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Just In Time Carbon Footprint

Just In Time Carbon Footprint. Manu Gupta, Stephen Intille and Kent Larson MIT Media Lab, House_n. manug@mit.edu intille@mit.edu kll@mit.edu. Carbon Footprint. Measurement & calculation of CO 2 emitted as part of daily life activities Why measure Conserve energy

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Just In Time Carbon Footprint

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  1. Just In TimeCarbon Footprint Manu Gupta, Stephen Intille and Kent Larson MIT Media Lab, House_n manug@mit.edu intille@mit.edu kll@mit.edu

  2. Carbon Footprint • Measurement & calculation of CO2 emitted as part of daily life activities • Why measure • Conserve energy • Allow people to be aware of their emission • Help people change usage patterns • Its our responsibility

  3. Our Focus • Behavior change Measuring and motivating energy conservation behavior at the personal level

  4. PlaceLab- A Live-In Laboratory • Data Collection • Only electric current information • 37 different electrical circuits fitted with current sensors • Circuits are divided into residential spaces and residential appliances

  5. PlaceLab- A Live-In Laboratory • Data Analysis • A Couple lived in the PlaceLab for 10 weeks • One day analysis • Annotation • Video analysis of usage, movement and behavior patterns • Current analysis • Of each individual appliance/device’s energy consumption

  6. Analysis Tool • Handlense sensor visualization tool • Developed at House_n Vampire power sources Unused lights & Devices

  7. Projected Usage Space in use Devices in use

  8. Results • Total electricity consumed in 1 day = 13.45 KWh • With an “optimal” change in behavior (but without impacting quality of life) • They would have saved ~ 38% (5KWh) of the total electricity consumed

  9. Behavior Change Strategies • To motivate energy conservation behavior two strategies can be used • Responsive Automation* • Just in Time Intervention* • *Current research is on designing these strategies and testing how • much energy can be saved using them.

  10. Behavioral Change Interfaces • Exploring the use of phones for just-in-time positive reinforcement. • Key idea: positive reinforcement tied to small behavior changes (Detected by sensing) • May lead to gradual behavior change.

  11. System Components • Sensor Network • Control and Actuation system • Mobile Devices • Intervention strategies

  12. System Characteristics • Low power • Wireless • Affordable • Real Time interaction with mobile devices • Non intrusive • Right mix of automation and intervention

  13. Thanks

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