peter xiang gao, s. keshav university of waterloo
TRANSCRIPT
- Slide 1
- Peter Xiang Gao, S. Keshav University of Waterloo
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- 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
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- Focus of this work Consider a single office heating system in winter Assume Thermal isolation Personal thermal control (heater)
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- Personal Office Thermal Comfort Management Office Corridor
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- 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
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- 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
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- Predicted Mean Vote (PMV) model Air Temperature, Background Radiation, Air Velocity, Humidity, Metabolic Rate, Clothing Level ColdCoolSlightly CoolNeutralSlightly WarmWarmHot -3-20123 ASHRAE Scale
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- 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
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- 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
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- 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
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- 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
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- 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)
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- Learning-Based Model Predictive Control
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- 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
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- 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
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- 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
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- 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
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- 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
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- Evaluation
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- Evaluation of clothing level estimation Root mean square error (RMSE) = 0.0918 Linear correlation = 0.9201
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- Predicted Personal Vote Estimation Root mean square error (RMSE) = 0.5377 Linear correlation = 0.8182
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- Accuracy of LBMPC The RMSE over a day is 0.17C.
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- 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
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- Comparison of schemes
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- 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
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- 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.
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- 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.
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- Average Discomfort vs Energy Consumption