Engineering Systems Division: The Energy Box

1. Engineering Systems Division: The Energy Box Integrating Energy Efficiency and Consumer Behavior into the Energy Box Design by Kat Donnelly, PE June 3, 2008 kdonnell@mit.edu Presentation for the House-n event: Energy Behavior Change + Low-Energy Homes

2. Energy Efficiency (1970’s) Dan Yergin & Amory Lovins • With serious commitment • Could consume 40% less • Quality energy source • Stimulates employment, innovation, environment, economy • cheapest, safest, most productive alternative • readily available in large amounts Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Kat Donnelly, PE; kdonnell@mit.edu; May 20, 2008 Slide 2 Slide 2

3. Energy Efficiency (1970’s) Dan Yergin & Amory Lovins Does not: • threaten international monetary system • emit [much] carbon dioxide • generate waste problems like nuclear • cause Geopolitical issues… http://www.oiladdict.com/icons/bookcover_home.jpg Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 3

4. States Adopting Residential Building Standards (EPA 2008) States Adopting Energy Efficiency Portfolio Standards (EPA 2008) Yet EE is lagging. Why? Policy http://www.epa.gov/solar/energy-programs/state-and-local/efficiency_actions.html Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 4

5. Yet EE is lagging. Why? Market Economics http://www.treehugger.com/cars-piled-up.jpg • Externalities (oil priced well below social costs) $15 to $50+/bbl • Military/security diplomatic/geopolitical • Climate • Other environmental • Oil subsidies • Bad price signals • Little real/time, time-of-use pricing • Little monitoring/feedback to customer Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008

6. Traditional Economics Assumptions • Agents: • Are rational, controlled, informed, selfish, and calculating; • Preferences are known, stable, and ordered; • Maximize their welfare • Market conditions optimal http://www.carbon-cutters.com/Energy_Security.htm Slide 6 Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008

7. Yet, EE is lagging. Why? Behavioral Economics http://www.inhabitat.com/wp-content/uploads/_dl09.jpg • People do not maximize welfare • Follow suboptimal decision strategies • Have very high implicit discount rate (>/ 60%/year) high time value of money, short-term view! • Information asymmetries--lack good information on end-use efficiency alternatives Slide 7 Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008

8. Diminishing Returns Psychological Value Losses are steeper $ Do people know their preferences? • Initial and relative positions/situations • Affects perception of possible outcomes • Inhibits rational energy consumption • Psychological value function. People are: • Risk adverse for potential gains • Risk seeking for potential losses • Which means, • Emphasize losses in marketing campaigns • Use strategies with biggest impacts • Reference Points Matter (set them effectively) Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008

9. Effective EE Strategies: Goal Setting and Commitment Increases motivation, commitment, direction Increases adoption of sustainable behaviors See progress & progress needed Develop new strategies Assess goals Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008

10. Total Goal ($X.XX, X.XX kWh) Resets Daily or Monthly Pace: 80% Pace: 95% Pace: 105% Pace: 120% Projection: 80% of goal Projection: 95% of goal Projection: 105% of goal Projection: 120% of goal Commitment&Goal Setting $140.00 1000.000 kWh (Cumulative Counter) Halfway through time period (daily/monthly), electricity pace should be half of the goal Example: expected electricity use as of May 14 relative to May goal Counter Real-time Consumption Gauge (kW) Pace: percent relative to expected electricity use Projection: expected end-of-month electricity use given current pace Zero Goal Tracker (Resets Daily or Monthly) Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 10

11. Effective EE Policy: Feedback http://i181.photobucket.com/albums/x176/weirdscience_photos/powercord_glow.jpg 1. Learning – better understand how behavior influences energy usage 2. Forming habits – new knowledge alters activities and causes a routine change 3. Internalization of behavior – new habits change attitudes to suit new behaviors. Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 11

12. Use Technology ToDrive Behaviors http://fivepercent.us/images/electricity-use-sm.png Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 • Rational decision making shaped by 1. Abundance of raw data 2. Dearth of meaningful information • Behavior and technology are closely interwoven throughout life • Role of technology receives surprisingly modest attention in psychology and in policy making. (2007, Midden, C.) Slide 12

13. The Energy Box: What Is It? Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 • Home computer running specially-developed software especially intended to achieve peak load shaving • controlling a combination of hardware that makes it possible to turn appliances on or off according to supply conditions • Users can download and install (or write their own!) • software onto their home computer • different algorithms for different appliances • Future migration to a Web-browser interface • Can also be a stand-alone display in the house Slide 13

14. Address weakness in Consumer Behavior literature Achieve energy efficiency behavior change Developing an Energy Box:A Systems Solution to Energy Efficiency ESD 2007

15. Comfort Efficient Adaptive The Energy Box: Controlling an Intelligent Building Safe Convenient Reliable Beyond Smart Metering…Behavioral aspects • Consider consumer comfort and preferences • Change attitudes (environmental, economic, geopolitical) • Modify consumption patterns • Take advantage of social marketing From: Filipe Rodrigues, IST, MIT-Portugal Program Slide 15 Kat Donnelly, PE; kdonnell@mit.edu; May 20, 2008

16. Communication with the Consumer (Supply, Personal consumption & characteristics) The Enabling Technology:Meter Prototype Design Source: MIT Design Contest, RPI Electronics Club Team Submittal Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 16

17. Upgrading the Technology: Processing on-board Source: Dane Kouttron, Project Update May 27, 2008 Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 17

18. Upgrading the Technology: Disaggregating the signal Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Source: Dane Kouttron, Project Update May 27, 2008 Slide 18

19. Future Study: Consumer Behavior Surveys (forthcoming) Consumer Behavior Surveys: Learn the attitude/behavior/intention relationship • Demographic variables, dwelling and energy use characteristics • Energy price and usage knowledge • Attitudes toward energy scarcity, energy conservation, energy efficiency, sustainability, mandatory or voluntary conservation • What kinds of things can people do to save energy? http://www.concord.org/~btinker/guide/footprint/index.html Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 19

20. Future Study: Consumer Behavior Experiments (forthcoming) Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 20

21. Future Study: Consumer Behavior Experiments (forthcoming) Social networking Establish personal “sustainability identification” Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 21

22. My Research: Expected Contribution • Low-hanging fruit • Achieve >10% reduction in home energy use for pilot group • Develop detailed user interface to influence consumer behavior • detailed application of behavioral principles to energy conservation • Barrier busting of current information asymmetries • Develop supportive pricing and technology policy recommendations Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008 Slide 22

23. References • Ariely, Dan. (Fall 2007). Consumer Behavior Class Lectures and Predictably Irrational (2008). • Bagozzi, R. P. (1999). Utpal Dholakia, Goal Setting and Goal Striving in Consumer Behavior. Journal of Marketing, Vol. 63, Fundamental Issues and Directions for Marketing, 19-32. • Bazerman, M. H. (1992). & Neale, M.A., Introduction to Rational Thinking in Negotiation, Part 1: Common Mistakes in Negotiation. In Negotiating Rationally (pp. 1-64). New York: The Free Press. • Darby, Sarah. (2006). "The Effectiveness of Feedback on Energy Consumption: A review for DEFRA of the Literature on Metering, Billing, and Direct Displays." Environmental Change Institute, University of Oxford. • Diamond, P. (Ed.). (2007). & Vartiainen, Hannu, Behavioral economics and its applications, Princeton, N.J: Princeton University Press. • Ester, P. (1985). Consumer Behavior and Energy Conservation. Dordrecht, The Netherlands, Martinus Nijhoff Publishers. • Houwelingen, J. H. v. (1989). W. Fred van Raaij, The Effect of Goal-Setting and Daily Electronic Feedback on In-Home Energy Use. The Journal of Consumer Research, 16(1), 98-105. • Katzev, R., and Johnson, Theodore (1987). Promoting Energy Conservation: An Analysis of Behavioral Research. Boulder, CO, Westview Press. • Kelly, S. (2006). Customer Intelligence: From Data to Dialogue. Chichester, West Sussex, England: John Wiley and Sons. • Lovins, A. (2007). Energy Efficiency: Concepts and Practices: Implementation Lecture, March 29, 2007, Stanford University. (from iTunesU) • Midden, C. J. H. (2007). Florian G. Kaiser, and L. Teddy McCalley, Technology’s Four Roles in Understanding Individuals’ Conservation of Natural Resources, Eindhoven University of Technology. Journal of Social Issues, 63(1), 155-174. • Sheehy, L. (2003). & Dingle, P., Goal Setting, Education, and Sustainability: Living Smart in the City of Fremantle: University of Australia. • Yergin, D. “Conservation: The key energy source”, In: R. Stobaugh & D. Yergin (Eds.) Energy future: Report of the Energy Project at the Harvard Business School. New York: Random House, 1979 Kat Donnelly, PE; kdonnell@mit.edu; June 3, 2008

24. Policy and Behavior Interventions Kat Donnelly, PE; kdonnell@mit.edu; May 20, 2008 Slide 4