Abstract:
This work aims to develop an accurate correlation using a modeling methodology to predict thermal comfort (TC) as function of occupant physiological and environmental parameters for a space that relies on the hybrid natural ventilation (NV) and personalized ventilation (PV) cooling system. 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 actual and predicted TC results over a scale from -4 (very uncomfortable) to +4 (very comfortable). The reported standard error in estimating TC was 0.4 with a maximum deviation of about 0.8.
The TC correlation is then utilized in developing dynamic controllers for NV-PV systems. A PV unit, autonomously controlled, in a NV office space was developed to maintain acceptable TC at all times of operation. The NV-PV controller robustly adjusts the PV supply temperature (TSPV) at the occupant set flow rate based on the developed TC correlation. The target TC level that the controller should attain at all times is between 0.5 and 1 based on Zhang’s TC scale (just comfortable to slightly comfortable). The developed controller was tested in a case study of an office space in Beirut’s climate (with indoor temperature ranging between 25 and 33 C, and RH between 60 and 80 %). It was shown that the NV-PV controller can dynamically adjust TSPV to maintain acceptable TC between 0.5 and 1 at all times.
