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Smart Buildings. Brian Cho, Hyungsul Kim CS598TAR - Green Computing . Why Do Buildings Matter?. Buildings are M ajor Energy Consumers. According to U.S. Department of Energy. Buildings Last a Long Time (Decades).

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smart buildings


Brian Cho, Hyungsul Kim

CS598TAR - Green Computing

buildings are m ajor energy consumers
Buildings are Major Energy Consumers
  • According to U.S. Department of Energy
buildings last a long time decades
Buildings Last a Long Time (Decades)
  • 20% of U.S. commercial floor space in use in 1995 was pre-WWII construction
  • Decisions today have a long-lasting impact on our future energy consumption
literature review
Literature Review

Oil Crisis!

  • A behavioral analysis of peaking in residential electrical energy consumers (1976)
  • Summary
    • They installed continuous data collection system in three homes
    • Combination of feedback and incentives led to 50% reduction in peak use, while removal of the experimental treatments resulted in a return to previous behaviors
    • They found that feedback was important in producing the behavioral changes
literature review1
Literature Review
  • Feedback as a means of decreasing residential energy consumption (1977)
  • Summary
    • Immediate feedback to homeowners about their daily rate of electricity usage resulted in 10.5% reduction
    • Writing during the last “energy crisis”, the introduction sounds remarkably similar to many papers today
    • “The world is in an energy crisis. Energy costs are increasing radpily and will continue to do so. Energy shortages have benn experienced; conservation techniques are needed.”
literature review2
  • The effect of goal-setting and daily electronic feedback on in-home energy use (1989)
  • Summary
    • Daily electronic feedback is much more effective than monthly feedback or self-monitoring (manually reading meters)
    • Daily electornic feedback resulted in 12.3% energy reduction and better than all of the other approaches
literature review3
Literature Review
  • What psychology knows about energy conservation (1992)
  • Summary
    • Information is more likely to change behavior when it is specific, vivid and personalized
    • Better delivery of messages can also lead to energy savings of 10-20%
    • Psychology has a special place in energy conservation because of its emphasis on the consumer’s point of view
literature review4
Literature Review
  • Reducing household energy consumption: a qualitative and quantitative field study (1999)
  • Summary
    • Computerized feedback helped reduce consumption most markedly
    • Consumers want customized or particularized advice
    • Computers have the potential to increase the visibility of fuel used within the home
literature review5
Literature Review
  • A review of intervention studies aimed at household energy conservation (2005)
  • Summary
    • Tailored information is much more useful than an overload of general information
    • Continuous feedback is beneficial(12% less electricity used over a control group)
    • “Many environmental problems, such as energy use, are related to human behavior, and, consequently, may be reduce through behavioral changes.”
literature review6
Literature Review
  • Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data (2006)
  • Summary
    • This study developed an online, interactive energy consumption information system and installed it in nine houses
    • They observed 9% reduction in power consumption, while monitoring only a subset of devices at 30 minute granularity
literature review7
Literature Review
  • The effect of tailored information, goal setting, and tailored feedback on household energy use, energy-related behaviors, and behavioral antecedents (2007)
  • Summary
    • An Internet-based tool was used to encourage households (N = 189) to reduce their energy use
    • A combination of tailored information, goal setting (5%), and tailored feedback was used
    • After 5 months, households exposed to the combination of interventions saved 5.1%, while households in the control group used 0.7% more energy
two main approaches
Two Main Approaches
  • Bottom-up approach
    • Make use of many different measurements at appliance level
    • Challenge: Difficult (expensive) to get full coverage at this level
  • Top-down approach
    • Make use of sensing instrument at the root of the power distribution network and use algorithms to increase visibility by disambiguating the aggregated load
direct plug load meters
Direct: Plug-load meters
  • Measure load of whatever is plugged in
  • Can load data to internet
  • Not cheap
  • Can’t measure electricload that doesn’t usestandard outlets(e.g. HVAC, boilers)

indirect associated sensors
Indirect Associated Sensors
  • Indirect sensors deployed and associated with specific appliance
  • Database of appliances is required

Specific appliance found in catalog and associated with sensor

C. Beckmann, S. Consolvo, and A. Lamarca. “Some assembly required: Supporting end-user sensor installation in domestic ubiquitous computing environments,” Ubiquitous Computing, 2004.

  • Indirect Sensing with autonomous sensor calibration
  • In absence of per-application current measurements
  • Indirect sensors:
    • Magnetic: standard deviation of magnetic field change
    • Acoustic, Light (e.g. refrigerator)
  • In-situ autonomous sensor calibration framework

Y. Kim , T. Schmid , Z. M. Charbiwala , M. B. Srivastava, “ViridiScope: design and implementation

of a fine grained power monitoring system for homes, “ Ubiquitous Computing, 2009.

mit plug
MIT Plug
  • Multimodal sensor networks in a power-strip form factor

J. Lifton , M. Feldmeier , Y. Ono , C. Lewis , J. A. Paradiso, “A platform for ubiquitous sensor deployment in occupational and domestic environments,” Information processing in sensor networks, 2007.

  • Measure energy consumption at the individual level
  • Activity monitoring + resource monitoring
    • Using activity monitoring can disambiguate which individual is using which resource

User activity monitoring using MicaZ mote

Y. Kim, Z. M. Charbiwala, A. Singhania, T. Schmid, and M. B. Srivastava.

“Spotlight: Personal natural resource consumption profiler.” HotEmNets, 2008




  • Thermostat/Dashboard + Power strips
  • ZigBee communication

energy load disaggregation
Energy Load Disaggregation
  • Disambiguating an aggregated load from the top down in order to give customers detailed information about how they're using power
  • It is also called Nonintrusive Appliance Load Monitoring
better signatures
Better Signatures
  • S.N. Patel, T. Robertson, J. A. Kientz, M. S. Reynolds, and G. D. Abowd. At the flick of a witch: Detecting and classifying unique electrical events on the residential power line
  • This study uses sensors with high sampling rates(100Mhz) to capture the electric noises when appliances turn on and off
  • Reducing building energy use is an important problem
  • Monitoring can show opportunities in energy savings
  • Many challenges in monitoring
    • We focused on residential monitoring
    • More on commercial buildings and their control in the next presentation…
  • Thanks!