1. Thesis Presentation by Peter Xiang Gao Supervised by Prof. S. Keshav

2. HVAC Energy Consumption HVAC: Heating Ventilation and Air-Conditioning 30% to 50% energy consumption in developed countries Save energy by changing temperature setpoint: 1 o C ≈ 10% saving

3. Problems Suppose we have a heating system in winter: How much can we reduce the setpoint? When can we reduce the setpoint?

4. 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

5. When can we reduce the setpoint? When occupied, reduce to the point that occupants still feel comfortable When vacant, turn it off Occupancy detection Occupancy prediction Thermal property modeling Setpoint scheduling

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

7. 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

8. 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 ColdCoolSlightly CoolNeutralSlightly WarmWarmHot -3-20123

9. 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 = f ppv (pmv) where f ppv () 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

10. Estimate Clothing Air TemperatureMeasure by sensor Background Infrared RadiationMeasure by sensor Air VelocityMeasure by sensor HumidityMeasure by sensor Metabolic RateConstant for indoor activity Clothing LevelUnknown Estimate clothing by measuring emitted infrared More clothing, lower infrared reading Clothing = k * (t clothing – t background ) + b k and b are parameters to be estimated by regression method t clothing is the infrared measured from human body t background is the background infrared radiation

11. SPOT Clothing Sensing Microsoft Kinect: Detects occupancy Detects location of the user 5° infrared sensor: Detects users’ clothing surface temperature Servos: Controls the direction of the 5° infrared sensor 90° infrared sensor: Detects background radiant temperature Weatherduck sensor: Detects air temperature, humidity, air velocity Microcontroller: Pull data from the sensors Control the rotation angle of the servos

12. 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

13. 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)

14. Learning-Based Model Predictive Control

16. 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

17. Occupancy Prediction We predict occupancy using historical data. Match Previous similar history Predict using matched records 0.3 1 1 1.3 0

18. 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

19. 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

20. 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 β c (s) or β h (s) will be positive to penalize the discomfort thermal environment

21. 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

22. Evaluation of clothing level estimation Root mean square error (RMSE) = 0.0918 Linear correlation = 0.9201

23. Predicted Mean Vote Estimation Root mean square error (RMSE) = 0.5377 Linear correlation = 0.8182

24. Accuracy of LBMPC The RMSE over a day is 0.1507C.

25. 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.

26. Reactive Control and Optimal Control Using reactive control: SPOT starts to heat when occupancy is detected Save more energy, less comfortable Average Power 261.8W Using optimal control: SPOT starts to heat ~1 hours before the predicted arrival time Save less energy, more comfortable Average power 294W Scheduled control (9am – 5pm) Average power 460W

27. 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

28. Related Work Nest Learning Thermostat learns occupancy pattern to save energy

29. 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

30. 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