Combining adaptive and heat balance models for thermal sensation prediction: A new approach towards a theory and data-driven adaptive thermal heat balance model

The adaptive thermal heat balance (ATHB) framework introduced a method to account for the three adaptive principals, namely physiological, behavioral, and psychological adaptation, individually within existing heat balance models. This work presents a more detailed theoretical framework together with a theory-driven empirical determination toward a new formulation of the ATHB(PMV). The empirical development followed a rigor statistical process known from machine learning approaches including training, validation, and test phase and makes use of a subset (N = 57 084 records) of the ASHRAE Global Thermal Comfort Database. Results show an increased predictive performance among a wide range of outdoor climates, building types, and cooling strategies of the buildings. Furthermore, individual findings question the common believe that psychological adaptation is highest in naturally ventilated buildings. The framework offers further opportunities to include a variety of context-related variables as well as personal characteristics into thermal prediction models, while keeping mathematical equations limited and enabling further advancements related to the understanding of influences on thermal perception.

Automated summary

The adaptive thermal heat balance (ATHB) framework introduces a method to account for physiological, behavioral, and psychological adaptation individually within existing heat balance models. This study presents a more detailed theoretical framework and a new formulation of the ATHB(PMV) based on theory-driven empirical determination. Through an empirical development using a subset of the ASHRAE Global Thermal Comfort Database, the results show improved predictive performance in various outdoor climates, building types, and cooling strategies. The framework challenges the common belief that psychological adaptation is highest in naturally ventilated buildings and provides opportunities to include context-related variables and personal characteristics in thermal prediction models.