Assessment of student thermal comfort perception of spaces in education building with different vent

Abstract

Keywords: Educational Buildings; Heat Balance and Adaptive Thermal Comfort Model; Questionnaire Based Survey; Statistical Models; Thermal Sensation

Introduction

With the effect of the pandemic (COVID-19), improving indoor air quality is now of greater importance for user comfort. Indoor Environmental Quality (IEQ) can be achieved with adequate thermal comfort, indoor air

quality, visual and acoustic comfort [1]. Building designers are required to achieve the IEQ with the lowest possible energy use. The relationship between thermal comfort and energy consumption is critical for providing a comfortable and efficient living environment in buildings [2]. Heating,

*Corresponding author. *E-mail address: resulozluk7@gmail.com This paper was recommended for publication in revised form by Editor-in-Chief Ahmet Selim Dalkılıç Published by Yıldız Technical University Press, İstanbul, Turkey Yıldız Technical University. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

cooling and ventilation systems generally provide thermal comfort, but these systems consume significant amounts of energy [3, 4]. Strategies are needed to decarbonize the building stock by improving building energy performance without compromising occupant comfort [5]. Changes in local climatic conditions and temperature increases are observed due to energy consumption and increased emissions. Temperature increases obstruct thermal comfort, and air conditioning is generally considered the leading solution. Therefore, developing innovative ventilation and HVAC systems solutions is essential in sustainable building design [6]. In this way, both energy savings can be achieved, and the comfort of building users can be increased [7]. IEQ affects not only health and comfort but also building occupants’ working and learning efficiency. In educational buildings, students and instructors spend most of their time in classrooms, studios, laboratories, etc. Therefore, investigating the thermal comfort of the school building is an important research topic. In addition, a significant part of the energy demand is related to indoor thermal comfort. Financial and energy restrictions, especially in school buildings, can harm students’ learning performance and health [8]. ISO 7730 [9] defines thermal comfort as a “state of mind that expresses satisfaction with the thermal environment” ASHRAE Standard 55 [10] defines thermal comfort as a state of mind that expresses satisfaction with the thermal environment and is determined by subjective evaluation. In addition to this definition, Szokolay [11] defines it as a state of mind that requires subjective evaluation. These definitions include factors other than physical or physiological characteristics. Thermal comfort varies depending on user activities, emotional states, personal and environmental factors [12]. Six parameters are used in the assessment of indoor thermal comfort. These parameters are divided into two: environmental and personal. The range of indoor comfort parameter values is defined in categories in EN 15251 [13]. Environmental factors: Air temperature is essential as it determines convective heat dissipation [14]. Air movement accelerates convection and creates a cooling effect as it increases evaporation from the surface. Air humidity has a negligible effect on thermal comfort, but high humidity restricts evaporation from the skin, while too low humidity causes the skin to dry out. The mean radiant temperature is the average temperature of all visible surfaces within the room from a user’s point of view [1]. Personal factors: Clothing is the thermal insulation of the body and is measured in clo units [15]. The metabolic rate depends on factors such as food, drink, body shape, etc., and may impact thermal preferences due to the body’s heat production effect [11]. Various studies on thermal comfort have been carried out in different countries of the world. Jiang et al. [16] analyzed the thermal comfort levels of students in controlled and uncontrolled environments during the winter season. It created an adaptive thermal comfort model based on students’ behaviors affected by the external thermal environment.

Merabtine et al. [17] studied thermal comfort for students of different age groups in the foyer area and the energy consumption of a two-story educational building. Pereira et al. [18] evaluated thermal comfort and indoor air quality in a school building. The results of PPD and PMV values were verified by comparing them with the data obtained from the survey. In his study of the indoor environment quality of different schools, De Giuli et al. [19] conducted a study on the indoor environmental quality of different schools. They found that students were not satisfied with the indoor conditions during the summer months. Aparicio-Ruiz et al. [20] investigated the thermal comfort of three classrooms in a school building. The study shows that the different models applied are inconsistent, and different strategies should be developed. Dorizas et al. [21] found that most students in different schools during the spring semester preferred a cooler environment, according to experimental measurements. Corgnati et al. [22] concluded that students prefer warm classrooms in winter and a more neutral indoor environment in other seasons when it is less cold. Similarly, Verma et al. [23] observed that there were slight differences in the values of experimental and survey data. Shrestha et al. [24] conducted a study on the thermal sensation of students in school buildings. The study shows that most students adapt to the ambient conditions and feel comfortable. Rodríguez et al. [25] addressed physiological and psychological variables in addition to physical and environmental factors. They concluded that little relationship exists between temperature, thermal sensation, and thermal comfort preferences in studying students of different age ranges. Similarly, Katafygiotou et al. [26] assessed indoor thermal conditions. The study found that air temperature and relative humidity were often insufficient for spaces. Energy consumption has been increasing over time due to developments in the sectors. For this reason, the building’s energy consumption and thermal comfort must be at optimum values with the proper efficiency studies. Kükrer and Eskin [27] found an increase in annual energy consumption while reducing user dissatisfaction. Hwang et al. [8] used different methods to evaluate different design parameters of the building envelope. Their study proposed school building envelope design criteria for balancing energy use and thermal comfort. Park et al. [28] analyzed the energy performance and thermal comfort of an educational building with a shading system. They concluded that cooling energy consumption decreases and thermal comfort increases when phase change material is applied to the shading system. Yu et al. [29] and Huh and Brandemuehl [30] used a multi-objective optimization to balance the energy use and thermal comfort of a building. However, Taylor et al. [31] found that more than 10% of users were dissatisfied when energy efficiency was achieved in classrooms using a multi-objective optimization. Li et al. [32] concluded that using RSM (Response Surface Method) for the optimal combination of different parameters in a

school building results in energy savings of about 4% and increased environmental comfort. In recent years, researchers from different parts of the world have carried out various studies on thermal comfort in educational buildings. Table 1 lists studies on thermal comfort in educational buildings over the past 20 years. This table contains information about the level of education in the studies, the country’s location, the seasons, the age range, the ventilation type, the number of surveys and the number of schools [33-50]. While many existing studies have examined thermal comfort and indoor air quality, few have examined changes in comfort expectations in spaces with different ventilation strategies post-COVID-19. While previous research often focused on a single system type, this study provides more comprehensive information across naturally ventilated and air-conditioned classrooms. Furthermore, the study makes a unique contribution by considering the thermal comfort of students from

various climatic regions of Türkiye. This emphasizes the influence of regional habits on thermal comfort. The primary purpose of this study is to determine the thermal comfort feeling of students by examining the thermal comfort conditions of spaces with different ventilation strategies in both winter and summer after COVID-19. We consider different spaces of a university building with both an objective and subjective approach. In addition, the students’ thermal sensation results were compared with the thermal comfort conditions of different regions where they had previously lived and got used to. Questionnaires constitute a subjective approach to thermal comfort, while the measurements constitute an objective approach based on the standards EN ISO 7730 and ASHRAE-55. In addition, the environmental conditions were evaluated by comparing their compliance with ASHRAE 55 and EN 15251 standards. This article seeks answers to two primary research questions (RQ): Do spaces with different ventilation strategies

Table 1. Previous thermal comfort research on classrooms in education buildings Level

KG, PE, U 2016

Note: NV - natural ventilation; AC - air conditioning, U- university; PE- primary education; SE- secondary education; KG-kindergarten.

after the pandemic comply with comfort standards when thermal comfort is evaluated (RQ1)? Moreover, does the effect of the climate region where students from different regions of the country live on user thermal comfort differ (RQ2)? To address these research questions and achieve the objectives, the remainder of this article is organized as follows: The first section explains the method section, which includes thermal comfort models, Balikesir’s climate, a description of the building, field measurements and instruments used for the survey and survey questions. The next chapter includes the findings and discussion section explaining the indoor and outdoor environment, subjective evaluation, evaluation of the cultural influence, predicted mean vote and thermal sensation vote and adaptive thermal comfort model results. Finally, the article concludes by discussing the study results and recommendations.

subjectively and experimentally in the selected spaces. As a result of these analyses, statistical models of the study were created, and the study results were compared (Fig. 1). Thermal Comfort Models Detailed research on thermal comfort has been conducted for several decades. Two models are used to evaluate thermal comfort: the Heat balance model and the Adaptive thermal comfort model developed by Fanger in 1970 [51]. These models form the basis of current thermal comfort standards. The first model is suitable for air-conditioned buildings where users have no control. The heat balance approach was developed by Fanger, which produced the predicted mean vote (PMV) and predicted percentage dissatisfied (PPD) model. PMV can be determined by the Equations 1 and 2 [51].

Methodology

In evaluating the user thermal comfort of different spaces in the selected university building, surveys, heat balance (PMV/PPD), and adaptive comfort model methods were used. These methods are two common approaches used in current comfort standards. When we examined the flow chart of the study, the literature review and commonly used thermal comfort models were first examined. Afterwards, the spaces belonging to different ventilation strategies to be examined in the study were determined. Survey questions, experimental equipment and area measurements were applied to evaluate user thermal comfort

Where M is metabolic rate of the occupant (W/m2 the body surface area), W is mechanical power by the occupant, fcl is the surface area of the body with clothes, tcl is clothing surface temperature (°C), Ta is temperature of air (°C), tr is mean radiant temperature (°C), Pa is partial pressure of water vapour (Pa). The PPD equation, which is a function of PMV, is calculated as follows: (3) Fanger defined the PMV scale between cold (mark −3) and warm (mark +3). At this scale, ideal thermal comfort conditions are achieved with PMV equal to zero. Limit values are defined in ISO 7730 [9] and EN 15251 [13] standards. These values are presented in Table 2.

Table 2. The classification is based on ISO 7730 and EN 1525 standards Category

Figure 2. Adaptive thermal comfort graph according to ASHRAE standard 55–2017 [10]. The adaptive thermal comfort model is most suitable for naturally ventilated buildings and interacting with the environment. In ASHRAE 55–2017, this model defines acceptable thermal environments for areas where no mechanical cooling system and no heating system are working; users have metabolic rates ranging from 1.0 to 1.3 met and clothing insulation in the 0.5 to 1.0 clo range. The adaptive thermal comfort model is a linear regression equation directly relating the indoor comfort temperature in Equations 4 and 5 to the outdoor temperature. Acceptable indoor operating temperatures will be determined from Figure 2 using 80% acceptability limits [10]. The following equations corresponding to the acceptable operative temperature in accordance with the ranges in Figure 2 are used [10]: Upper 80% acceptability limit Tc (°C) = 0.31Tpma(out) + 21.3 (4) Lower 80% acceptability limit Tc (°C)= 0.31Tpma(out)+ 14.3

Where Tc is indoor comfort temperature, Tpma(out) is the prevailing mean outdoor dry bulb temp (°C). For this study, the calculation of the operative temperature (To) is shown in Equation 6: (6) Where To is the Operative temperature, Ta is the air temperature, and Tr is the mean radiant temperature. A is a parameter estimated from the relative air velocity. Values depending on the air velocity of parameter A are given in Table 3 [48]. Table 3. Estimation of (A) parameter for mean radiant temperature calculation Velocity (m/s)

Research Findings And Discussion

Indoor and Outdoor Environment HOBO data loggers were used for outdoor data, and Testo 480 instruments were used for indoor data. All received data were transferred to an Excel table in a computer environment. The collected data were analyzed using Python and statistical software (IBM SPSS 26 and Minitab). In addition, calculations of thermal comfort indicators (PMV and PPD) were taken from the Testo 480 instrument. In this study, the environmental parameters and thermal comfort indicators collected for the summer and winter months of the classroom, design studio and amphitheater are shown in Table 7. Table 7 shows the statistical results of the measured values (mean, maximum, minimum, standard deviation, standard uncertainty, and normalized standard uncertainty) for the different spaces. Within the scope of this study. experimental measurements were made to evaluate the users’ thermal comfort. Many different devices were used during the measurements, and uncertainty analysis is necessary for the accuracy of the measurement results. The uncertainty of a measurement result reflects the need for more precise knowledge of the value of the measured substance. Uncertainty is measured by the standard deviation of the measurement (Equation 9). The standard deviation measures how far the values deviate from the mean (average), while the Standard Uncertainty (Standard Error of the Mean) measures the average

(10) Where x− is the mean, xi is the individual measurements, s is the standard deviation, µ is the standard uncertainty of the mean, and n is the number of measurements. Table 7 shows that there are differences between the measurements of the venues. The Normalized Standard Uncertainty (µnorm) gives a relative measure of uncertainty that can be useful for comparing and interpreting uncertainties between measurements. For example, in space C1, the Normalized Standard Uncertainty value of the indoor temperature measurements was calculated to be 1.9%. This value indicates that the uncertainty is approximately 1.9% of the measured value, and the smaller this value is, the higher the precision of the measurement. Analyzing the measurements, it can be seen that the air velocity measurement has the highest uncertainty. Similarly, Ribeiro et al. [58] performed an uncertainty analysis of PMV and found that airspeed measurement is the primary source of uncertainty in PMV calculations. Due to COVID-19, the distribution of various parameters, such as indoor air flow rate and indoor temperature, were different, especially in naturally ventilated classrooms. “Opening windows” was a joint action used by users in naturally ventilated classrooms. This

Figure 6. The relationship between the Top and other environmental parameters.

situation has been an essential factor in the change in the average air speed in the classrooms. Especially in the summer, the high temperature and humidity make the users feel more comfortable thanks to the increased airspeed. In addition, the correlations between Top and the values of environmental parameters such as Ta, To, RH, Va and Tg were examined during the measurements (Fig. 6). Significant correlation values were found, especially between Top, Tg, and Ta. By examining correlations, it becomes easier to predict changes in thermal comfort based on changes in environmental parameters. For example, suppose an increase in outdoor temperature is strongly associated with an increase in operative temperature. In that case, steps can be taken to reduce the impact of hot weather on indoor comfort. Correlations between operative temperature and other environmental parameters provide essential data for optimizing comfort conditions. In addition, due to the high correlation coefficient of Top, Top can be selected as an independent variable for adaptive comfort models, which is commonly used in international standards [59]. Subjective Evaluation The distribution of thermal sensation votes (TSV) according to the number of students is shown in Figure 7,

respectively. By the ASHRAE seven-point scale, the students were asked, “How do you feel the warmth of the environment you are in?” TSVs were calculated by asking the question. The data were evaluated separately for both summer and winter months for students in the classroom (C1), design studio (C2) and lecture hall (C3). For the winter TSV results of C1 space, 99 (70.2%) out of 141 questionnaires were in the comfort range (between -1 and 1). For the summer, 77 (84.7%) of 91 surveys were in the comfort range. C2 space was in the comfort range in 69 (84.3%) of 80 questionnaires for winter and 49 (92.4%) of 53 questionnaires for summer. Similarly, 121 (63%) of 192 questionnaires for the winter month and 65 (83.3%) of 78 questionnaires for the summer questionnaires were within the comfort range. The distribution of collected thermal preference votes (TPV) according to the number of students is shown in Figure 8. TPVs of students; “What kind of temperature change do you prefer during your time in the environment?” It was calculated with the question. For the three different spaces, in winter, most students indicated that they preferred “No change” and their environment to be warmer. In summer, on the other hand, students preferred their environment to be cooler. Looking at the relationship between TSV and TPV, 32% of the students

Figure 7. Distribution of thermal sensation responses during the field study.

Figure 8. Distribution of thermal preference responses during the field study.

preferred “No change” for their “Neutral” thermal senses in winter; 28.6% of the students who identified as “Slightly cold” preferred “Slightly warmer” conditions, 16% of the students who identified as “Cold” preferred “Warmer” conditions, and 6% of the participants who identified as “Very cold” preferred “Much warmer” conditions. For “Neutral” thermal sensations in summer, 41% of the students preferred “No change”; 31% of the students who identified as “Slightly warmer” preferred “Slightly warmer” conditions, 8.6% of the students who identified as “warmer” preferred “Cooler” conditions, and 2% of the participants who identified as “Very cold” preferred “Much warmer” conditions. Statistical characteristics of thermal sensation and thermal preferences for different spaces are given below (Table 8). Overall, Table 8 shows that the average thermal sensation rating for space C2 indicates that most of the students felt comfortable and did not prefer an environment that was too different from their current environment. For spaces C1 and C3, the average thermal sensation rating indicates

that students felt a little uncomfortable but did not prefer an environment that was too different from their current environment. In addition, the average AVS and HS ratings in C1, C2, and C3 are close to neutral, indicating that students feel comfortable in the environment. In this study, thermal acceptability is the question “What is the temperature of your environment?” measured from the answers given to the question. In the C1 environment, approximately 65% of the students feel comfortable in the winter and 95% in the summer. In the C2 environment, approximately 85% of the students feel comfortable in the winter and 34% in the summer. In the C3 environment, approximately 48% of the students feel comfortable in winter and 65% in summer (Fig. 9). The low thermal acceptability, especially in the C2 summer period, maybe because it is located on the top floor. Because it is known that the building envelope is in direct contact with the sun’s rays in the summer months.

Table 8. Descriptive statistics of subjective thermal sensation and preference variables Variables

Figure 9. Distribution of acceptability responses during field study.

Figure 10. Distribution of the factors that most influenced students during the measurements.

The factor affecting thermal comfort, “Which factor do you think is the most affecting you in your environment?” It was measured from the answers given to the question (Fig. 10). In general, temperature is the factor that most affects thermal comfort for all places and seasons. This result reveals the importance of the temperature factor in determining thermal comfort perception. Evaluation of the Regional Influence Balikesir, partly in the Southern Marmara Region and partly in the Aegean Region, has coastlines on both the Marmara and Aegean Seas. For this reason, the number of students coming from the Aegean and Marmara Regions is higher than the other regions. Using the ArcGIS program, the density analysis of the students according to the regions was performed, and then the average TSV values of both terms were shown on the map (Fig. 11). According to the average TSV values of the winter period, students in regions where the winter season is generally cold are more satisfied with their environment. It was concluded that students from the Marmara Region, especially in the

summer period, were more dissatisfied with the environment and preferred a cooler environment. In this study, the effect of the regional factor on thermal comfort is more pronounced, especially in the winter period. Predicted Mean Vote and Thermal Sensation Vote In order to compare TSV and PMV values, the operative temperature range of C1, C2 and C3 environments was considered. Operative temperature is an important method used for thermal comfort analysis [60, 61]. Table 9 shows the regression model of the mean values of PMV and TSV at indoor operative temperatures for both summer and winter periods. In addition, the regression models, correlation coefficients (r2) and comfort temperatures (Tn) of TSV and PMV for all periods are given in Table 9. PMV and TSV were the dependent variables, and operative temperature was the independent variable. The significance value of the results was taken as p<0.05. Neutral or comfort temperature is the operative temperature calculated when PMV and AMV values are zero. Neutral temperature is

Figure 11. Density and average TSV values of the students participating in the survey by region.

Table 9. Regression models, neutral temperature and coefficient of determination for all periods Winter Regression models

normal in terms of thermal comfort for the winter period. The survey results show that the students want a somewhat cold environment (Table 8). Similarly, López-Pérez et al. [62] calculated a neutral operating temperature of 25.6°C in naturally ventilated spaces, outside the recommended range according to ISO 7730. C2 is for winter (Fig. 13(c)); the PMV model shows sensations similar to actual voting for operative temperatures. Students are more sensitive to the indoor environment than the PMV model. The Tn temperatures measured and calculated according to the survey results were 22.3°C and 23.8°C, respectively. The calculated values are by the standards. Similarly, survey results confirm that students would like a slightly warmer environment (Table 8). That is, current temperatures meet participant preferences. C2 is for the summer period (Fig. 13(d)). It is seen that the AMV and PMV graphs intersect when the operative temperature is between 26–28°C. The PMV model predicts colder sensations for operative temperatures than the actual rating. Students are not as sensitive to the indoor environment as PMV predicts. The Tn temperatures measured and calculated according to the survey results were 23.4°C and 41.6°C, respectively. The measurements have an extensive comfort range, and the calculated values do not meet the comfort conditions. However, the survey results show that students feel a little warmer (Table 8). In this case, current temperatures remained high according to participant preferences. These findings are consistent with those of LópezPérez et al. [62]. Similarly, Jindal [47] conducted a study on the PMV and PPD scale for students of different age groups. He concluded that the PMV and PPD models failed to predict the thermal sensations of students in naturally ventilated classrooms. Papazoglou et al. [48], in their research on the sensation of thermal comfort in students of different age

taken as “slightly cold”, “neutral”, or “slightly warm” on the ASHRAE scale. Quantifying the subjective and empirical data collected is always a challenge. Larger data sets may be needed to obtain a more concrete regression analysis. When we look at the studies in the literature, the sample size varies between 19 and 2990. C1 for the winter period (Fig. 12(a)); It is seen that the AMV and PMV graphs intersect when the operative temperature is between 20–21°C. After the operative temperature intercepts, the AMV graph rises steadily and predicts students’ thermal sensation better than the PMV graph. The PMV model predicts colder sensations for operative temperatures than the actual rating. Students are not as sensitive to the indoor environment as PMV predicts. The Tn temperatures measured and calculated according to the survey results were 23.6°C and 31.8°C, respectively. Operative temperatures should be between 20°C and 24°C for the winter period according to ISO 7730. The calculated temperature was outside the standard range. According to the survey results, the students’ thermal perceptions are ‘cold’, and the indoor conditions are unsuitable for thermal comfort during winter. The survey results show that the students want a warm environment (Table 8). For the C1 space summer term (Fig. 12(b)), It is seen that the AMV and PMV graphs intersect when the operative temperature is between 26–28°C. It is seen that the slopes of AMV and PMV are in opposite directions after the operative temperature intersects. The measured temperature is different from the students’ thermal perception. The Tn temperatures measured and calculated according to the survey results were 24.6°C and 32.4°C, respectively. Operative temperatures should be between 23°C and 26°C for the summer period according to ISO 7730. The calculated temperature was outside the standard range. According to the survey results, the students’ thermal perceptions are ‘’a little warm,’’ and the indoor conditions are

Figure 12. Observed thermal sensation votes and predicted thermal sensation votes for C1.

Figure 13. Observed thermal sensation votes and predicted thermal sensation votes for C2.

Figure 14. Observed thermal sensation votes and predicted thermal sensation votes for C2. ranges in a school building, concluded that there were differences between experimental data and survey results. C3 for the winter period (Fig. 14(e)); It is seen that the AMV and PMV graphs intersect when the operative temperature is between 20–22°C. The PMV model shows similar sensations to actual voting for operative temperatures. The Tn temperatures measured and calculated according to the survey results were 23.3°C and 23.4°C, respectively. The calculated values are by the standards. Similarly, when the survey results are taken into account, it is seen that the students want a somewhat warm environment (Table 8). That is, current temperatures meet participant preferences. C3 space for the summer period (Fig. 14(f)); the PMV model predicts colder sensations for operative temperatures than the actual rating.

The Tn temperatures measured and calculated according to the survey results were 25.3°C and 21.86°C, respectively. The calculated temperature was outside the standard range. However, according to the survey results, the students’ thermal perceptions are ‘a little warm’, and they want a ‘a little cold’ environment (Table 8). López-Pérez et al. [62] showed that in the space with AC mode, the linear regression line has a higher slope than expected for the summer season. They confirmed that the increase in the slope according to the TSV scale is due to the high proportion of people dissatisfied with the environment. Adaptive Thermal Comfort Model With the adaptive comfort approach, building users can adjust physiologically, psychologically and behaviorally according to environmental conditions [63]. In ASHRAE 55–2017, the Adaptive thermal comfort model is applied

Figure 15. Acceptable operative temperature ranges for naturally conditioned C1 and C2 spaces [10]. for areas that are controlled by the user, naturally conditioned, where the mechanical cooling system and heating system do not work, the users have metabolic rates ranging from 1.0 to 1.3 met, and the clothing insulation is between

Conclusion

This study conducted a thermal comfort field study for three different spaces in a university building with different ventilation strategies in both summer and winter. A questionnaire-based study was conducted to collect subjective and objective data from building occupants. The study results were statistically analyzed using both PMV and adaptive comfort models based on the thermal comfort standards of the collected environmental parameters and questionnaires. Using adaptive strategies in naturally ventilated environments during summer makes students feel more comfortable and improves indoor air quality. During the study period, students’ thermal preferences in all classes were slightly cooler in summer and slightly warmer in winter. There were differences in the acceptability of the

comfort level of the student’s environment. Especially in the C2 class summer period and the C3 class winter period, the comfort level needed to be at the desired level. In addition, it was confirmed that the effect of the cultural factor on thermal comfort is more pronounced, especially in the winter period, and students from colder regions feel warmer in the environment. Another important finding is that temperature is the most important factor affecting student comfort. In summary, the study presented here shows that the thermal sensations of the students and the measured experimental data differ in some periods. Students’ thermal sensations are within an acceptable comfort range. In this study, students’ thermal comfort temperature range in naturally ventilated spaces fits the upper and lower limits of the Adaptive comfort model. However, adjustments should be made to accurately reflect their thermal sensations, especially for the neutral temperature range. There has been a significant increase in the number of educational buildings and students in Türkiye, especially in recent years. Therefore, it is predicted that quality education and better IEQ will significantly increase student performance. Professional groups, such as architects, engineers, etc., must show interest in this issue. This interest may contribute to improving knowledge in the field of indoor environmental quality for future studies and designing healthier and more comfortable educational environments for students.

Air velocity of parameter (m/s). Classroom. Design Studio Lecture hall Thermal sensation vote. Thermal preference vote. Thermal acceptability. Air velocity sensation. Humidity sensation. Actual Mean Vote. Actual Percentage Dissatisfied. Standard Deviation. Standard Uncertainty. Normalized Standard Uncertainty. Neutral (comfort) temperature (°C). Coefficient of Determination. Running mean temperature (°C).

Data Availability Statement

The authors confirm that the data that supports the findings of this study are available within the article. Raw data that support the finding of this study are available from the corresponding author, upon reasonable request.

Ashrae

American Society of Heating, Refrigerating, and Air-Conditioning Engineers. CO2 Carbon Dioxide. IEQ Indoor Environmental Quality HVAC Heating, Ventilation, and Air Conditioning. Clo Clothing insulation. ISO International Organization for Standardization. RSM Response Surface Method. PMV Predicted Mean Vote. PPD Predicted Percentage of Dissatisfied. NV Natural Ventilation AC Air Conditioning System. U.- university PE- primary education; SE- secondary education; KG-kindergarten. M Metabolic rate of occupant (W/m2 the body surface area). W Mechanical power by the occupant. fcl Surface area of the body with clothes. tcl Clothing surface temperature (°C). Ta Temperature of air (°C). tr Mean radiant temperature (°C) Pa Partial pressure of water vapour (Pa). Met Metabolic rates. Tc Indoor comfort temperature. Tpma(out) Prevailing mean outdoor dry bulb temp (°C). Top Operative temperature (°C). Ta Air temperature (°C).

Conflict Of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethics

There are no ethical issues with the publication of this manuscript.

Share and Cite