Constructal Evaluation of Polynomial Meta-Models for Dynamic Thermal Absorptivity Forecasting for Mixed-Mode nZEB Heritage Building Applications

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