IERE Qiu 2016 · 2016 IERE – CLP-RI Hong Kong Workshop November 22, 2016, Hong Kong Junxian Yang...
Transcript of IERE Qiu 2016 · 2016 IERE – CLP-RI Hong Kong Workshop November 22, 2016, Hong Kong Junxian Yang...
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HKUST 香港科技大學
Multimodal Sensing for Thermal Comfort and Energy Saving in Smart Buildings
2016 IERE – CLP-RI Hong Kong WorkshopNovember 22, 2016, Hong Kong
Junxian Yang and Huihe Qiu
Department of Mechanical and Aerospace EngineeringHong Kong University of Science and Technology
Hong Kong
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Contents
● Smart Buildings
● Thermal Comfort Control in Smart Buildings
● Multimodal Thermal Comfort Sensing Techniques
● Experimental Results
● Conclusion
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Smart Evolution---Smart World
Courtesy BSRIA 2015
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IoT Based Thermal Comfort in Smart Buildings
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How to Define Thermal Comfort
www.dantecdynamics.com
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Fanger’s Equation
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Wireless Sensor Networks for PMV Control
Temperature
Humidity
Air Velocity
Mean RadianTemperature
Metabolic Rate
Clo Value
Micro Controller
Wireless Transmittter
Wireless Receiver
Co-ordinatorGUI
PC
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Challenges
According to a sensitivity analysis, the most influencing variables are:
Clothing values
Metabolic rates
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How to Measure Metabolic Rate for Thermal Comfort Control?
OR
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Thermal Balance and Thermoregulation
Hands have a big temperature difference between cold and hot environment. This feature apparently indicates the heat loss and microcirculation of hands can be utilized to estimate the metabolic rate and evaluate the thermal sensation.
Microcirculation
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Aspirating system for heat loss measurement
Relative humidity: , is the saturated vapor pressure at T.
. . ∗.
Air volume is V, vapor mass is , dry air mass is , then
, ∗∗,
is dry air pressure, is vapor pressure,Bis standard atmospheric pressure.is gas constant of dry air, isgas constant of vapor.
Calibration of Microenvironment
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Aspirating system for heat loss measurement
The enthalpy of air is defined as the sum of the enthalpy of 1g dry air and that of g of vapor. For enthalpy calculation, set dry air enthalpy and water enthalpy are 0 at 0 .
Air enthalpy: ∗ ⁄Dry air enthalpy: ∗ ⁄ , ∗⁄ is dry air specific heat at 1 atm;Vapor enthalpy: ∗ ⁄ , ∗⁄ is vapor specific heat at 1 atm;
/ is latent heat of vaporization at 0 .
∗ ∗ ∗ ∗ ⁄
⁄ , is the vapor mass, is the dry air mass.
Heat Loss Measurement
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Air-in temperature: Air-out temperature: Air-in relative humidity: Air-out relative humidity: Cover area : Skin surface area :
. . .
Air volume flow rate: ⁄ : Air velocity: /
Dry air mass flow rate: ⁄Air-in enthalpy: ⁄
Air-out enthalpy: ⁄ Heat loss: /
∗
HL
Aspirating system for heat loss measurement
Heat Loss Measurement
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All energy-releasing reactions in the body ultimately depend on oxygen use.
Compared with direct calorimetry, indirect calorimetry remains simple and less expensive
However, still high cost and inconvenient for individuals’ daily use
Metabolic Measurement: Indirect calorimetry
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Metabolic Rate Measurement
SV and blood flow rate (oxygen supply) have an impact on skin impedance (microcirculation, vasoconstriction and vasodilation)V : Oxygen consumption rate (L/s)HR: Heart rate (bmp)SV: Stroke volume (L), the amount of blood ejected with each contraction
: Difference between the oxygen content of arterial and mixed-venous blood (ml/dl)Z : Skin impedance (Ohm)
The TE depends on the type of metabolism that is indicated by the RQ. In the determination of the metabolic rate, a mean RQ of 0.85 is used and . is equal to 20.36 kJ/L. The maximum possible error is 3.5%, but generally the error will not exceed 1%.
, . .
∝ ∝ | |
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Experimental Setup
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Experiments and Results Temperature: 21, 25, 29 Relative Humidity: 50%
0 200 400 600 800 10001
2
3
4
5
Hea
t Los
s &
Met
abol
ic R
ate
(kca
l/min
)
Time (s)
HL @ 21 degC MR @ 21 degC
0 200 400 600 800 10001
2
3
4
5
Hea
t Los
s &
Met
abol
ic R
ate
(kca
l/min
)
Time (s)
HL @ 25 degC MR @ 25 degC
0 200 400 600 800 1000
1
2
3
4
5
Hea
t Los
s &
Met
abol
ic R
ate
(kca
l/min
)
Time (s)
HL @ 29 degC MR @ 29 degC
1 2 31 2 3
1 2 3
21 25 29
Temperature ( ) 21 25 29
Phase 1 2 3 1 2 3 1 2 3
Voted PMV -1 1 0 0 1 0 1 3 2
Calculated PMV -1.8 1.0 -1.5 0 2.0 0 1.2 2.6 1.2
Comfort Temperature ( ) 26.2 15.7 25.8 25.1 14.7 25.3 25.4 16.1 24.9
Table 1 Voted and calculated PMV of one subject at the chamber temperature of 21, 25 and 29
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2426
Apply to Thermal Comfort Control and Energy Saving
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Utilizing human’s thermal sensation for thermal comfortcontrol and energy saving in Smart Buildings has beendeveloped.
A novel approach using heart rate and skin impedance ratiohas been developed for predicting human’s metabolic ratefor thermal comfort evaluation in free-living conditions
It is possible to predict thermal comfort for PMV basedcontrol and energy saving in buildings
A personalized comfort sensor networks with a wirelesscommunication system can be facilitated with IoT.
Conclusion
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Thank You!The Hong Kong University of Science and Technology