Indoor thermal environments in Chinese residential buildings responding to the diversity of climates

China has a diversity of climates and a unique historic national heating policy which greatly affects indoor thermal environment and the occupants’ thermal response. This paper quantitatively analyzes the data from a large-scale field study across the country conducted from 2008 to 2011 in residential buildings. The study covers nine typical cities located in the five climate zones including Severe Cold (SC), Cold (C), Hot Summer and Cold Winter (HSCW), Hot Summer and Warm Winter (HSWW) and Mild (M) zones. It is revealed that there exists a large regional discrepancy in indoor thermal environ- ment, the worst performing region being the HSCW zone. Human’s long-term climate adaptation leads to wider range of acceptable thermal comfort temperature. Different graphic comfort zones with accept- able range of temperature and humidity for the five climate zones are obtained using the adaptive Predictive Mean Vote (aPMV) model. The results show that occupants living in the poorer thermal environments in the HSCW and HSWW zones are more adaptive and tolerant to poor indoor conditions than those living in the north part of China where central heating systems are in use. It is therefore recommended to develop regional evaluation standards of thermal environments responding to climate characteristics as well as local occupants’ acclimatization and adaptation in order to meeting dual targets of energy conservation and indoor thermal environment improvement

Quantification of personal thermal comfort with localized airflow system based on sensitivity analysis and classification tree model

Although local air movement acts as a critical factor to enhance human thermal comfort and energy efficiency, the various factors influencing such movement have led to inconsistent publications on how to evaluate and design localised airflow systems in practice. This study aims to identify the main impacting factors for a localised airflow system and predict a cooling performance based on machine learning algorithms. Three typical localised airflow forms, i.e. an isothermal air supply (IASN), non-isothermal air supply (NIASN), and floor fan (FF), were deployed. The experiments were conducted under a variety of temperature/humidity/air velocity conditions in a well-controlled climate chamber, and a database including 1305 original samples was built. The primary results indicated that a classification tree C5.0 model showed a better prediction performance (83.99%) for a localised airflow system, with 17 input parameters in the model. Through a sensitivity analysis, 8 feature variables were quantified as having significant main effect responses on subjects’ thermal sensation votes (TSV), and three environmental factors (temperature, air velocity, and relative humidity) were identified as having the most significant effects. Using the 8 sensitive factors, the C5.0 model was modified with 82.30% accuracy for subject TSV prediction. A tree model demonstrating the decision rules in the C5.0 model was obtained, with air velocity (=0 m/s,＞0 m/s) as the first feature variable, and root node and temperature (≤28 °C,＞28 °C) as the second feature variable and leaf node, respectively. The outcomes that provide the most influential variables and a machine learning model are beneficial for evaluating personal thermal comfort at individual levels and for guiding the application of a localised airflow system in buildings

Moisture in clothing and its transient influence on human thermal responses through clothing microenvironment in cold environments in winter

Air humidity produces conditions of varying moisture contents in clothing, which affects the heat and moisture transfer between human body, clothing and environment, as well as the wearers’ comfort. This study was designed to evaluate the moisture effects in clothing in cold environments. A series of wearing experiments were conducted in a climate chamber, simulating transient moisture absorption and desorption in experimental clothes. Totally 20 subjects were involved in three temperature levels (16 oC/20 oC/24 oC) and two relative humidity levels (15% RH/85% RH) during winter, with physiological measurement and subjective evaluation. The results showed that moisture in clothing under 85% RH significantly reduced subject mean skin temperatures(MST) and increased the local blood flow, due to enhanced heat loss by vapour evaporation. The initial skin wettedness was approximately 0.7 at 85% RH and stabilised at 0.33 after 90min exposure. The skin heat loss (Qskin) at 85% RH was almost twice as high as that at 15% RH under the same temperature conditions, owing to larger sensible and evaporative heat loss caused by moist clothing. The inner clothing effective temperature Teff was proposed to relate to TSV that the TSV increased by 1.12 units with an increase of 1 oC of Teff, which quantified the coupled effects of air temperature and humidity in clothing microenvironment on human thermal comfort. The findings address the negative effect of clothing absorbing a large amount of moisture, which should be considered for indoor heating temperature designs in cold-humid environments

A modified method of evaluating the impact of air humidity on human acceptable air temperatures in hot-humid environments

This research aims to investigate human thermal responses to air humidity in warm and hot environments and to evaluate the effect of humidity on human thermal comfort. 20 subjects were involved in 12 exposure experiments in a well-controlled climate chamber at three relative humidity levels (40%, 60%, 80%) and four air temperature levels (26 °C, 28 °C, 30 °C, 32 °C) with little indoor airflow. The physical environmental and physiological parameters, as well as subjective questionnaires, were collected simultaneously with the on-going experiments. The results show that in hot environments, particularly when the air temperature exceeds 30 °C, the relative humidity has a significant effect on human thermal responses both physiologically and subjectively. The Standard Effective Temperature (SET) is biased when evaluating human thermal comfort in hot-humid environments without considering human thermal adaptation to humidity. Hence, a humidity correction coefficient eRH is proposed to modify the deviation of the SET under different relative humidity levels, and to quantify the effect of humidity on human acceptable air temperatures. The modified acceptable temperature-humidity zone has been obtained using the modified method

Energy flexibility for heating and cooling based on seasonal occupant thermal adaptation in mixed-mode residential buildings

The energy flexibility for heating and cooling has not been fully explored though human thermal adaptation has been acknowledged in achieving energy savings in buildings. The aim of this study is to explore the capacity for heating/cooling flexibility in residential buildings in the hot summer and cold winter zone in China, by investigating the year-round dynamic changes in the thermal adaptation of occupants. A 13,005-set data set was extracted from a nation-wide field survey database. The results showed that the measured indoor temperatures were linearly related to the outdoor temperature in transient seasons but were discrete in the summer/winter seasons due to the mixed-mode operations of heating/cooling devices. The occupants’ neutral temperatures varied with outdoor temperatures in step with seasonal changes. Flexibility of temperature settings during the whole heating and cooling periods have been demonstrated, incorporating the dynamic thermal adaptation changes of occupants; such implementation has been estimated with great energy saving potential (e.g. 34.4% in Nanjing). This work contributes to the quantitative understanding of the role of human thermal adaptation in the smart control of residential energy management. It provides evidence for policy-making for flexible thermal design codes in building, to discourage excessive cooling/heating demands

Modification of the Predicted Heat Strain (PHS) model in predicting human thermal responses for Chinese workers in hot environments

The Predicted Heat Strain (PHS) model predicts physiological responses of European people to heat stress; while its performance for Chinese population remains underexplored. The study conducted a heat exposure experiment (temperature: 35 °C/38 °C/40 °C, relative humidity (RH): 25%/40%/60%) in a well-controlled climate chamber. 10 male Chinese workers were recruited to perform exercises on the treadmill at a speed of 0.5m/s for 120min, to simulate moderate metabolic rate. Rectal temperature(Tre), skin temperature(Tsk), sweat rate(SR) and heart rate(HR) were monitored continuously; the corresponding predicted values were obtained by the PHS model. The results showed that the measured Tre, Tsk, SR increased significantly with increased temperature and humidity. The PHS model overestimated the maximum allowable exposure time of subjects but underestimated the final Tre and Tsk. Bland–Altman analysis showed that the differences and 95%CI between the observed and predicted values increased with increasing temperature and RH and exposure time, indicating the significant prediction deviation of the PHS model. Through adjusting the initial Tre from 36.8 °C to 37 °C, the protection efficacy was improved from original 24.7% to 57.1% for the PHS model. The protection efficacies were further improved to 71.2% through adjusting the maximum HR based on ages, and to 68.2% through adopting the real-time HR to predict metabolic rates. The proposed three methods improve the heat strain prediction in the PHS model for Chinese workers and are more applicable in practical hot working place. This benefits to policy decisions and occupational safety protection for Chinese workers with heat exposure risks

A ‘heart rate’-based model (PHSHR) for predicting personal heat stress in dynamic working environments

The parameter of human body metabolic rates has been popularly used for the prediction of human heat stress in hot environments. However, most modules use the fixed and estimated metabolic heat production. The aim of this study is to develop the prediction of personal heat stress in dynamic working environments. Based on the framework of the predicted heat stress (PHS) model in ISO 7933, a heart-rate based PHSHR model has been developed using the time-based heart rate index, which is suitable for prediction in situations where metabolic rates are dynamic and there are inter-individual variations. The infinitesimal time unit Δti, has been introduced into the new PHSHR model and all the terms used in the PHS model related to metabolic rates are thus redefined as the function of real-time heart rates. The PHSHR has been validated under 8 experimental combined temperature-humidity conditions in a well-controlled climate chamber. The feature of the PHSHR model is being able to calculate dynamic changes in body metabolism with exposure time. It will be useful to the identification of potential risks of individual workers so to establish an occupational working environment health and safety protection mechanism by means of simultaneous monitoring of workers’ heart rates at the personal levels, using advanced sensor technology

A method to identify individually physiological response differences to heat exposure using Comprehensive Deviation Coefficient (CDC)

With increasing global warming, a method to identify individual heat exposure risk and conduct interventions is essential, in order to mitigate impacts of extreme climates on people's health. This paper aims to examine the differences of individual's physiological response in hot environments and consequently proposes a personal-based method to identify potentially vulnerable populations with high risk. A heat exposure experiment was carried out in a climate chamber to build datasets, with nine conditions combining air temperature (35 °C/38 °C/40 °C) and relative humidity (25%/40% /60%). The rectal temperature (Tre), skin temperature (Tsk) and heart rate (HR) of 10 subjects were monitored. Data were analyzed using multiple-dimensional metrics of average deviation(AD), coefficient of variation(CV) and skewness(SKEW). The study introduced the Moment of Inertia (MI) and the Simulated Mass System (MS) in a multidimensional coordinate system and developed a Comprehensive Deviation Coefficient (CDC) method. Using various combinations of AD/CV/SKEW, the values of CDCTre, CDCTsk, CDCHR were calculated; the high-risk thermal environment (40 °C/60%) and subject were thus identified. The proposed CDC method enables to distinguish the individual's physiological response differences, under different hot environments and personal characteristics. The equations in this method can be programed in computer and integrated with smart sensor technology, contributing to identify the high-risk environments and provide precautions for susceptible populations, to mitigate the heat exposure hazards on people’ health and safety

How do urban residents use energy for winter heating at home? A large-scale survey in the hot summer and cold winter climate zone in the Yangtze River region

The increasing demand for improving indoor thermal environment in the hot summer and cold winter climate zone (HSCW) in the Yangtze River region in China poses enormous challenges in terms of energy policy and design solutions for this unique region. A comprehensive understanding of people’s habits and behaviors involving winter heating is imperative for decision making for urban heating infrastructure investment strategies that significantly impact the decarbonization of heating. However, there are little studies of a large-scale survey to gain such knowledge acrose the region. The aim of this study is to develop a rigorous survey method in order to obtain reliable data for analysis. Five municipal/capital cities across the upper, middle and downstream Yangtze River were surveyed based on 30 randomly generated locations in each city. A total of 8481 valuable samples were obtained in the survey conducted in the winter from November 2017 to March 2018. It is revealed that air conditioning/air source heat pumps are the predominant systems, accounting for 63% and 58% for bedroom and living room heating respectively. The use patterns of heating are diverse featuring ‘part-time-part-space’ systems in accordance with the occupancy patterns. There is significant evidence of the habit of opening a window to provide a gap for fresh air irrespective of whether the heating is in use. Two-step cluster analysis is employed to subdivide occupants’ heating-related behaviors into three clusters to characterize households. This study fills the knowledge gap of winter-heating-related behaviors. The research outcomes will benefit building energy simulations for energy prediction and help policy makers making decisions on providing strategic guidance in terms of winter heating solutions in this region

Exploring on the prediction model of human skin temperature and rectal temperature under heat stress

Hot environments cast direct influence on people's well-beings and health besides working productivity during the accelerating global warming. The systematic mechanism is not entirely clear due to the complex human physiological response which also handled human heat strain protection. This study carries out mathematical derivation on the rectal temperature and skin temperature under human heat strain based on the PHS model of ISO 7933 standard. The experimental validation was conducted in a climate chamber with continuously monitoring the typical physiological parameters of 10 subjects in 9 hot working environments. Physiological parameters of subjects were analyzed on average difference (AV), coefficient of variation (CV) and skewness (SKEW) to explore their mutual linkages. Both experimental and theoretical result shows that certain relationship existed between the rectal temperature and skin temperature. The Poisson's ratio on their average value, coefficient of variation, average difference and skew were 0.957, 0.991, 0.990 and 0.941 respectively. Linear relation with coefficient of 0.73 was found between rectal temperature and skin temperature coupling with the constant of 12.09. Comparison on the dynamic changes of rectal temperature and skin temperature was carried out and verified under PHS model. The results showed that rectal temperature was adjusted earlier and more forcefully than skin temperature indicating that human core temperature was better protected under heat stress. The findings contribute to a convenient method of human heat strain prediction when taking rectal temperature as the best physiological threshold with fast human skin temperature and environment parameters monitoring in complex high temperature working environments