Introduction
Study Methodology
Case-study buildings description
Field survey through questionnaire
Data collection through instruments
Analysis
Thermal comfort parameters
Temperature
Relative humidity
Clothing
Adaptive control measures
Comfort votes
Residents’ overall satisfaction
Actual Mean votes (AMV), Actual percentage dissatisfied (APD), Predicted mean votes (PMV), Predicted percentage dissatisfied (PPD)
Establishing the relationship between Physical environmental and Personal variable dataset
Results and Discussion
Obtaining the comfort range and thermal neutrality
Relationship between indoor temperature as a function of outdoor temperature
Discussion
Conclusion
Introduction
People spent the majority of their time within the buildings, and once when their indoor thermal environment is unsatisfactory, it not only causes a risk to their health but also hinders their ability for efficient mental and physical functioning [1, 2]. According to several studies [3, 4, 5], creating a comfortable indoor environment could enhance residents’ well-being in their residences. Thermal comfort is defined by BS EN ISO-7730 as “that condition of mind which expresses satisfaction with the thermal environment”, i.e. the state in which a person does not feel too hot or cold. A subjective state of mind where ideally an individual feel comfortable with their indoor environment has been defined as thermal comfort [6]. Thermal comfort is achieved through climate-responsive design and building materials [7].
To assess the thermal comfort range for buildings, there are two approaches used, depending on the functions, users, climate, and the building’s heating and cooling systems [8]. They are the field studies, which involve experiments carried out with regular activities and clothing in the actual world, and the rational studies, which involve experiments carried out in climate-controlled rooms. The first thermal comfort equation, created by P.O. Fanger in 1967 [9] led to the development of the “Predicted Mean Votes- Predicted Percentage Dissatisfied (PMV- PPD) comfort model”, that was adopted by the ASHRAE 55 [9]. The research also established the use of the seven-point temperature feeling scale and the same is used in this study, as depicted in Figure 1.
Various studies [10, 11, 12] evaluated the thermal comfort of buildings with distinct cases by using the PMV-PPD comfort model. Alessandra Donato et al. [13] performed research to demonstrate a predicted analysis of an Italian historic residence’s energy performance analyzing with thermal comfort range defined using the PMV-PPD model. By comparison with simulated results, it was determined that the retrofitting of the residential building ensured a better level of thermal comfort for users. Design values for indoor temperatures are provided by international standards including the National Building Code (NBC) [14], ISO 7730 [6], ASHRAE 55 [9] and SP41 [15] depending on either of the two models that are the heat balance models such as PMV-PPD model, that relates thermal sensation to the thermal load on the human thermoregulatory system or the adaptive comfort model based on field studies that consider additional human adaptive factors in determining the thermal sensation that is nearly identical to indoor thermal load and explain discrepancies between predicted and actual thermal sensations in free-running indoor climates using the PMV-PPD indices [16].
Even though there is an abundance of mixed-mode residential buildings in the country, a few studies have been conducted on such buildings [17, 18, 19]. The comfort range for 50% relative humidity is 25°C to 30°C according to the Tropical Summer Index (TSI) is used in Indian standards [15], although this range may not be valid for other relative humidity levels. To establish guidelines for thermal comfort in naturally ventilated residences and office buildings in India, the Indian Model of Adaptive (Thermal) Comfort (IMAC) [20], defined a range for the conditions with relative humidity between 30-60% to be in between 20.5°C - 28.5°C. Comfort field studies in Chennai (tropical Savanna Climate-Aw) & Hyderabad (hot and semi-arid-Bsh) [21, 22], Jaipur (Hot Semi-Arid -BSh) [23, 24], suggested models for mixed-mode buildings that are adaptable to the respective local climate only.
Hamirpur, one of the 12 districts in the northern Indian state of Himachal Pradesh, as shown in Figure 2, situated at an average altitude of 790 meters above mean sea level, is the subject of this study [25]. This region falls within the ‘subtropical highland’ climate (Cwa) according to the Köppen climatic classification [26], as shown in Figure 3, with the average temperature in June, which is the hottest month, reaching up to 43.0°C and drops down to 3.0°C during the coldest month of January [27]. According to the census 2011, a major share of 61.73 % of residences is categorized to be permanent households in the entire district of Hamirpur [28].
Residential stocks of Hamirpur are classified as conventional and traditional houses in terms of several parameters such as the predominant material used for construction, number of stories, spatial design, plot sizes, and typology of residence [28, 30, 31]. The traditional houses have distinct climate-responsive features such as walls with high thermal mass made of Adobe or stones, to maximize solar exposure and protect against weathering agents, provision of solar passive features such as solariums and verandas, as well as Attics with wooden plank ceilings and pitched roofs with slate roofing on wood rafter serve as an insulating chamber. [25, 32]. While, conventional houses have elements such as over hangings, large openings, etc. provided based on historical experiences [25] but not consciously designed resulting in a poor thermal indoor environment [31]. It is seen that no studies have been conducted on mixed-mode buildings located in subtropical highland climate zone Thus, for residential buildings in this particular climate zone of India, determining a thermal comfort range is essential for providing guidelines to future establishments.
This paper aims to determine the thermal comfort range of 11 residences with both traditional and conventional architectural styles that are located in Hamirpur having a subtropical highland climate zone. Although these buildings have different passive design elements and architectural styles, they are functionally related and serve similar users.
Study Methodology
Through the methodology illustrated in Figure 4, the thermal study has been conducted to determine the thermal comfort range in the sample residences taken for this study.
Case-study buildings description
From the study area, a total of 11 residential buildings, out of which 6 conventional houses and 5 traditional houses were chosen for study, as shown in Figure 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. An on-site visit was conducted to determine the physical characteristics and passive design features. The summarized study of all the buildings is shown in Table 1:
Table 1.
Summary of study sample residences
Field survey through questionnaire
The survey was carried out over four months: summer (July 2022), monsoon (September 2022), winter (January 2023), and spring (March 2022), from 10 am - 12 pm, 12 pm - 3 pm, and 3 pm - 5 pm, three times a day. During this study, the residents’ responses has been collected using a survey questionnaire, and the environmental parameters of thermal comfort were measured instrumentally. One participant from each house was interviewed, among which 54.5% of them spent more than 8 hours and 45.5% of them spent more than 12 hours a day in their residence. Twelve questions, divided into five sections, were included in the survey questionnaire to understand the resident’s age, gender, exposure prior to getting into the space, activity, thermal sensation and preference as well as for humidity, adaptive measure to attain contentment, clothing insulation, and overall contentment.
To understand the resident’s perception of their thermal environment, the seven-point ASHRAE thermal sensation scale, which is shown in Figure 1, served as the instrument to obtain the occupants’ thermal sensation votes. In addition, the Nicol preference [33] scale as per Figure 16 was used to gather the occupants’ thermal preferences. The activity was categorized as “sitting,” (MET = 1). The residents were instructed to list the items of clothing they had, which was subsequently turned into the clo unit. The ASHRAE 55 [9] clo values were inadequate since they had no regard for the typical Indian clothing combinations. To address this issue, another research on Indian clothing values [20] was referenced and used in this study.
Data collection through instruments
Information on the outdoor and indoor environmental parameters such as Outdoor air temperature (To), indoor air temperature (Ti), and outdoor relative humidity (RHo), indoor relative humidity (RHi), airspeed (v), were gathered using a CE-certified and calibrated digital hand-held anemometer shown in Figure 17, such as airspeed (0.8 -30 m/s with a 0.01 m/s ± 02% accuracy), air temperature (-10°C - 60°C with a ±1.5°C accuracy), and relative humidity (0% to 99% with a ±5% accuracy at 20% to 80%). The globe temperature and outdoor air temperature were almost identical thereby it was not perceived individually. The device was held at a height of one meter and readings were obtained while the residents responded to the questionnaire. The results were then converted into mean values for easier processing. The obtained dataset was then used to calculate several comfort indices, including Actual mean votes (AMV) and Actual percentage dissatisfied (APD) along with predicted mean votes (PMV), predicted percentage dissatisfied (PPD), metabolic rate (MET) and mean clothing insulation.
Analysis
Thermal comfort parameters
The data from the field surveys undertaken across all buildings over four different months have been shown in Tables 2, 3, 4 and, 5.
Table 2.
Summary of thermal environmental parameters recorded values - July
Table 3.
Summary of thermal environmental parameters recorded values - September
Table 4.
Summary of thermal environmental parameters recorded values - January
Table 5.
Summary of thermal environmental parameters recorded values - March
Temperature
As depicted in Figure 18, the mean indoor air temperature (Ti) obtained across all residences had a maximum value of 32.6°C and a minimum value of 13.05°C covering a temperature range of 19.55°C among all buildings. In addition, the research also covers a range of 20.55°C for outdoor temperature, since the maximum Ti value is found to be 34.3°C and the minimum Ti value to be 13.75°C. The collected indoor air temperature data were subjected to an ANOVA test, which indicated that while there was a significant variance in the values for July, the variation in the mean indoor temperature of buildings in March was relatively low. As shown in Figure 19, 90.9 % of residents considered the temperature varying in the span of 21.05°C and 23.15°C (> 80%) during March as well as 81.81 % in the span of 25.95 and 28.35 degrees (> 80%) during September to be more acceptable than the temperature varying in the span of 13.05 °C and 15.6°C (< 80%) during the winter month of January and 29.65 °C and 32.6°C (< 80%) during the summer month of July.
Relative humidity
All the buildings in the study had a maximum mean indoor relative humidity value of 70.7% and a minimum mean interior relative humidity value of 42.5%, illustrated in Figure 20 spanning a range of 20.55%. The maximum mean outdoor relative humidity measured was 67.25%, while 36.9% was the lowest mean outdoor relative humidity measured, indicating that the research covers a span of 30.35%. The mean indoor relative humidity during March, September, and January had minimal variation, respectively, according to an ANOVA test on the recorded values. However, it is shown that the mean interior relative humidity of the residences varies significantly in July. Figure 21 shows that in March, the relative humidity range of 43.15 to 50.5 %was regarded by 90.9% of the population (acceptability > 80%) as acceptable. While 45.45 % of residents found the relative humidity range of 42.5% - 54.7% (acceptability> 80%) in July to be more agreeable as same as the range of 51.7%-59.1% (acceptability> 80%) in September than the range of 59.45% to70.7% (acceptability< 80%) in January.
Clothing
To maintain comfort throughout the different seasons, one of the crucial and obvious approaches that are also the most practical adaption procedure is to adjust clothing ensembles. The mean clo values across all of the buildings in the study; During July it is 0.21 clo, in September it is 0.26, in January it is 0.85, and in March, it is 0.55 clo respectively. The clo values varied slightly by 0.05 clo. from July to September and by nearly 0.6 clo from September to January and this was reduced by 0.3 in March, The correlation between clothing pattern and outdoor temperature is shown in Figure 22. The clothing value varies from 0.18 - 0.36 clo in July and September, 0.5 - 0.7 clo in March, and 0.7 - 0.99 clo in January, showing the existence of three unique clothing profiles.
Figure 22 demonstrates that the clothing patterns of the occupants of all buildings are nearly similar. Equations 1 and 2 illustrate how linear and polynomial regression was used to assess the dependence of clothing patterns across the study on the outdoor temperature.
clo = -0.0361To + 1.403 r2 = 0.8872
Equation 1. Clothing insulation (clo value) to outdoor temperature (To)(Linear relationship)
clo = -0.0004 (To)2 + 0.0151To + 1.1811 r2 = 0. 8907
Equation 2. Clothing insulation (clo value) to outdoor temperature (To)(polynomial relationship)
Also, Figure 22 illustrates that the upward curving of the polynomial curve is caused by clo values between 17°C - 30°C, demonstrating the occupants’ adaptive behavior. The variation in the highest and lowest mean outdoor temperatures with clo values in both July and September is 0.6°C and 0.05 clo, between September and January it is 0.2°C and 0.6 clo, and between January and March, it is 0.9°C and 0.3 clo. The ASHRAE 55 standard predicts a variation in clo value [9] to be 0.15 clo, however, for each 6°C temperature variation, the difference is anticipated to be 1 clo.
Adaptive control measures
The residents had access to air conditioning, space heaters, ceiling fans, moveable windows, doors, a few windows with curtains or blinds, and the ability to change their clothing as their adaptive measures to attain thermal comfort. Based on the survey, it was determined that during the summer and monsoon seasons, The most often used adaptive means of control were ceiling fans and operable windows, and that most residents of conventional homes also preferred air conditioning. During the winter, however, adjusting the number of clothing ensembles and using room heaters was the most popular approach. As shown in Figure 23, 90.9% (80%-100%) during March and 72.27% (80%-100%) of the occupants are satisfied during September with the adaptive control available to them. While only 9 % (80%-100%) of the occupants are satisfied during July while 81.8 %(60%-80%) of the occupants in July and 36.3 % (60%-80%) of the occupants in January were merely content with the adaptive control options that have access to them. It is observed that the dissatisfaction among the occupants is due to the available means of control being inadequate to adjust the indoor thermal environment to the desired range and the high energy consumption required during extreme summer and winter periods.
Comfort votes
Thermal preference votes (TPV) and Thermal sensation votes (TSV)
More than 80% of votes have to fall in the middle of the range (1, 0, -1) [9] for a given environment to be considered thermally comfortable. Table 6 shows the percentage of votes given by residents for Thermal Sensation in July, September, January, and March. With 34.3% of votes received in July and just 19.1% in January in the middle category of the sensation scale, it was obvious that the residents weren’t content in those seasons. Nonetheless, the number of votes in the middle category increased from 79.7% in September to 82.75% in March, indicating that the residents were content with their surroundings. Table 7 summarises the respondents’ votes for their preferred thermal environment.
Table 6.
Resident’s thermal Sensation votes (%)in each of July, September, January, and March
Table 7.
Resident’s thermal preference votes (%)in each of July, September, January, and March
During July (51.5%) and September (57.5%), the most of residents preferred the indoor temperatures somewhat slightly cooler, whereas in March (63.6%), they preferred it to be significantly warmer. In January, the majority of the occupants (66.6%) preferred the indoor temperature to be hotter. Even though a fair percentage of the resident also preferred the indoor environment to remain the same in March (33.3%) as well as in September (24.2 %). Figure 24 shows that while residents of all the buildings felt essentially neutral in March, the mean TSV was mostly warmer in September, while the votes in July were hotter and colder during January.
Residents’ overall satisfaction
When 80% or more of the residents are satisfied with their surroundings, the environment is deemed satisfactory. As indicated in Figure 25, only 36.36% of the total residents are satisfied >60% with their surroundings which increased to 54.54 % (>80%) in September and 100 % (>80%) in March. But this decreases to 54.54 % (>60%) in January. This demonstrates that the residents were content with their surroundings in March and September but dissatisfied with them in January and July. From July to September, as the mean outdoor temperature is lowered by 2.88°C and the average outdoor relative humidity rose by 3.67%, more residents reported feeling satisfied overall. Yet, between September and January, a 15.22°C mean outdoor temperature drop and a 2.06% increase in mean outdoor relative humidity level resulted in a decline in the percentage of satisfied residents. The percentage of satisfied increased to 100 % during March. Overall, it was observed that residents were more accepting of their environmental constraints in March than in the other seasons.
Actual Mean votes (AMV), Actual percentage dissatisfied (APD), Predicted mean votes (PMV), Predicted percentage dissatisfied (PPD)
Equations 3 and 4 demonstrate how the PMV and PPD were determined using the model established by P.O. Fanger that was further adopted by ASHRAE 55 [9]. The Resident’s TSV collected based on the thermal sensation scale is itself assumed as AMV for this study.
PMV = (0.303 e (-0.035M) + 0.028) L
Equation 3. The equation as per ASHRAE standard [9] to determine Predicted Mean Vote (PMV), where L stands for the body’s thermal load and M is the metabolic rate.
PPD = 100-95e –(0.03353PMV^4 +0.2179PMV^2)
Equation 4. Predicted Percentage dissatisfied (PPD) as a function of PMV [9]
Figure 26 demonstrates the predicted mean votes from Equation 3 and the AMVs of all Residences collected to represent the thermal sensation profile over outdoor temperature.
Every season of the year has its unique profile, as shown in Figure 26. While the thermal sensation ranges between -1 and +2.5 once the outdoor temperature changes between 28.15°C and 31.65°C in September and between +1 and +3 when it ranges between 31.4°C and 34.3°C during July, while in January, once the outdoor temperature varies from 13.75°C to 17.05°C, TSV changes in between -3 and -1and in between -2 and 1 when it changes between 21.1°C and 30.1°C in March. Additionally noted is that the PMV profile consistently resembles the AMV across all months.
The percentage of dissatisfied indicator reflects the percentage of those who experience discomfort or dissatisfaction in a demanding environment. Figure 27 illustrates its relevance to thermal sensation by taking into account the correlation of PMV-PPD to AMV- APD.
Figure 27 shows that the PMV-PPD graph underpredicts both the cooler and warmer sensations of the dissatisfied people. This shows that both warmer and cooler sensations are endured higher by the residents. Equations 5 and 6 are used to derive the relation between AMV-APD and PMV-PPD, respectively.
APD = 14.383 AMV2 – 1.1357 AMV+ 10.2306
R2=0.7876
Equation 5. AMV and APD Relationship
PPD = 10.724 PMV2 + 3.0475 PMV+ 16.012
R2=0.888
Equation 6. PMV and PPD Relationship
Establishing the relationship between Physical environmental and Personal variable dataset
The relationship between the different data sets that were obtained for the study has been examined using Pearson correlation. Table 8 depicts the relationships between indoor and outdoor air temperature, mean indoor temperature to thermal sensation, and thermal preference. Table 9 depicts the relationships between indoor humidity to thermal sensation. Table 10 analyses the relationship between mean TSV and indoor temperature to overall occupant satisfaction.
Table 8.
Relationship between mean indoor temperature (Ti) and outdoor temperatures (To), thermal preferences (TPV), and thermal sensations (TSV) in residences over all four seasons
Table 9.
Relationship between mean thermal sensations (TSV) and mean indoor relative humidity (RHi) in residences over all four seasons
Table 10.
Relationship of mean thermal sensations (TSV) and mean indoor temperature (Ti) with the overall satisfaction of the residents over all four seasons
The analyzed correlations in Table 8 show that throughout July and September, there is no significant correlation between indoor and outdoor air temperature. Whereas it shows significance during January and March which indicates the implementation of adaptive measures such as fans and operable windows by the residents to improve air movement thus significantly affecting the indoor thermal environment during July and September. The indoor temperature and the residents’ thermal preferences are significantly correlated between July and September whereas it decreases during January and March. Further analysis of Table 8 reveals that although there is a significant correlation between residents’ thermal sensation with indoor air temperature throughout July, September, and January, this relationship is weaker in March. This demonstrates that in March, factors other than temperature have an impact on how comfortable people are inside.
As indicated in Table 9, the relation between RHi and mean TSV was examined for determining the influence of humidity on the occupants’ thermal sensations. The analysis showed that RHi and mean TSV correlated favorably in July, but not significantly in September, January, or March. Comparing the relationships in Tables 8 and 9 demonstrates that indoor air temperature does have a substantial influence on seasonal thermal comfort.
Table 10 examines the correlation between thermal sensation and indoor temperature to understand how these factors impact residents’ overall satisfaction. According to Table 10, there’s no significant correlation between overall occupant satisfaction with the indoor temperature of buildings and occupants’ thermal sensation across all research buildings. This is caused due to the variations in the case study buildings that used different building materials, were oriented in different directions, and are either single-storied or double- storied causing different thermal performances. This resulted in a wide range of variations in the physical environmental variables.
Results and Discussion
Obtaining the comfort range and thermal neutrality
A comfortable range is attained when the mean TSV is between -1 and +1 as well as the resident level of satisfaction must be above 80%. The “neutral” temperature for each month separately and for the entire study is determined respectively listed in Table 11, by doing regression analysis among thermal sensation votes indoor air temperature as shown in Figure 28.
Table 11.
Linear regression of the TSV with Ti during individual seasons and overall study
| Month | Regression equation | R2 | Tn, s (°C) |
| July | TSV = 0.3439 Ti- 8.8431 | 0.78 | 25.71 |
| September | TSV = 0.3364 Ti – 8.2931 | 0.79 | 24.65 |
| January | TSV = 0.3225 Ti – 6.8766 | 0.87 | 21.32 |
| March | TSV = 0.3113 Ti – 7.0355 | 0.84 | 22.60 |
| Overall study | TSV = 0.2502 Ti – 5.8521 | 0.96 | 23.39 |
The study’s neutral temperatures for July, September, January, March, and the Overall study are 25.71°C, 24.65°C, 21.32°C, 22.60°C and 23.39°C respectively. For March, it is established that the comfort range lies between 19.5°C- 26.2°C, and between 24.1°C - 28.5°C for September. As the neutral temperature was found greater than the maximum indoor temperature recorded in residential buildings during January and lower than the minimum indoor temperature recorded in the same structures during July, a comfort range could not be calculated immediately. Instead by considering the range of values in between the temperature values obtained from the regression equations derived in this study for those respective months when the value of TSV is -1 and +1. The comfort range for July is found to be between 22.80°C and 28.62°C and for January it is between 18.22°C and 24.42°C.
Using the thermal preference votes concerning the indoor air temperature of the residences included in this research, as illustrated in Figure 29, the correlation between the neutral temperature and the preferred temperature was evaluated. The preferred temperature for the entire study was determined to be 23.5°C. These findings suggest that, as shown in Figure 29, the neutral temperature that has been reached and the optimal temperature coincide around this value.
Relationship between indoor temperature as a function of outdoor temperature
For the neutral thermal sensation votes across all residences for all seasons during the study, Equation 7 was created by employing the regression analysis approach to the outdoor temperature on indoor temperature, as shown in Figure 30.
Ti = 1.136 To – 6.78 r2 = 0.913
Equation 7. Relation between Ti and To for all the residents across the study.
The relation between indoor and outdoor air temperatures was demonstrated by comparing Equation 8 from the ASHRAE 55 standard to this equation 7. It is evident that research participants’ tolerance for higher temperatures exceeds that of ASHRAE 55 [9].
Ti = 0.31Tmo + 17.79
Equation 8. Relation between Ti and Tmo as given in ASHRAE 55 [9]
Discussion
Both traditional and conventional architectural styles were included in the buildings that were selected for the study. It was noted that the traditional structures have a few passive design elements that are common in climate-responsive architecture in regions accompanied by hot summers and cold winters which have been neglected as the transition happened toward conventional architectural practices. It is observed that this factor has been directly influencing the thermal environment of the respective buildings. The mean airspeed in the study spaces was found through an evaluation to be less than 0.6 m/s, hence it was not taken into account.
It was further observed that there is substantial evidence that different seasons have varying thermal sensations. The temperature variation experienced between each season can be explained by several factors. Real buildings have dynamic thermal conditions (i.e. not steady-state). Given their adaptability to the dynamic thermal environment in actual buildings [34], in dynamic environments, it is difficult to anticipate residents’ thermal sensations using physical data [35, 36, 37, 38]. According to the adaptive comfort theory, when a change makes people uncomfortable, people will react in a manner that is likely to make them feel better [8]. Although there was no noticeable change in the airspeed of the room, it was found throughout the study that between July and September, opening and closing the window provided the rooms their adequate ventilation. The residents’ adaptable behavior is seen in their choice of clothes, which is heavily influenced by the outside temperature that makes it the preferred method of adaptive control through adding or removing layers of clothing, especially during winter and summer months respectively. During the months the relative humidity level increased above 60 %, and the occupants were uncomfortable. In addition, while preferring slightly warmer and slightly cooler temperatures in March and September, respectively, the inhabitants felt content in the thermal environment due to the pleasant climate in an area with a composite climate that features cold winters and hot summers.
Table 12 shows that the overall study’s neutral temperature and comfort temperature range are lower than those indicated by the SP41, NBC 2016 and merely different than ASHRAE 55 standard but are consistent with the findings of IMAC.
Table 12.
Summary of the comfort temperature range and the neutral temperature range as determined by the study and as stated in standards
Conclusion
This research assessed how thermal sensation varies with the seasons using information collected from 11 residential buildings in the Hamirpur district of Himachal Pradesh that falls under the highland subtropical climate zone. It has been established that human thermal adaptability varies significantly depending on the season. Detailed evaluation of the structures and interaction with the residents led to the identification of factors that are physical and psychological that likely impacted their level of comfort which are stated as follows:
∙Residents have varied levels of tolerance to their respective thermal environment is a direct influence of the thermal performance of their respective architectural style to which they got accustomed for a long time. This demonstrates why those who reside in houses are very content with their thermal environment.
∙Residents modify their clothing insulation depending on the outside temperature, especially during the transitional seasons when the temperature ranges from 17 to 30 degrees Celsius.
∙Residents actively involve in adjusting the inside air velocity. For instance, during the winter months when the outside temperature is low, residents close the majority of their windows and use fans and they utilize them entirely during the summer period.
∙For the study area, the seasonal neutral temperatures are found to be 25.71°C in July, 24.65°C in September, 21.32°C in January, 22.60°C in March, and 23.39°C for the overall study with a thermal comfort range between 19.39°C to 27.39°C with a relative humidity of 42.5% - 60%.
∙The survey’s AMV values are quite consistent with the predicted mean vote values. Thus, it is possible to predict thermal sensations in mixed- mode residences in the Cwa Zone of Himachal Pradesh using the PMV proposed in the ASHRAE 55 model.
