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Smart Buildings Brian Cho, Hyungsul Kim CS598TAR - Green Computing
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Smart Buildings

Feb 25, 2016

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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). - PowerPoint PPT Presentation
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Page 1: Smart Buildings

Smart Buildings

Brian Cho, Hyungsul KimCS598TAR - Green Computing

Page 2: Smart Buildings

Why Do Buildings Matter?

Page 3: Smart Buildings

Buildings are Major Energy Consumers

• According to U.S. Department of Energy

Page 4: Smart Buildings

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

Page 5: Smart Buildings

Monitoring energy in buildings can lead to energy savings

Page 6: Smart Buildings

Literature Review

• 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

Oil Crisis!

Page 7: Smart Buildings

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.”

Page 8: Smart Buildings

Literature Review

• 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

Page 9: Smart Buildings

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

Page 10: Smart Buildings

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

Page 11: Smart Buildings

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.”

Page 12: Smart Buildings

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

Page 13: Smart Buildings

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

Page 14: Smart Buildings

The Message in Summary

MonitoringFine-

granularity, Appliance Level

FeedbackAppliance

Level, Vivid, Personalized

UserBehavior Changes

Energy Savings

Page 15: Smart Buildings

How to monitor the energy usage in buildings?

Page 16: Smart Buildings

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

Page 17: Smart Buildings

Bottom-up approaches

Page 18: Smart Buildings

Direct: Plug-load meters

• Measure load of whatever is plugged in• Can load data to internet• Not cheap• Can’t measure electric

load that doesn’t usestandard outlets(e.g. HVAC, boilers)

http://www.wattsupmeters.com/secure/products.php?pn=0

Page 19: Smart Buildings

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.

Page 20: Smart Buildings

Viridiscope

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

Page 21: Smart Buildings

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.

Page 22: Smart Buildings

Spotlight

• 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

Page 23: Smart Buildings

Startups

Tendril EnergyHub

• Thermostat/Dashboard + Power strips• ZigBee communication

http://www.tendrilinc.com/products/http://www.energyhub.com/forhome/

Page 24: Smart Buildings

Top-down approaches

Page 25: Smart Buildings

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

Page 26: Smart Buildings

Signatures in Aggregated Loads

Page 27: Smart Buildings

Appliance Signatures

Page 28: Smart Buildings

Signature Space

Page 29: Smart Buildings

Appliance Models

Page 30: Smart Buildings

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

Page 31: Smart Buildings

Conclusion

• 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!

Page 32: Smart Buildings

Backup slides

Page 33: Smart Buildings

U.S. residential Electricity Consumption by End Use, 2008

Page 34: Smart Buildings

2006 U.S. Buildings Energy End-Use Splits