going to work, with and without children, retired etc
- We monitored electricity in these houses, we analysed the data with participants Then we designed and deployed technology interventions based on emails, text messages and electronic tablets We collected our qualitative data through home visits, interviews and focus group and we performed a thematic analysis
- What did we monitor: LOTS of DATA!! We knew everything about these people. Minute by minute. The generation coming from solar panels The import from and export to the grid Data from a specially constructed washing machine, using zigbee for communication and control. We knew every wash they did, what time, which type of cycle etc And we also monitor other appliances through a variety of smart plugs
- We had scheduled interviews at regular intervals, but also we had lot of contact with the householders in-between for help and technical assistance. Many home visits were made There were thus many opportunities to gather additional and informal qualitative data.
- So here is our intervention plan in 4 stages around different time and place In the remaining of this presentation, I will detail each of these interventions
- First, we explored delayed energy feedback via email
- What was very interesting is that people did not comment at all about these emails. We think that this is because people don’t read their email as when they are doing their laundry. Often in a separate room and at a very different time.
- Then we moved onto real time feedback via text messages
- So for our second intervention – we asked for participants phone numbers so that we could send them a message straight after each wash was finished. The message would tell them how well they’d done. If they’d done well they were congratulated – but otherwise they might get a suggestion for what would have been a better time.
- By and large people enjoyed receiving the text messages. A number of people said they enjoyed getting a message from their washing machine. It was funny and unusual to have a conversation with your washing machine However, they felt that the message was coming too late. People were not able to change anything. Particularly if they had a poor score there was no opportunity to improve it as it was after the fact. Ironically when we stopped sending these messages, people kept asking to have them back.
- Then we moved onto a more proactive suggestion, BEFORE the washing machine load
- When we asked participants when they’d like to receive these messages, we got a huge diversity of answer across households For example, I want it at 7 in the morning because then it fits in when we’re getting ready for work and school. I want it the night before – say around 8 in the evening I want it every day. Only twice a week We also got many requests for changes to mobile phone number, as we were not sending messages to the right person in the household. They wanted to make sure the message would go to the persons who were mostly involved with doing the laundry, often not the same person who is mostly concerned with the household energy concerns. So this meant more people were becoming involved in participating in the study and in thinking about the tools needed to support them.
- Finally, we went onto embedded control
- We set up an electronic tablet near the washing machine Showing the best time to use the washing machine The user can select auto-start at best time and select constraints for start and end time Then the application was actually controlling the washing machine and receiving feedback
- We also observed the emergence of new laundry practices. People would say ”I load my machine in the morning, set to auto-start, then leave for work“ We also noticed that people appropriated the tool for their own purposes. So they would use the information for the washing time to manually switch on other high consumption appliances. For example, manually switch on the dishwasher. Even the hot tub!