Experimental study of human thermal sensation estimation model in built environment based on the Takagi–Sugeno fuzzy model
thermal sensation estimation, T–S fuzzy model, human experiment, different active states, built environment
Current thermal sensation estimation models mostly are suitable for the sedentary condition, failing to consider the difference of human thermal sensation in different activity states. This has caused critical limitations in accurately predicting thermal sensation. Moreover, the development method of current models primarily relied on regression analysis, which ignored the non-linear characteristics between the skin temperature and thermal sensation. This paper aimed to identify the significant parameters that can accurately estimate human thermal sensation in different activity states by experimenting and developing the estimation model based on the Takagi–Sugeno (T–S) fuzzy model. A series of human subject experiments were carried out in an environment chamber. The results indicated the feasibility of using wrist skin temperature and its time differential and heart rate as variables for developing thermal sensation estimation model. After that, the T–S fuzzy model was used to develop the thermal sensation estimation models, taking into account the influence of gender. To analyze the applicability of the estimation models in an unstable condition, several experiments were further carried out in the actual built environment. The study revealed that the thermal sensation estimation model based on skin temperature and its time differential and heart rate showed a high degree of accuracy, while the estimation model based only on skin temperature and heart rate also indicated good prediction effect. In addition, the verification results illustrated that the proposed models can predict the human thermal sensation in the unstable environmental condition.
Tsinghua University Press
Wei Li, Jili Zhang, Tianyi Zhao et al. Experimental study of human thermal sensation estimation model in built environment based on the Takagi–Sugeno fuzzy model. Build Simul, 2019, 12(3): 365–377.