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 (T _indoor), relative humidity (RH), facial temperature (T _facial) and its rate of change (dT _facial/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.

这项工作提出了一种方法，可以开发一种精确的相关性，以根据乘员的生理和环境参数预测热舒适度（TC）。该方法适用于依赖混合自然通风（NV）和个性化通风（PV）冷却的空间。采用多变量线性回归来发展TC相关性，同时保留基于显着性和相互依赖性的变量。发现相关性取决于室内温度（T _室内），相对湿度（RH），面部温度（T _面部）及其变化率（dT _面部/ dt）。利用用于建立相关性的观测数据样本和外部数据来比较从-4（非常不舒服）到+4（非常舒适）的模拟和预测TC。估算TC的标准误差为0.4，最大偏差为1.0 。所开发的方法可用于导出与具有不同应用的其他复杂动态热环境有关的TC相关性。