Abstract
The intelligent and appropriate regulation of indoor temperatures within heritage buildings is crucial for achieving nearly Zero-Energy Building (nZEB) standards, since the technical improvement of the envelope and the overall shape of heritage buildings should be very limited in order to preserve the buildings’ authenticity. Thermal comfort is a very important factor that influences the energy performance of a building and the wellbeing of its end users. The present paper focuses on the development of a dynamic thermal human stress model that aimed to accurately predict the necessary garment insulation within a typical high-inertia heritage building. Two different statistical approaches (a Hoke D6 design and a composite factorial design) were employed for the development of this meta-model adapted to a typical mixed-mode heritage building seeking to obtain nZEB classification. Thermal human stress was modeled through the prediction of the thermal absorptivity (b) in accordance with the updated ASHRAE 55 model. Physically measured indoor climate parameters, outdoor meteorological data, and building operational information were coupled to the subjective sensorial dimensions of the problem with the aim of identifying the necessary garment insulation levels within heritage buildings composed of high-thermal-mass materials (for example, stone, concrete, and ceramic tiles). Our investigation focused on the parameter directly linked to the cold/warm sensations experienced due to clothing insulation: thermal absorptivity (b). In brief, the present paper proposes a third-order regression polynomial model that facilitates the calculation of thermal absorptivity, relying on adaptive thermal comfort concepts. The meta-model was then evaluated using Adrian Bejan’s constructal law after conducting entropy analysis. The constructal evaluation of the meta-model revealed the characteristic size of the domain regarding variable thermal absorptivity (b) and identified the necessary evolution of the model in order to increase its forecasting capacity. Thus, the model provided accurate forecasting for thermal absorptivity values greater than 50 Ws−1/2 m−2K and will be developed further to improve its absolute location accuracy for scenarios wherein the thermal absorptivity value is lower than 50 Ws−1/2 m−2K.