Search

Personal thermal preference information can help to create a building environment that satisfies all staff, instead of an environment that only satisfies most people, to enhance personal thermal comfort. Research has shown that thermal preference can be predicted using parameters that are based on various local body parts, but the selected body parts are often different. Using too many body parts for the measurements leads to high costs, while using too few body parts results in large errors. In this study, 19 adult subjects (8 females and 11 males) were recruited, their overall and local thermal preferences were surveyed, and the skin temperature of seven body parts were measured. A machine learning algorithm, random forest, was employed to analyse the contributions of different body parts. Three criteria (the best combination, fewest combination, and common combinations) were employed to select body parts to use to establish thermal preference models for individuals and groups. The results show that the prediction power of these combinations reached 0.91 ± 0.07 (accuracy), 0.75 ± 0.16 (Cohen's kappa), and 0.87 ± 0.09 (AUC) when using 2–8 body parts. The common combinations are recommended for their balance of their prediction power and the number of local body parts involved. This study offers a reference for efficient and economic measurements for thermal comfort research in building environments. [ABSTRACT FROM AUTHOR]

Clothing has a significant impact on the heat transfer between the human body and the environment, and clothing insulation is an important input parameter for many thermal comfort models. With the development of studies on non-uniform environment and human's local thermal comfort, the clothing local insulation is required in the multi-node human thermoregulation models and significantly affects the accuracy of simulation results. However, there lacks data on clothing local insulation values in standards. In this study, the local insulation of 57 typical garments and 62 ensembles with different layers (1–6 layers) were measured using a thermal manikin. The results show that the ranges of overall insulation for tested garments and ensembles were 0.01–0.56 clo and 0.27–2.17 clo, respectively. The local insulation values differed greatly from the overall insulation values, and varied greatly between different body parts. Further, the linear regression was performed between the ensembles' local insulation at different body parts and the sum of the garments' local insulation. Based on the prediction equations of local insulation, a new method for estimating the overall insulation of ensembles was proposed. Compared with the tradition method using the linear regression equations of overall insulation, the new method reduced the mean relative error from 12.6%–9.1% to 3.4%. This study provided basic data and prediction equations of local insulation for future researches of clothing insulation and local thermal comfort. • A clothing local insulation database including 57 typical garments and 62 ensembles was built. • Linear regression equations of local insulation at different body parts were proposed. • A new method for predicting overall insulation of ensembles using garments' local insulation was developed. • New method reduced the MAPE from 9.1% to 3.4% compared with the method using linear model of overall insulation. [ABSTRACT FROM AUTHOR]

Human thermoregulation models can predict human thermal responses to evaluate thermal comfort and help create a healthy environment, while their applicability to older people has not been sufficiently validated. This study aimed to evaluate the performance of the Stolwijk model and the Tanabe model for predicting older people's mean and local skin temperatures under thermal transient conditions. Eighteen healthy older people were recruited and exposed to transient environments including neutral (26 °C), low-temperature (23 and 21 °C), and high-temperature (29 and 32 °C) conditions. The local skin temperatures of the subjects were measured and compared to predictions of the Stolwijk model and the Tanabe model. The results revealed that the Stolwijk model and the Tanabe model could accurately predict the mean skin temperature of older people under neutral and high-temperature conditions, while their predictive accuracy declined under low-temperature conditions. Increased deviations were observed in the predictions of local skin temperatures for all conditions. This work attempted to provide an understanding of older people's thermal response characteristics under transient conditions and to inspire the improvement of thermoregulation models for older people. [ABSTRACT FROM AUTHOR]