Exploring the “black box” of thermal adaptation using information entropy

Abstract

Thermal adaptation has been interpreted well by behavioral, physiological, and psychological factors, but the mechanism and interaction between the three factors remain in the "black box". This paper aims to apply the theory of general system and information entropy to investigate the quantitative relationships of the three thermal adaptation processes. Based on the database from the field survey and laboratory experiments conducted in the hot summer and cold winter climate zone of China, three typical adaptive indices: clothing insulation (Clo), thermal sensation votes (TSV) and sensory nerve conduction velocity (SCV) were selected to calculate Clo entropy, TSV entropy, SCV entropy and total entropy. The regression models were developed between these entropies and the indoor air temperature to quantify the weights of the three adaptive categories. The models were used to compare the differences between China and Pakistan as well as between adaptive approaches and climate chamber experiments. The thermal comfort and acceptable temperature ranges were obtained using the entropy models. Our findings propose a new perspective using entropy to quantify the behaviorally, physiologically, and psychologically adaptive approaches, which contribute to a better understanding of opening the "black box" of thermal adaptation.

Adaptive‐rational thermal comfort model: Adaptive predicted mean vote with variable adaptive coefficient

“…Field data from the naturally ventilated, air‐conditioned, and mixed‐mode buildings have verified the proposed arPMV with a large reduction in the mean absolute error and a large improvement in the robustness of the thermal sensation prediction compared with the aPMV (Sections 3‐5). In the literature, there are some efforts on differentiating the three categories of thermal adaptations to explore the mechanisms of thermal adaptations 34,40,63,64 . For example, Jing 40 and Liu 64 aimed at figuring out the relative importance of the three categories of thermal adaptations using the information entropy and the analytic hierarchy process, respectively.…”

“…In the literature, there are some efforts on differentiating the three categories of thermal adaptations to explore the mechanisms of thermal adaptations 34,40,63,64 . For example, Jing 40 and Liu 64 aimed at figuring out the relative importance of the three categories of thermal adaptations using the information entropy and the analytic hierarchy process, respectively. Schweiker and Wagner 34,63 determined the clothing level and metabolic rate for the PMV calculation by setting up equations for each of the three categories of thermal adaptations individually.…”

“…The aPMV determines thermal comfort statically because for a given type of buildings with a cool or warm environment, the adaptive coefficient is fixed 17,38 . In contrast, the real thermal comfort is dynamic with the ambient temperature (Figure 1) 1 because the thermal adaptations are a dynamic process 40,41 . Conceição et al 42 and Liu et al 43 also identified the inadequacy of the aPMV in explaining the thermal adaptations due to the fixed adaptive coefficient.…”

Improving predicted mean vote with inversely determined metabolic rate

“…The linear relationship between the metabolic rate and the indoor air temperature is resulted from the physiological adaptation. Using the information entropy analysis of a large database, Jing et al [40] found that the physiological adaptation was linearly related to the indoor air temperature approximately. Since the air velocity (> 0.2 m/s) also has cooling effects [41] and is encouraged for the thermal preference and energy saving [6], Equation 1 correlates the metabolic rate to both the room air temperature and velocity linearly.…”

Assessing energy saving potentials of office buildings based on adaptive thermal comfort using a tracking-based method

“…The study proposed that the active interaction of offices' internal environments with the outdoor environment will motivate further connections between occupants' thermal opinions and outdoor physical variables [26]. However, in most recent researches [11,14,[27][28][29][30][31][32], there is a lack of explanation regarding the dynamic changing characteristics of a person's behavioral adaptability to environmental changes, while there is a growing need to better understand the impact of seasonal variation on occupants' dynamic thermal comfort and using large time scales may hide the dynamic process of thermal adaptation. Therefore, obtaining detailed information such as the occupant's response to seasonal changes in climate in a large-scale survey is very challenging.…”