Alliesthesia-Informed Machine Learning for Predicting Dynamic Thermal Comfort in Intermittent Convective Cooling Environments

In intermittent convective cooling environments created by split air conditioners, the dynamic nature of the environment poses challenges to traditional steady-state thermal comfort models in predicting human thermal comfort. Therefore, this study proposes an alliesthesia-informed machine learning framework that encodes alliesthesia theory into explicit mathematical features for predicting dynamic overall thermal comfort. Data were obtained through controlled experiments under intermittent cooling conditions, and a theory-driven feature set incorporating dynamic set points and physio-psycho gap was constructed. The results demonstrate that the gradient boosting model achieved optimal performance under rigorous subject-level cross-validation (test set R² = 0.71). Interpretability analysis confirmed that model decisions are highly dependent on exposure time and alliesthesia features, whose importance far exceeds that of conventional environmental parameters, revealing that the core of thermal comfort perception lies in the dynamic interplay between physiological states and psychological expectations. Furthermore, the proposed few-shot personalized calibration strategy can effectively accommodate individual differences with minimal user data. This study demonstrates that the framework not only enhances prediction accuracy but also improves model interpretability and generalizability by incorporating alliesthesia-inspired feature representations, offering a new perspective for developing next-generation human-centric intelligent environmental control systems.