Clothing Insulation Levels Research Articles

Predicting the clothing insulation through machine learning algorithms: A comparative analysis and a practical approach

Since indoor clothing insulation is a key element in thermal comfort models, the aim of the present study is proposing an approach for predicting it, which could assist the occupants of a building in terms of recommendations regarding their ensemble. For that, a systematic analysis of input variables is exposed, and 13 regression and 12 classification machine learning algorithms were developed and compared. The results are based on data from 3352 questionnaires and 21 input variables from a field study in mixed-mode office buildings in Spain. Outdoor temperature at 6 a.m., indoor air temperature, indoor relative humidity, comfort temperature and gender were the most relevant features for predicting clothing insulation. When comparing machine learning algorithms, decision tree-based algorithms with Boosting techniques achieved the best performance. The proposed model provides an efficient method for forecasting the clothing insulation level and its application would entail optimising thermal comfort and energy efficiency.

Real-time clothing insulation level classification based on model transfer learning and computer vision for PMV-based heating system optimization through piecewise linearization

Achieving a balance between energy efficiency and thermal comfort is a key aspect of sustainable building design. Traditional control methods typically maintain indoor air temperature within predetermined limits, disregarding variable factors such as occupancy activity and clothing levels, which influence thermal comfort. Conversely, comfort-based control strategies present an opportunity to automate heating and cooling systems, enabling them to dynamically respond to variations in thermal comfort. To achieve this, real-time information on clothing insulation (and its adjustment) is indispensable for accurately estimating thermal comfort. In this study, we explore the potential of a novel detection approach capable of classifying clothing insulation in real-time and utilizing this information to optimize the operation of building energy systems. By doing so, the proposed method facilitates the delivery of indoor conditions tailored to user requirements and potentially reduces energy wastage. The development of a two-stage, computer vision-based framework for occupancy detection and clothing insulation classification forms the core of this approach. Leveraging deep learning algorithms, this framework performs detection and recognition tasks, even with limited training data effectively, enabling real-time classification of light, medium and heavy clothing. To address the nonlinearity of traditional predicted mean vote (PMV) models, we applied a piecewise linearization approach to our PMV-based optimal control strategy. We evaluated the performance of this detection method through initial experimental field tests conducted in a case study university building. The results demonstrate the proposed method's ability to classify clothing insulation levels and generate real-time profiles. We further analyze the impact of our proposed approach on thermal comfort and energy performance through scenario-based modelling and simulations. The initial results showed the potential of integrating our method with PMV-based controls to enhance thermal comfort and overcome the limitations of predefined values or fixed schedules. However, while our study confirms the feasibility of classifying clothing insulation levels for multiple occupants engaged in diverse indoor activities, it also highlights the need for further refinement to enhance detection accuracy and ensure seamless integration with building energy systems.

Adaptive thermal comfort in the offices of three climates of North-East India

Buildings not only provide shelter to the occupants but also security, safety and comfort, as well as signifying economic and socio-cultural status. The high energy intensity of buildings has become a matter of concern worldwide in the context of climate change and global warming. Adaptive comfort principles and guidelines afford greater design opportunities for reduced energy intensity without compromising occupant thermal comfort and indoor environmental quality performance. However, thermal adaptation opportunities of office occupants in the North-Eastern part of India are rarely addressed by previous thermal comfort studies conducted in the Indian subcontinent. A yearlong questionnaire-based adaptive thermal comfort study with concurrent indoor environmental measurements was conducted in naturally ventilated office buildings located in three locations, i.e., Tezpur, Imphal and Shillong, representing the climatic zones of North-East India. A total of 2326 valid questionnaire responses were collected. ASHRAE Class-II field study protocols, seven-point thermal sensation and five-point preference scales were used to collect objective and subjective comfort assessments. It was found that the clothing insulation (clo) levels worn by occupants varied non-linearly with indoor temperatures. The estimated preferred temperatures for building occupants in Tezpur, Imphal and Shillong were 24 °C, 23.5 °C and 22 °C, respectively. Probit analysis of thermal sensation data indicated that the indoor temperature stimulus associated with a shift of one thermal sensation unit differed between the warm and cool side of the 7-point thermal sensation scale. Increasing indoor air velocity up to 0.8m/s shifted the upper threshold of discomfort to 32 °C. It was also found that 80% of office occupants were comfortable within the temperature range of 25 °C–30 °C. The adaptive comfort model derived from the current database shows lower sensitivity of subjects to outdoor temperature changes compared to existing International standards like ASHRAE Standard 55 or EN16798. The present study demonstrates the potential for low-energy, naturally ventilated office design strategies in similar climatic conditions in North-East India and other parts of the world. Above all, it enriches the worldwide thermal comfort database.

A Comparison in Perception of Local and Foreign Residents to Thermal Comfort in Naturally Conditioned Residential Buildings

Thermal comfort is the individual satisfaction with the surrounding thermal environment. It is mainly affected by environmental factors (i.e., air temperature, relative humidity, air movement, and mean radiant temperature) and individual factors (i.e., activity and clothing). However, other factors such as acclimatization, experiences and expectations, food and drink, body shape and subcutaneous fat, age and gender, and state of health might play a significant role in the individual sensation and satisfaction of the thermal environment. This study investigates the possible differences in thermal perception between local and foreign residents, which might occur due to the influence of the individual and contributing factors that are linked to their thermal, cultural, and behavioural backgrounds. High-rise residential buildings that accommodate local and foreign residents were selected, and a questionnaire survey was distributed to assess their thermal comfort perception. The results showed differences between local and foreign residents in thermal comfort perception. The foreign residents were more satisfied and comfortable with the thermal environment compared to the local residents. However, this difference was found to be statistically insignificant; therefore, it might be linked to factors linked to the current study, including acclimatization, expectation, clothing insulation and activity levels. Therefore, a further large-scale investigation might be required with more analysis on the role and influence of the contributing factors on the thermal sensation of different gropes.

A prediction model for CO2 concentration and multi-objective optimization of CO2 concentration and annual electricity consumption cost in residential buildings using ANN and GA

Environmental pollutants in the air have long been a great threat to the health and life of human society and the volume of these pollutants is rapidly increasing. Human beings spend most of their time in closed environments which highlights the demand for appropriate indoor air quality. This favorable air quality makes sense when the concentration of pollutants such as CO2 that have penetrated the building space is reduced. This paper aims to predict a model for CO2 emissions and optimize it for 6 cities of the U.S with different climates. Firstly, using the time series of GMDH type of artificial neural network, the amount of these pollutants was predicted monthly and annually from 2020 to 2025. The results show that the amount of CO2 pollutants during this period increases by 1–3% and 1.25–1.8% on a monthly and annual basis, respectively. In this research, to solve this huge predicament in the residential sector, 5 design variables are considered, which are the thermostat set point temperature of the air conditioning system for cooling and heating, the clothing insulation level of the residents' clothes for winter and summer seasons, and the amount of clean air that is transferred from the outside to the inside of the building by the air conditioning system. The goal is to simultaneously minimize CO2 emissions and annual electricity consumption costs of the building and improve the thermal comfort of building occupants. Therefore, design variables and objective functions in JEPLUS software are defined. Afterward, they are analyzed based on the building's energy performance using EnergyPlus software. The elicited data are then transferred to JEPLUS + EA software, where they are optimized by the NSGA-II algorithm, which finally discovers the most optimal states so that users can select any state that is in line with their goals.

Comfort temperature prediction according to an adaptive approach for educational buildings in tropical climate using artificial neural networks

Following the adaptive thermal comfort approach, we show a thermal comfort modeling by artificial neural networks to predict the preferred comfort temperature Tcomf for educational buildings occupants in a tropical climate. The field data collected in 27 educational buildings, climatized with air conditioning systems AC, and natural ventilation NV, in Tuxtla Gutiérrez-México, allow developing the Tcomf modeling by artificial neural network-based models ANN-BM. The predictor variables were the outdoor running mean temperature, relative humidity, air velocity, weight, clothing insulation, and activity level. For AC, the R2 and the mean absolute error were 0.985 and 0.319°C; for NV, 0.981 and 0.286°C. Concerning local models and current standard models, the accuracy improvement to determine Tcomf was significant, following R2; for AC mode, the ANN-BM was 50 times better; for NV was 41.5 times better. The mean Tcomf by ANN-BM was 2.4°C above obtained with the CIBSE guide and 1.3°C concerning a Local model. For NV, the mean Tcomf was 0.5°C above following CEN standard EN15251, 2.0°C above ASHRAE standard 55, and 1.1°C above the Local model. Thus, allowing the air conditioning to run at a higher Tcomf than calculated with current standard models, making it possible to reduce thermal cooling loads while thermal satisfaction also increases.

Thermal discomfort in healthcare workers during the COVID-19 pandemic.

Introduction:Due to the COVID-19 pandemic, healthcare workers are now required to use additional personal protective equipment (PPEs) to protect themselves against the virus. That led to an increased clothing insulation which is negatively affecting the perceived healthcare workers’ thermal sensation.Objectives:While demonstrating through software simulations the potential level of thermal discomfort healthcare workers involved in the COVID-19 emergency can be subjected to, this work aims at identifying measures to improve thermal sensation perception and acceptable thermal conditions for medical personnel.Methods:After having obtained the insulation values of individual clothing used by staff during COVID-19 emergency through the use of a thermal well-being evaluation software, the Fanger indexes (PMV - Predicted Mean Vote and PPD - Predicted Percentage of Dissatisfied) were calculated in order to estimate staff satisfaction to microclimatic conditions.Results:The use of COVID-19 additional PPEs with an air temperature equal to 22 °C (normally considered optimal) brings the PMV index equal to 0.6, which corresponds to 11.8 % being unsatisfied (PPD) due to perceived heat.Discussion:The use of additional protective devices significantly increases the clothing insulation level, facilitating the onset of conditions of thermal discomfort in the health workers. Workers engaged in the execution of nasopharyngeal swabs were most affected by the summer weather conditions and certainly represent the most critical category, for which it would be recommended to implement a higher turnover of service to reduce individual exposure time and consequent discomfort.

A survey of high school students' clothing in classroom

In this study, a thermal-environment survey focusing on clothing behaviors of students in high-school classrooms was performed during summer. Subsequently, statistical analysis was performed to estimate the realizable limit level of adaptation to the thermal environment by adjusting clothing behavior. Additionally, a questionnaire-based subjective-vote and thermal-environment surveys were conducted in a naturally ventilated high school in Nagano city, Japan, during summers of 2017 and 2018. Nagano city is characterized by a humid subtropical climate (Cfa), per Köppen climate classification. The study involved 59 male and 21 female participants aged between 15 and 18 years. Five hundred and two subjective votes were collected in this study with 365 and 137 votes being cast by male and female participants, respectively. The average outdoor air temperature measured 23.9 °C during the survey period, whereas the average indoor operative temperature equaled 28.0 °C. The insulation level of clothing worn by participants varied in the 0.29–0.77 clo range. Results of regression analysis reveal that both male and female participants reduced their clothing insulation level in response to an increase in indoor operative temperature. However, at operative temperatures exceeding 29 °C, the median value for clothing insulation was observed to be in the 0.31–0.37 clo range. This result reveals limitations that exist with regard to clothing adjustments that can be made by participants in hot environments. Additionally, thermal sensation votes were observed to increase at operative temperatures exceeding 29 °C, thereby confirming the existence of a limit to thermal-environment adaptation realizable via adjustment of clothing insulation levels.

The degree of adaptive thermal comfort in office workers in a hot-summer Mediterranean climate

This study aims to investigate occupants’ perception of feeling comfortable over different seasons in offices located in Amman, Jordan, to determine the comfort temperature zones, and to compare the results developed from this study with other adaptive models and standards. It is based on longitudinal field surveys, which were conducted in three office buildings, two mixed mode buildings and a free running building, during four seasons: April 2016 (spring), July/August 2016 (summer), October 2016 (autumn) and January/February 2017 (winter). A total of 119 occupants participated in the thermal comfort surveys and completed 659 questionnaires. The free running building experienced a variation in operative temperature during the four seasons, while median temperatures in the mixed-mode buildings stayed almost the same during all seasons. Occupants felt comfortable in a broad range of thermal sensations from ‘cool’ to ‘warm’. Thermal comfort was expressed by a high proportion of the occupants. Occupants changed their clothing insulation with season, whereby compared to winter some occupants showed a changed clothing insulation already in the spring survey and others only in the summer and autumn survey. A Loess analysis between the temperatures at which the occupants felt comfortable and the running mean outdoor temperature indicated independency between the two variables in case of the mixed-mode buildings at running mean outdoor temperatures below 22 °C, but a trend towards decreasing comfort temperature above 22 °C. The comfort temperature in both mixed mode buildings was around 23.5 °C. In the free running building, a monotonically increasing linear relation between the two variables was found at running mean outdoor temperatures below appr. 24 °C, but a constant comfort temperature of about 26 °C above this value. Compared to the adaptive comfort models in ASHRAE Standard 55 and EN 15251 the gradient in the adaptive comfort equation resulting from the free running building in Amman was higher. Although the occupants adjusted their clothing insulation level, the gradient in the comfort equation was close to zero in the mixed mode buildings.

Influence of outdoor thermal environment on clothing and activity of tourists and local people in a severely cold climate city

Outdoor thermal environment has become a critical factor in both architecture design and urban planning field, and some researchers argued that the reactions of different people to outdoor environments should be considered, for instance, age, gender and so on. While tourists are exposed to an outdoor environment of unfamiliar climate in a short period, and their clothing and activity level which are supposed to be different from local people are surely to influence their outdoor thermal environment experiences, which need to be clearly revealed. This study investigated 1740 samples in Harbin, China, and 844 samples in summer and 896 samples in winter were gathered. The samples were divided into local people and tourists, and their reactions of clothing insulation and activity level to the outdoor environment were studied. The results show that the local people and tourists reacted to the outdoor environment by clothing insulation in both summer and winter, and the tourists dressed more clothing in winter than the local people. The tourists’ activity level was not influenced by the outdoor thermal environment, which should be due to a pre-made plan when they traveled to a new place, as for the local people, their activity level was influenced by the outdoor environment in summer, higher air temperature led to higher activity level, and in winter, it seems that they did not react to the outdoor environment clearly, and they preferred to do more activities in late morning and afternoon, to avoid the very cold early morning maybe.

Study on clothing insulation distribution between half-bodies and its effects on thermal comfort in cold environments

Clothing is important for keeping the human body warm in cold environments. In Fanger's predicted mean vote (PMV) model, clothing insulation is a single input parameter that describes the dressing status of the whole body. The objective of this study was to examine the effects of the clothing insulation distribution between half-bodies. The distribution index (DI) was defined to describe the uneven level of the clothing insulation distribution. An experiment with 20 subjects and 9 different clothing conditions was conducted in a climate chamber. Subjects were asked to be sedentary and wear specified sets of garments in a 20 °C environment. The thermal insulation values of the garment sets were measured by using a thermal manikin. The nine clothing conditions were divided into four groups. Although their thermal insulations were very close, the DI values were quite different in each group. Subjective evaluations of thermal comfort were collected, and seven-point skin temperatures were measured. To investigate this topic with a wider temperature range and greater clothing diversity, a one-year continuous field study was conducted with 18 subjects. The results indicated that the subjective thermal evaluations were different when the clothing insulation levels were close and the DI values were different. Sets of garments with a lower DI were more likely to lead to lower thermal sensation vote (TSV) and more subjects preferring a warmer environment. Clothes with an uneven DI (<0.75 or >1.25) and less clothes on the lower body (DI < 1) made people more sensitive to cold environments.

Evaluating the suitability of standard thermal comfort approaches for hospital patients in air-conditioned environments in hot climates

Hospital patients have the same thermal needs but disparate metabolic levels, and physical/medical conditions. However, the thermal environments of hospitals are often not designed with these distinctions in mind, nor with a particular focus on patients, but based rather on standard comfort methodologies more often used in offices. This paper seeks to confirm if a standard steady-state thermal comfort approach is inadequate, especially in hot climates. The research was conducted on 120 patients during the summer of 2017 in Jeddah, Saudi Arabia, with environmental monitoring of all thermal comfort parameters, alongside estimations of clothing insulation and activity levels for patients in the surgical and medical wards. The data was analysed using simple and multiple regressions, and measures of correlations tests to assess the reliability of the results in addition to t-tests for detecting the differences. The findings revealed a significant difference between the Thermal Sensation Vote (TSV, assessed by patient surveys), and the Predicted Mean Vote (PMV, assessed by physical measurement), with the TSV survey approach failing to identify a unique neutral temperature while the PMV revealed a neutral temperature of 25.6 °C. Importantly, the neutral temperature predicted by Griffith's method gave an extremely large range of results, form 16.2 °C–28.8 °C (mean = 22.7 °C; SD = 2.51). The corollary being that using PMV or a non-patient-specific temperature for hospital rooms is a poor idea. Given the known links between hospital environments and recovery outcomes, this result has implications for the design of hospital environments and the setting of national or international standards.

Prediction of indoor clothing insulation levels: A deep learning approach

Clothing insulation is a key variable in the prediction of occupant thermal comfort. Consequently, the aim of the current study was to develop predictive models that forecast clothing insulation levels of building occupants. Using field measurements, we investigated the influence of outdoor environment factors and mode of transport on clothing insulation levels of university students. Our results showed that both the mode of transport and weather variables influenced the clothing insulation levels of the students. We then developed a deep neural network model that forecasts mean daily clothing insulation levels using outdoor air temperature at 6 am, dew point temperature at 6 am, gender, season and mode of transport in the based on the collected data from 1316 questionnaire surveys. In addition, we revealed that outdoor environment factors had stronger associations with clothing insulation levels than indoor environment elements. The developed deep neural network model indicated a high R² value of 0.90. In comparison to the deep neural network model, a developed linear model using the same data indicated a lower R² value of 0.698, which implies that the proposed deep neural network model provides an efficient method to forecast clothing insulation levels.

Thermal Comfort, Energy and Cost Impacts of PMV Control Considering Individual Metabolic Rate Variations in Residential Building

To date, most of the indoor environment control is based on the dry-bulb air temperature, which is one of the simplified control methods having the limitation to truly represent the thermal comfort of individual occupants. A variety of factors affect the thermal comfort such as dry-bulb air temperature, humidity, air movement, radiation, clothing insulation, and metabolic activity level. In this circumstance, this study investigated the effects of considering hourly metabolic rate variations for predicted mean vote (PMV) control on the actual thermal load, energy usage, and life cycle cost (LCC). The case adopting PMV control taking the hourly metabolic rate into account was comparatively analyzed against the conventional dry-bulb air temperature control, using a detailed simulation technique after the validation process. As a result, when the activity state of the occupant is house cleaning in the summer, the indoor temperature decreases rapidly due to the high amount of activity. It requires a temperature that is 11.7 °C and 9.7 °C lower than the conventional dry-bulb air temperature control method, respectively, and generally forms a higher indoor air temperature than the conventional control method after 7 p.m. This means the difference in temperature to satisfy the comfort of the occupant according to the amount of activity, and during winter as opposed to summer, was found to form a lower indoor air temperature than the conventional temperature control. In case of annual boiler gas consumption, PMV control showed 7.3% less energy consumption than the dry-bulb air temperature control and showed 28.8% less energy consumption than the dry-bulb air temperature control for annual cooling electricity consumption. Considering the cooling and heating energy reduction rate and the initial installation cost of measuring equipment for real-time metabolic rate and PMV measurement, a payback period of approximately 4.15 years was required.

Variations of PMV based Thermal Comfort and Cooling/Heating Load according to MET

Purpose: Korean government originally established the target of greenhouse gas emission reduction by 30% by the year of 2020, but adjusted the target to 37% reduction by 2030, recently. The efficient indoor environmental control is an essential factor for the achievement of the goal. However, most of the indoor environment control is based on the dry-bulb air temperature, which is one of the most simplified control methods having limitation to truly represent thermal comfort of individual occupants. A variety of factors affect the thermal comfort such as dry-bulb air temperature, humidity, air movement, radiation, clothing insulation and metabolic activity level. Method: In this circumstance, this study investigated the effects of MET (metabolic rate) on thermal load and PMV, which is the thermal comfort index considering all the comfort factors listed above. Four cased were simulated using EnergyPlus: Case1: 0.7 (Sleeping), Case2: 1.0 (Seated, quiet), Case3: 2.0 (Walking), Case4: 2.3 (Fast walking, Dish washing). Result: It turned out that indoor air temperature in MET 0.7 Case can be even 10℃ higher than that in MET 2.3 Case to accomplish the same PMV in the summer period and that MET has dominant effects on heating and cooling load in residential buildings.

The indoor thermal environment of rural school classrooms in Northwestern China

School classroom thermal environments could impact on thermal comfort and learning performance. Currently, the majority of research on the school indoor thermal environment has been focusing on urban areas, but notably little research has been conducted on rural schools. A field study was undertaken during the winter in Northwestern China, in order to investigate the indoor thermal environment and the impact on students’ thermal comfort and learning performance in rural primary and secondary schools. Through subjective surveys and objective measurements, we gathered 763 sets of data and questionnaires. Together with the measured air temperature, relative humidity, air velocity, globe temperature, teenagers’ activity levels and clothing insulation levels, the sensations of the indoor air conditions and the learning performance were evaluated. The current thermal environment situation in rural schools was identified. The neutral temperature was found to be approximately 15.0℃ with an average clothing insulation of 1.6 clo. Overall, the environment is satisfactory and comfortable. Moreover, the results from the students’ fatigue testing demonstrated the temperature corresponding to the highest learning performance is lower than the actual neutral temperature by about 1.0℃. The basic parameters of our findings have provided a future reference for improvement of the thermal environment in rural schools.

Revisiting an overlooked parameter in thermal comfort studies, the metabolic rate

Human metabolism is a fundamental and important aspect of thermal comfort prediction. However, to date, most previous thermal comfort studies tended to treat metabolic rate as a constant value that only depends on activity level without consideration of ambient thermal conditions. In this paper, we explore the impacts of indoor thermal conditions on occupants’ metabolic rate and try to describe such impacts in a straightforward way. Climate chamber experiments, including both physiological measurements and subjective questionnaires, were conducted at temperatures ranging from cold to hot with two clothing insulation levels. The results indicate that human metabolic rate can be significantly affected by thermal conditions such as ambient temperature and clothing insulation. The changing trend of metabolic rate can be expressed as a second-order polynomial equation with actual thermal sensations or other predicted values. Generally, a neutral thermal sensation corresponds to the lowest metabolic value, while the metabolic rate increases when thermal sensation departs from neutral to the cold or warm side. For instance, the metabolic rate at a sensation of ‘cold’ is 16.6% higher than it is in ‘neutral’ sensation. These findings, especially the modified equations describing metabolic rate changes, can serve as useful reference for future thermal comfort studies.

Behavioural responses to cold thermal discomfort

Heating energy demand in buildings depends in part on occupants' behavioural responses to thermal discomfort during the heating season. The understanding of this has become one of the priorities in the quest to reduce energy demand. Thermal comfort models have long been associated with occupants' behaviour by predicting their state of thermal comfort or rather discomfort. These assumed that occupants would act upon their level of discomfort through three types of response: mechanisms of thermoregulation, psychological adaptation and behavioural responses. Little research has focused on the behavioural aspect. One of the key challenges is to gather accurate measurements while using discreet, sensor-based, observation methods in order to have minimum impact on occupants' behaviour. To address these issues, a mixed-methods approach is introduced that enables the establishment of a three-part framework for mapping behaviour responses to cold sensations: (1) increasing clothing insulation level; (2) increasing operative temperature by turning the heating system on/up; and (3) increasing the frequency, duration and/or amplitude of localized behaviour responses such as warm drink intake or changing rooms. Drawing on this framework, an extended model of thermal discomfort response is introduced that incorporates a wider range of observed behaviours.

Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures

Clothing affects people's perception of the thermal environment. Two dynamic predictive models of clothing insulation were developed based on 6333 selected observations of the 23,475 available in ASHRAE RP-884 and RP-921 databases. The observations were used to statistically analyze the influence of 20 variables on clothing insulation.The results show that the median clothing insulation is 0.59 clo (0.50 clo (n = 3384) in summer and 0.69 clo (n = 2949) in winter). The median winter clothing insulation value is significantly smaller than the value suggested in the international standards (1.0 clo). The California data (n = 2950) shows that occupants dress equally in naturally and mechanically conditioned buildings and all the data has female and male dressing with quite similar clothing insulation levels. Clothing insulation is correlated with outdoor air (r = 0.45) and indoor operative (r = 0.3) temperatures, and relative humidity (r = 0.26). An index to predict the presence of a dress code is developed.Two multivariable linear mixed models were developed. In the first one clothing is a function of outdoor air temperature measured at 6 o'clock, and the second one adds the influence of indoor operative temperature. The models were able to predict 19 and 22% of the total variance, respectively. Climate variables explain only a small part of human clothing behavior; nonetheless, the predictive models allow more precise thermal comfort calculation, energy simulation, HVAC sizing and building operation than previous practice of keeping the clothing insulation values equal to 0.5 in the cooling season and 1 in the heating season.

Influence of weather and indoor climate on clothing of occupants in naturally ventilated school buildings

Adaptive thermal comfort standards hold the potential for energy savings and greenhouse gas emission reductions in naturally ventilated buildings. The potential for energy savings relies on the fact that applying adaptive standards, indoor comfort temperature is shifted up on warmer days and shifted down in colder ones. In order to determine the correct comfort temperature inside, two variables need to be known in advance: The clothing insulation level and the metabolic activity of the inhabitants. Using an adult population, it was observed that the clothing insulation level can be calculated based on recent thermal memory and weather prediction. The field study was carried out with 732 individuals for one year. All individuals performed the same task. In these circumstances, the clothing insulation level revealed to be a key issue. The clothing insulation level of the population was determined by the clo-checklist method and showed significant variation along the time period, with standard deviation representing about 23% of the mean clo (1 clo = 0.155 m2 K W−1). Results showed that the clothing insulation level worn inside has the most significant relationship to the previous day’s average outside temperature (Tday.x−1: R2 = 88%) followed by the maximum outside temperature during that day (maxTday.x: R2 = 71%).