Abstract
This work proposes a method for developing an accurate correlation to predict thermal comfort (TC) as function of occupant physiological and environmental parameters. This method is implemented for a space that relies on hybrid natural ventilation (NV) and personalized ventilation (PV) cooling. Multivariable linear regression was adopted to develop the TC correlation while retaining variables based on the significance and interdependency. The correlation was found to be dependent on indoor temperature (Tindoor), relative humidity (RH), facial temperature (Tfacial) and its rate of change (dTfacial/dt). Sample data from the observations used in developing the correlation and outside-data were utilized to compare simulated and predicted TC over a scale from −4 (very uncomfortable) to +4 (very comfortable). The standard error in estimating TC was 0.4 with a maximum deviation of 1.0. The developed method can be used to derive TC correlations pertaining to other complex dynamic thermal environments with different applications.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Nomenclature
dRH/dt | = | rate of change of relative humidity temperature, %/min |
dTfacial/dt | = | rate of change of facial temperature, °C/min |
dTindoor/dt | = | rate of change of indoor temperature, °C/min |
dTwrist/dt | = | rate of change of wrist temperature, °C/min |
HVAC | = | heating, ventilation and air conditioning area |
IES-VE | = | integrated environmental solutions-virtual environment |
NV | = | natural ventilation |
PV | = | personalized ventilation |
QSPV | = | personalized ventilator supply flow rate, L/s |
RH | = | relative humidity, % |
TC | = | thermal comfort |
Tfacial | = | facial temperature, °C |
Tindoor | = | indoor temperature, °C |
TMY | = | typical meteorological year |
TS | = | thermal sensation |
TSPV | = | personalized ventilator supply temperature, °C |
Twrist | = | wrist temperature, °C |
Greek symbols
α | = | intercept of the line |
β | = | linear slope coefficient |