Thesis Presentation by Peter Xiang Gao
Supervised by Prof. S. Keshav
HVAC Energy Consumption
 HVAC: Heating Ventilation and
Air-Conditioning
 30% to 50% energy
consumption in developed
countries
 Save energy by changing temperature setpoint:
 1oC ≈ 10% saving
Problems
Suppose we have a heating system in winter:
 How much can we reduce the setpoint?
 When can we reduce the setpoint?
How much
can we reduce the setpoint?
 Thermal Comfort
 Save energy while keeping people feel comfortable
 Need to evaluate thermal comfort
 Personal Thermal Comfort
 Only the occupants’ thermal comfort matter
 Need to evaluate personal thermal comfort
When
can we reduce the setpoint?
 When occupied, reduce to the point that occupants
 When vacant, turn it off
 Occupancy detection
 Occupancy prediction
 Thermal property modeling
 Setpoint scheduling
Temperature
still feel comfortable
26
25
24
23
22
21
20
Occupancy
Temperature
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
SPOT: A Smart Personalized
Office Thermal Control System
Personal Thermal Comfort Evaluation
Learning-Based Modeling
500W
Arrive office
Lunch
Leave office
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Occupancy Prediction
Temperature
Occupancy
->
f (•)
->
+ 1 oC
26
24
22
20
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Setpoint Scheduling
SPOT: A Smart Personalized
Office Thermal Control System
Personal Thermal Comfort Evaluation
Learning-Based Modeling
500W
Arrive office
Lunch
Leave office
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Occupancy Prediction
Temperature
Occupancy
->
f (•)
->
+ 1 oC
26
24
22
20
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Setpoint Scheduling
Predicted Mean Vote (PMV) model
 Six input parameters
 Air Temperature, Background Radiation, Air Velocity,
Humidity, Metabolic Rate, Clothing Level
 Developed by P.O. Fanger in 1970, widely used for
thermal comfort evaluation, standardized by ISO
 Seven scale output
Cold
Cool
Slightly Cool
Neutral
Slightly Warm
Warm
Hot
-3
-2
-1
0
1
2
3
Predicted Personal Vote (PPV) model
 PMV model only represent the group average
 In office environment, only the occupant’s vote cares
 Predicted Personal Vote (PPV) Model
ppv = fppv (pmv)
where fppv(•) is a linear function
 Occupant first gives votes in the training phase
 SPOT learns the user’s thermal preference and control
temperature on behalf of the user
Estimate Clothing
Air Temperature
Measure by sensor
Background Infrared Radiation
Measure by sensor
Air Velocity
Measure by sensor
Humidity
Measure by sensor
Metabolic Rate
Constant for indoor activity
Clothing Level
Unknown
 Estimate clothing by measuring emitted infrared
 More clothing, lower infrared reading
Clothing = k * (tclothing – tbackground) + b
 k and b are parameters to be estimated by regression method
 tclothing is the infrared measured from human body
 tbackground is the background infrared radiation
SPOT Clothing Sensing
Servos:
•
5° infrared sensor:
•
Detects users’
clothing surface
temperature
Controls the direction of
the 5° infrared sensor
90° infrared sensor:
•
Microsoft Kinect:
•
•
Detects occupancy
Detects location of
the user
Detects background
radiant temperature
Microcontroller:
•
•
Pull data from the sensors
Control the rotation angle of
the servos
Weatherduck sensor:
•
Detects air temperature,
humidity, air velocity
SPOT: A Smart Personalized
Office Thermal Control System
Personal Thermal Comfort Evaluation
Learning-Based Modeling
500W
Arrive office
Lunch
Leave office
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Occupancy Prediction
Temperature
Occupancy
->
f (•)
->
+ 1 oC
26
24
22
20
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Setpoint Scheduling
Learning-Based
Model Predictive Control
 Learning-Based Predictive Control (LBMPC) can
predict the control output given the control input
 We model the thermal characteristics of a room using
LBMPC
 The model can predict
future temperature = f lbmpc (current temperature, heater power)
Learning-Based
Model Predictive Control
Consider heating in winter
 Q: Thermal Energy of a Room
 Tin: Indoor room temperature
 Tout: Outdoor temperature
 k: Conduction factor of the room
By Newton’s Law of Cooling:
𝑃𝑙𝑜𝑠𝑠 = 𝑘 (𝑇𝑖𝑛 − 𝑇𝑜𝑢𝑡 )
The net thermal input rate is:
𝑃 = 𝑒𝑃ℎ𝑣𝑎𝑐 − 𝑃𝑙𝑜𝑠𝑠 = 𝑒𝑃ℎ𝑣𝑎𝑐 − 𝑘 (𝑇𝑖𝑛 − 𝑇𝑜𝑢𝑡 )
where e is the heater efficiency and Phvac is the heater power
Learning-Based
Model Predictive Control
The thermal input rate P is the differentiation of the
room’s thermal energy, which is proportional to
temperature change:
𝑑𝑄
𝑑𝑇𝑖𝑛
𝑃=
=𝐶
𝑑𝑡
𝑑𝑡
where C is the heat capacity of the room
Combine the two equations:
𝑑𝑇𝑖𝑛
𝑒𝑃ℎ𝑣𝑎𝑐 − 𝑘 𝑇𝑖𝑛 − 𝑇𝑜𝑢𝑡 = 𝑃 = 𝐶
𝑑𝑡
and we can rewrite the equation as:
𝑑𝑇𝑖𝑛
𝑑𝑡
=
𝑒𝑃ℎ𝑣𝑎𝑐 −𝑘 𝑇𝑖𝑛 −𝑇𝑜𝑢𝑡
𝐶
SPOT: A Smart Personalized
Office Thermal Control System
Personal Thermal Comfort Evaluation
Learning-Based Modeling
500W
Arrive office
Lunch
Leave office
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Occupancy Prediction
Temperature
Occupancy
->
f (•)
->
+ 1 oC
26
24
22
20
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Setpoint Scheduling
Occupancy Prediction
 We predict occupancy using historical data.
0 .3 1 1 1 .3 0
Match Previous
similar history
Predict using
matched records
SPOT: A Smart Personalized
Office Thermal Control System
Personal Thermal Comfort Evaluation
Learning-Based Modeling
500W
Arrive office
Lunch
Leave office
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Occupancy Prediction
Temperature
Occupancy
->
f (•)
->
+ 1 oC
26
24
22
20
6 7 8 9 10 11 12 13 14 15 16 17 18
Time of a day
Setpoint Scheduling
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 0 when the
user feels
comfortable
Optimal Control - Constraints
 ε is the tolerance of predicted personal vote (PPV)
 So when | ppv(x(s)) | is smaller than ε, there is no
penalty
 Otherwise, either βc(s) or βh(s) will be positive to penalize
the discomfort thermal environment
Discretization
 ppv(•) is not a convex function, we discretize the
problem by converting it into a shortest path problem
 Calculate the all possible state at each time step
 Assign a cost for each state
 The best schedule is the states on the shortest path
Evaluation of clothing level estimation
 Root mean square error (RMSE) = 0.0918
 Linear correlation = 0.9201
Predicted Mean Vote Estimation
 Root mean square error (RMSE) = 0.5377
 Linear correlation = 0.8182
Accuracy of LBMPC
 The RMSE over a day is 0.1507C.
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.
Reactive Control and Optimal Control
1
Occupancy
PPV
0
PPV
Using reactive control:
• SPOT starts to heat
when occupancy is
detected
• Save more energy, less
comfortable
• Average Power 261.8W
-1
-2
-3
6
7
8
9
10
11
Time of a day
12
13
14
13
14
1
Occupancy
0
PPV
Using optimal control:
• SPOT starts to heat ~1
hours before the
predicted arrival time
• Save less energy, more
comfortable
• Average power 294W
PPV
-1
-2
Scheduled control (9am – 5pm) -3
6
• Average power 460W
7
8
9
10
11
Time of a day
12
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
Related Work
 Nest Learning Thermostat learns occupancy pattern to
save energy
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
Conclusion
 We extended PMV model for personalized thermal
control
 We design and implemented SPOT and find that
SPOT can accurately maintain personal thermal
comfort
 We use LBMPC and optimal control for personalized
thermal control