Transcript of Peter Xiang Gao, S. Keshav University of Waterloo.
- Slide 1
- Peter Xiang Gao, S. Keshav University of Waterloo
- Slide 2
- HVAC Energy use Buildings use 1/3 of all energy 30-50% of
building energy is for HVAC Can save energy by changing temperature
setpoint: 1 o C higher when cooling 10% saving 1 o C lower when
heating 2-3% saving
- Slide 3
- Focus of this work Consider a single office heating system in
winter Assume Thermal isolation Personal thermal control
(heater)
- Slide 4
- Personal Office Thermal Comfort Management Office Corridor
- Slide 5
- SPOT+: A Smart Personalized Office Thermal Control System
Occupancy Prediction Learning-Based Modeling 500W f () + 1 o C
-> Personal Thermal Comfort Evaluation Arrive officeLunch
Setpoint Scheduling
- Slide 6
- SPOT+: A Smart Personalized Office Thermal Control System
Occupancy Prediction Learning-Based Modeling 500W f () + 1 o C
-> Personal Thermal Comfort Evaluation Arrive officeLunch
Setpoint Scheduling
- Slide 7
- Predicted Mean Vote (PMV) model Air Temperature, Background
Radiation, Air Velocity, Humidity, Metabolic Rate, Clothing Level
ColdCoolSlightly CoolNeutralSlightly WarmWarmHot -3-20123 ASHRAE
Scale
- Slide 8
- SPOT [1] Clothing Sensing Microsoft Kinect: Detects occupancy
Detects location of the user 5 infrared sensor: Detects users
clothing surface temperature _______________________________ [1]
P.X. Gao, S. Keshav, SPOT: A Smart Personalized Ofce Thermal
Control System, e-Energy 2013 WeatherDuck: Senses other
environmental variables
- Slide 9
- Clothing level estimation Estimate clothing by measuring
emitted infrared More clothing => lower infrared reading Clo = k
* (t clothing t background ) + b t clothing is the infrared
measured from clothes on human body t background is the background
infrared radiation k and b are parameters to be estimated by
regression
- Slide 10
- Personalization PMV model represents the average for a single
office, only the occupants vote matters Predicted Personal Vote
(PPV) Model ppv = f ppv (pmv) where f ppv () is a linear
function
- Slide 11
- SPOT+: A Smart Personalized Office Thermal Control System
Occupancy Prediction Learning-Based Modeling 500W f () + 1 o C
-> Personal Thermal Comfort Evaluation Arrive officeLunch
Setpoint Scheduling
- Slide 12
- Learning-Based Model Predictive Control We model the thermal
characteristics of a room using LBMPC The model can predict future
temperature = f lbmpc (current temperature, heater power)
- Slide 13
- Learning-Based Model Predictive Control
- Slide 14
- Slide 15
- SPOT+: A Smart Personalized Office Thermal Control System
Occupancy Prediction Learning-Based Modeling 500W f () + 1 o C
-> Personal Thermal Comfort Evaluation Arrive officeLunch
Setpoint Scheduling
- Slide 16
- Occupancy Prediction We predict occupancy using historical
data. Match Previous similar history Predict using matched records
0.3 1 1 1.3 0 _______________________________ [1] James Scott et.
al., PreHeat: Controlling Home Heating With Occupancy Prediction,
UbiComp 2011
- Slide 17
- SPOT+: A Smart Personalized Office Thermal Control System
Occupancy Prediction Learning-Based Modeling 500W f () + 1 o C
-> Personal Thermal Comfort Evaluation Arrive officeLunch
Setpoint Scheduling
- Slide 18
- Optimal Control We use the optimal control strategy to schedule
the setpoint over a day. The control objective is to reduce energy
consumption and still maintain thermal comfort Overall energy
consumption in the optimization horizon S Weight of comfort, set to
large value to guarantee comfort first Predicted occupancy, we only
guarantee comfort when occupied. aka m(s) = 1 Thermal comfort
penalty. Both term equal zero when the user feels comfortable
- Slide 19
- Optimal Control - Constraints is the tolerance of predicted
personal vote (PPV) So when | ppv(x(s)) | is smaller than , there
is no penalty Otherwise, either c (s) or h (s) will be positive to
penalize the discomfort thermal environment
- Slide 20
- Evaluation
- Slide 21
- Evaluation of clothing level estimation Root mean square error
(RMSE) = 0.0918 Linear correlation = 0.9201
- Slide 22
- Predicted Personal Vote Estimation Root mean square error
(RMSE) = 0.5377 Linear correlation = 0.8182
- Slide 23
- Accuracy of LBMPC The RMSE over a day is 0.17C.
- Slide 24
- Accuracy of Occupancy Prediction The result of optimal
prediction is affected by occupancy prediction. False negative
10.4% (From 6am. to 8pm.) False positive 8.0% (From 6am. to 8pm.)
Still an open problem
- Slide 25
- Comparison of schemes
- Slide 26
- Limitations SPOT+ requires thermal Insulation for personal
thermal control Current SPOT+ costs about $1000 PPV requires some
initial calibration State of window/door is not modelled in the
current LBMPC Accuracy of clothing level estimation is affected by
Accuracy of Kinect Distance effect of the infrared sensor
- Slide 27
- Conclusion We extended PMV model for personalized thermal
control We design and implement SPOT+ We use LBMPC and optimal
control for personalized thermal control SPOT+ can accurately
maintain personal comfort despite environmental fluctuations allows
a worker to balance personal comfort with energy use.
- Slide 28
- Slide 29
- Relationship between PPV and Energy cost Maintaining a PPV of 0
consumes about 6 kWh electricity daily. By setting the target PPV
to -0.5, we can save about 3 kWh electricity per day.
- Slide 30
- Average Discomfort vs Energy Consumption