A novel multi-domain model for thermal comfort which includes building indoor CO2 concentrations

Abstract

In a prior work, we applied Bayesian logistic regression to investigate possible quantifiable correlations between occupant’s perceived thermal comfort, thermal indoor conditions, and non-thermal metrics of indoor environmental quality (IEQ). This study updates the findings of the prior work by adding IEQ data collected from a recent field study carried out at the University of British Columbia. Bayesian logistic regression of the expanded dataset reinforces observations made in our first paper that thermal comfort is correlated to measured values of indoor CO2 concentrations. Cross-validation and posterior checks revealed that it is possible to increase the prediction accuracy of thermal satisfaction in open-plan offices by including measurements of CO2 concentration. This paper formulates a new predictive model of thermal comfort which can be used by building modellers to predict thermal comfort in office settings based on thermal conditions and ventilation rates.