Thermal Comfort Model Research Articles

The thermal perception of outdoor urban spaces in a hot arid climate: A structural equation modelling (SEM) approach

Human thermal comfort is linked to multi-sensory attributes and psychological cognition besides the influence of prominent meteorological parameters. To date, even the most advanced thermal comfort models like UTCI consider the interaction between human and external surroundings to be a heat transfer problem applying thermodynamic equations in the derivation behind thermal comfort indices. Machine learning (ML) models offer the advantage of improving the accuracy of predictor variable, but they fail to determine the relationship between the independent variables and data-driven prediction models. Structural Equation Modelling (SEM), an advanced modelling technique, can expose all hidden relationships among variables using latent variables while not being data-driven like ML. This study aimed to develop a structural framework which systematically explains these relationships and quantify the effects of 24 variables on outdoor thermal comfort using SEM due to its ability to represents the response of the dependent variable to a unit change in an explanatory variable. The method employed 635 structured interviews, observations, and wide-ranging micrometeorological measurements conducted concurrently in three Pedestrianized urban alleys in Cairo. The model statistically confirmed the hypothesis that overall thermal comfort in urban spaces is significantly influenced by multiple factors. Of these, background microclimatic conditions had the greatest relevance and certain urban features the least. 1 unit increase microclimate leads to a change in thermal comfort by 0.423 and 0.349 in summer and winter, respectively. In terms of thermal perception, the model shows that individuals are willing to tolerate a wider range if certain urban features are present such as shaded seating opportunities. These outcomes may aid to provide important guidance for urban designers to enhance thermal perception in urban streets.

Passive cooling and thermal comfort performance of Passive Downdraught Evaporative Cooling (PDEC) towers in a Saudi library: An on-site study

In the hot arid climate of Saudi Arabia, where air conditioning, primarily fueled by fossil fuels, accounts for a significant portion of energy consumption, the implementation of Passive Downdraught Evaporative Cooling (PDEC) towers presents a sustainable solution. This study conducts an extensive evaluation of PDEC towers in a public library, focusing on their effectiveness in reducing reliance on conventional cooling and enhancing thermal comfort. Through detailed field measurements, the study examines the influence of wind speed on the towers' cooling performance, correlating it with internal environmental conditions. The research extends to a comparative analysis of two prominent thermal comfort models: the Predicted Mean Vote (PMV) and the Adaptive Thermal Comfort (ATC) model. Findings indicate a notable interaction between wind speed and indoor air temperature, where increased wind speeds correspond to higher indoor temperatures. Despite this, the PDEC towers demonstrate a significant reduction in temperature, maintaining cooling effectiveness between 70 % and 75 %. The comparative study of thermal comfort models reveals that the ATC model provides a more relevant assessment of comfort levels in naturally ventilated spaces, with 71 % of the recorded temperatures falling within the comfortable range during the monitoring period. The research highlights the PDEC tower's potential in delivering effective passive cooling, adaptable to wind speed changes, and contributing to a sustainable and energy-efficient building environment in hot arid climates.

A Field Investigation to Quantify the Correlation between Local and Overall Thermal Comfort in Cool Environments

The thermal comfort of local body parts is the essential factor that affects people’s health and comfort as well as a buildings’ energy. This study aims to (1) investigate the characteristics of the local thermal comfort of different body parts of occupants in real buildings in winter, (2) quantify the correlation between the amount of local body parts with coolness or discomfort and the overall subjective thermal responses, and (3) validate an easy-to-use local–overall thermal comfort model. A field investigation in the office and study rooms of a university was conducted in winter. The results indicate that the top five percentages of local coolness appeared in the feet (41.02%), the hands (26.58%), the calves (25.18%), the thighs (13.99%), and the head (9.72%) and that the top five percentages of local discomfort appeared in the feet (44.99%), the palms (28.2%), the calves (24.74%), the head (19.66%), and the thighs (16.35%). Moreover, when the whole body felt cool, at least four local body parts had cool sensations; when the whole body felt thermally uncomfortable, at least three local body parts had cool sensations; and when the whole body felt that the ambient environment was thermally unacceptable, at least seven local body parts had cool sensations. Meanwhile, the correlation between local discomfort and whole-body responses was different: when the whole body felt thermal uncomfortable, at least three local body parts had discomfort; and when the whole body felt that the ambient environment was thermally unacceptable, at least four local body parts had discomfort. Further, the local–overall thermal comfort model proposed by the authors exerted high accuracy in predicting overall thermal comfort.

Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings

In the current context of energy transition and increasing climate change, optimizing building performance has become a critical objective. Efficient energy use and occupant comfort are paramount considerations in building design and operation. To address these challenges, this study introduces a predictive model leveraging Machine Learning (ML) algorithms. The model aims to predict thermal comfort levels and optimize energy consumption in Heating, Ventilation, and Air Conditioning (HVAC) systems. Four distinct ML algorithms Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and EXtreme Gradient Boosting (XGBOOST) are employed for this purpose. Data for the model is collected using a network of Raspberry Pi boards equipped with multiple sensors. Performance evaluation of the ML algorithms is conducted using statistical error metrics, including, Root Mean Square Error (RMSE), Mean Square Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Results reveal that the RF and XGBOOST algorithms exhibit superior performance, achieving accuracies of 96.7 % and 9.64 % respectively. In contrast, the SVM algorithm demonstrates inferior performance with a R2 of 81.1 %. These findings underscore the predictive capability of the RF and XGBOOST model in forecasting Predicted Mean Vote (PMV) values. The proposed model holds promise for enhancing occupant thermal comfort in buildings while simultaneously optimizing energy consumption in HVAC systems. Further research could explore the practical applications of these findings in building design and operation.

Enhancing thermal comfort prediction in high-speed trains through machine learning and physiological signals integration

Heating, Ventilation, and Air Conditioning (HVAC) systems in high-speed trains (HST) are responsible for consuming approximately 70% of non-operational energy sources, yet they frequently fail to ensure provide adequate thermal comfort for the majority of passengers. Recent advancements in portable wearable sensors have opened up new possibilities for real-time detection of occupant thermal comfort status and timely feedback to the HVAC system. However, since occupant thermal comfort is subjective and cannot be directly measured, it is generally inferred from thermal environment parameters or physiological signals of occupants within the HST compartment. This paper presents a field test conducted to assess the thermal comfort of occupants within HST compartments. Leveraging physiological signals, including skin temperature, galvanic skin reaction, heart rate, and ambient temperature, we propose a Predicted Thermal Comfort (PTC) model for HST cabin occupants and establish an intelligent regulation model for the HVAC system. Nine input factors, comprising physiological signals, individual physiological characteristics, compartment seating, and ambient temperature, were formulated for the PTS model. In order to obtain an efficient and accurate PTC prediction model for HST cabin occupants, we compared the accuracy of different subsets of features trained by Machine Learning (ML) models of Random Forest, Decision Tree, Vector Machine and K-neighbourhood. We divided all the predicted feature values into four subsets, and did hyperparameter optimisation for each ML model. The HST compartment occupant PTC prediction model trained by Random Forest model obtained 90.4% Accuracy (F1 macro = 0.889). Subsequent sensitivity analyses of the best predictive models were then performed using SHapley Additive explanation (SHAP) and data-based sensitivity analysis (DSA) methods. The development of a more accurate and operationally efficient thermal comfort prediction model for HST occupants allows for precise and detailed feedback to the HVAC system. Consequently, the HVAC system can make the most appropriate and effective air supply adjustments, leading to improved satisfaction rates for HST occupant thermal comfort and the avoidance of energy wastage caused by inaccurate and untimely predictive feedback.

Seasonal field study on thermal comfort in university classrooms in Mediterranean climate

The assessment of indoor environmental conditions of educational buildings is not only essential to ensure the correct performance of heating, cooling and ventilation systems but it is also fundamental to guarantee a suitable environment. This study aims to analyse the thermal comfort of students in teaching–learning spaces in university buildings with natural ventilation. A field measurement campaign and a questionnaire survey were carried out from September 2021 to June 2022 in educational buildings in southern Spain. The collected data were analysed and the neutral temperature in each season was obtained, based on the thermal sensation votes of 1966. The neutral temperatures found in this study were 23.5°C, 23.1°C, 23.3°C and 23.9°C, for autumn, winter, spring and summer, respectively. The ranges obtained for 90% acceptability in the winter season (21.1°C–25.1°C) provided lower temperature limits than the ranges obtained in the summer months (22.6°C–25.3°C). The highest values for clothing insulation were found in the autumn (0.90 clo) and winter (0.75 clo) seasons, compared to the spring (0.5 clo) and summer (0.4 clo) seasons. An adaptive thermal comfort model was applied. These findings could be used to improve thermal comfort and to optimise energy consumption according to the students’ actual thermal perception.

Deriving thermal sensitivity across educational stages: Evidence-based definition of Griffiths' coefficient

Thermal sensitivity directly impacts occupants' comfort by influencing neutral temperatures and comfort models. The Griffiths method, commonly used to derive thermal sensitivity, often assumes a constant value of 0.5°C-1 for calculating occupants' neutral temperatures. However, it is important to note that thermal sensitivity can vary depending on factors such as building type, climatic conditions, operation mode (i.e., natural ventilation, mechanical ventilation, or mixed mode), and occupants' age. This study aims to define whether age-dependent differences in thermal sensitivity exist in schools, achieved through the formulation of specific Griffiths coefficients for each educational stage. A field study was conducted in naturally ventilated classrooms within the same climate zone, collecting 1548 subjective responses and objective measurements from students across different educational stages. The results demonstrate that the thermal sensitivity in schools is lower than the commonly assumed values, especially in primary schools (0.085°C-1), and it increases with students' age. This variation is reflected in the corresponding neutral temperatures, which also increase with age. Using a constant Griffiths coefficient can lead to significant estimation errors in neutral temperature, potentially up to 1 °C, impacting both comfort and energy consumption. This study emphasizes that there are different thermal comfort expectations related to students’ age. To improve the reliability of adaptive thermal comfort models and ensure accurate neutral temperatures estimations, it is essential for future research to consider the students' age when evaluating the thermal sensitivity.

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.

Relative thermal pleasure model: A unified framework for understanding and predicting thermal comfort in dynamic environments

Thermal comfort within dynamic environments is shaped by a combination of factors including thermal adaptation levels, short-term thermal histories, thermal expectations, and non-thermal stimuli. However, existing thermal comfort models have fallen short in providing a unified theoretical framework capable of elucidating the combined impact of these factors on thermal comfort. Here, we introduce the Relative Thermal Pleasure (RTP) model, which offers a unified theoretical framework for both elucidating and predicting thermal pleasure. The RTP model posits that the contrast between the target thermal stimulus and its reference points, in conjunction with the distribution of attention weights among these elements, together determine the emergence and intensity of thermal pleasure. To validate the model's predictions and determine model coefficients, nineteen participants were recruited to access their thermal pleasure between two warm environments with different temperatures ranged from 32 to 45 °C. The RTP model clarifies the psychological mechanisms underlying the generation of thermal pleasure and elucidates how external environmental factors and internal psychological and physiological factors influence thermal pleasure. Through theoretical and experimental analysis, we found that improving a poor thermal environment may or may not trigger thermal pleasure, depending on the improvement percentage in the thermal environment. The critical improvement percentage to induce thermal pleasure is 48.7%. The findings of this study have the potential to advance our understanding of thermal comfort and guide us in creating more comfortable thermal environments.

Data Driven Type-2 Fuzzy Modeling and Control of Personalized Thermal Comfort

Since the room thermal comfort has high uncertainties, it is difficult but necessary to study the modeling and control of thermal comfort. This paper tries to solve the modeling and control problem for the room thermal comfort by utilizing the temperature and humidity data collected from the working or living room. Firstly, in order to discard the noisy data and to obtain the reasonable intervals for modeling the temperature and humidity, a statistic method based preprocessing strategy is presented. Secondly, the Gaussian interval type-2 fuzzy set models are constructed to depict the personalized temperature and humidity comfort by measuring the uncertainty degrees of the obtained intervals. Thirdly, a interval type-2 fuzzy method based control scheme is proposed to realize the personalized thermal comfort regulation. Finally, some experimental results are given. And, the results show that the proposed thermal comfort models and control scheme can not only recommend a reasonable temperature and humidity range, but also can realize the optimal thermal comfort control.

Machine learning-based approach to predict thermal comfort in mixed-mode buildings: Incorporating adaptive behaviors

Mixed-mode (MM) buildings are designed to provide mechanical air conditioning and natural passive cooling as regulated by occupants. This would enable the potential of shifting the narrow comfort range in HVAC (heating, ventilation and air conditioning) buildings to a wider range similar to NV (naturally ventilated) buildings. Recent studies have provided evidence of higher degrees of thermal adaptation among occupants in MM buildings. However, limited attention has been given to understanding the linkages between these expanded ranges and the specific adaptive behaviors or contextual factors that influence them. This paper aims to investigate the influence of occupants’ adaptive behaviors on thermal comfort in MM buildings. A one-year field study in two MM office buildings with 5096 valid questionnaires was conducted in Chongqing, China, under hot summer and cold winter climatic characteristics by developing machine learning algorithms compared with classic thermal comfort models. Results show that incorporating adaptive behaviors as input variables enhances the performance of machine learning algorithms, leading to improved overall model performance, while the classic thermal comfort index PMV (predictive mean vote) presents the limited accuracy but the best recall in most cases. This paper also demonstrates that some energy-inefficient thermal adaptations were found in MM buildings during the HVAC mode, such as using air conditioning in mild spring and autumn, and frequent window openings during cooling periods of summer. It is therefore valuable for future research to further focus on how MM buildings both incorporate positive features and reduce negative features during the HVAC and NV modes.

Comparative feasibility study of physiological signals from wristband-type wearable sensors to assess occupants' thermal comfort

This study compares the feasibility of physiological signals acquired from wearable sensors to assess building occupants' thermal comfort. Field experiments were conducted to acquire electrodermal activity (EDA), heart rate (HR), skin temperature (SKT), and thermal comfort ratings from 18 subjects in actual office settings using wristband-type wearable biosensors based on thermal preference votes. Multilevel modeling outcomes indicate that (1) there are significant differences in electrodermal level (EDL) and SKT between the “Want Warmer” and “No Change” and between the “No Change” and “Want Cooler” states across subjects; (2) while the thermal comfort significantly affects subjects' EDLs, the effects are not significant on SKT after associations with other physiological signals are accounted for. Given the diversity of building occupants and environmental conditions, findings from the comparative analysis are expected to provide a foundation for the development of a general thermal comfort model using physiological signals.

High-resolution multi-scaling of outdoor human thermal comfort and its intra-urban variability based on machine learning

Abstract. As the frequency and intensity of heatwaves will continue to increase in the future, accurate and high-resolution mapping and forecasting of human outdoor thermal comfort in urban environments are of great importance. This study presents a machine-learning-based outdoor thermal comfort model with a good trade-off between computational cost, complexity, and accuracy compared to common numerical urban climate models. The machine learning approach is basically an emulation of different numerical urban climate models. The final model consists of four submodels that predict air temperature, relative humidity, wind speed, and mean radiant temperature based on meteorological forcing and geospatial data on building forms, land cover, and vegetation. These variables are then combined into a thermal index (universal thermal climate index – UTCI). All four submodel predictions and the final model output are evaluated using street-level measurements from a dense urban sensor network in Freiburg, Germany. The final model has a mean absolute error of 2.3 K. Based on a city-wide simulation for Freiburg, we demonstrate that the model is fast and versatile enough to simulate multiple years at hourly time steps to predict street-level UTCI at 1 m spatial resolution for an entire city. Simulations indicate that neighbourhood-averaged thermal comfort conditions vary widely between neighbourhoods, even if they are attributed to the same local climate zones, for example, due to differences in age and degree of urban vegetation. Simulations also show contrasting differences in the location of hotspots during the day and at night.

Machine learning-based prediction of outdoor thermal comfort: Combining Bayesian optimization and the SHAP model

Rising global temperatures have resulted in urban heat waves in recent years, endangering residents' health and even their lives. As a result, accurate outdoor thermal comfort prediction is critical. The goal of this research is to develop highly accurate and interpretable machine learning models of outdoor thermal comfort. We created a summer outdoor thermal comfort dataset for cold regions using microclimate parameter measurements and questionnaires, and divided it into two datasets: without and with shading. The prediction performance of nine machine learning models was compared, as well as their prediction performance following Bayesian optimization. Finally, the SHAP model was used to explain the important features of the best machine learning models and their impact on thermal comfort. The results show that after partitioning the dataset by shading, the accuracy of the best machine learning prediction models for Thermal sensation vote (TSV), Thermal acceptable vote (TA), and Thermal comfort vote (TCV) in the unshaded space improves by 9.2%, 9.31%, and 6.16%, respectively, while the prediction accuracy in the shaded space remains basically unchanged. Extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and categorical boosting (CatBoost) outperform other methods. After Bayesian optimization (BO), the machine learning models' TSV, TA, and TCV prediction accuracies improved by 6.83%, 4.05%, and 2.55%, respectively. CatBoost model with Bayesian optimization (CatBoost + BO) was the best model in most cases. TSV, TA, and TCV predictions were heavily influenced by microclimate parameters, with mean radiant temperature being the most important in unshaded spaces and air temperature being critical in shaded spaces. Furthermore, age, body mass index, and emotional state all had a significant impact on TA and TCV. This research will contribute to improving the quality of life of urban residents by providing a scientific foundation for the design and planning of urban open space.

Exploring thermal comfort in rural houses of Chhattisgarh state, India: A comprehensive survey and adaptive model analysis

Thermal comfort studies in India are very much at an emerging stage. According to Indian codes, in the summer, the comfortable temperature range for residential buildings is 23–26 °C. The recommended Indian residential comfort range does not consider the impact of India’s enormous cultural and geographical heterogeneity. Additionally, there seems to be very little effort to conduct a thermal comfort field survey in India’s rural regions. In recent years, houses in rural India have been converted from kutcha (mud houses) to pucca (concrete houses). This work is aimed at proposing an adaptive thermal comfort model for the premonsoon season in the rural area of Raipur, Chhattisgarh, India. The model was achieved using a field study survey conducted in 80 rural area houses in the months of June and July. Regression analysis, the Griffiths method, and the Fanger method have been adopted to analyze thermal comfort. Based on the regression analysis, the comfort band (voting −1 to +1) is established in the range of 28.39 °C to 32.31 °C, with 30.30 °C as the neutral temperature. The study has been carried out to assess the influence of gender, age, controls, and fan usage on thermal comfort perception in rural households.

Improved grey principal component analysis neural network based adaptive thermal comfort model: Application in the enclosed cabin with microclimatic conditions

Predicting the thermal comfort of operators in enclosed cabins under extreme operational conditions is crucial for the enhanced and optimal design of cabin air circulation systems. In this study, an improved supervised machine learning algorithm, namely a Grey Principal Component Analysis (G-PCA) was proposed to evaluate the operators’ thermal comfort. The comprehensive dataset was first attained and constructed from the proposed 32 indicators, which recorded each tested object’s EEG and physiological features, core body temperature, skin temperature, and subjective assessments. The accuracy of the proposed model was demonstrated by comparing it with the results predicted via existing indicator sets (PMV and 7-point skin temperature) and modelling algorithms (BP and PCA BP). The results showed that the prediction accuracy of the PMV model, 7-point skin temperature, the 32 indicators were found to be 79.4%, 82.8%, 96.1%, utilising the G-PCA WNN algorithm, and the prediction accuracy of BP, PCA BP, G-PCA WNN is 82.3%, 89.9%, 96.1%, based on the 32 indicators. A conclusion could be drawn that the proposed 32 indicators can incorporate a more comprehensive range of thermal comfort information, and the improved G-PCA WNN thermal comfort prediction model achieved the best prediction performance among the compared algorithms and models.

Physiological and Thermal Sensation Responses to Severe Cold Exposure (−20 °C)

Various jobs, indoors and outdoors, are subjected to severe cold temperatures during daily activities. Extremely low-temperature exposure and work intensity affect health, safety, and occupational performance. This work aimed to assess the physiological and thermal sensation responses before, during, and following a 60 min exposure to cold (−20 °C), during which occupational activities were developed. Using ingestible telemetric temperature pills, eight skin temperature sensors, blood pressure equipment, and the Thermal Sensation Questionnaire, experiments were conducted with 11 healthy male volunteers wearing highly insulating cold protective clothing. The most notorious alterations were reported in mean skin temperatures and thermal sensation responses during the first 20 min of cold exposure. Among the eight skin temperature points, the forehead and left hand showed a higher sensitivity to cold. The mean core temperature reported significant variations throughout the protocol, with decreases during the initial 10 min of cold exposure and posterior increases despite the cold environment. Blood pressure showed slight increases from the initial to the recovery period. Overall, outcomes contribute to current scientific knowledge on physiological and perception responses in extremely cold environments while describing the influence of protective clothing and occupational activities on these responses. Future research should be developed with additional skin temperature measurements in the extremities (fingers, face, and toes) and the analysis of thermal sensation potential associations with performance changes, which can also be of great significance for future thermal comfort models.

The Effects of Exterior Glazing on Human Thermal Comfort in Office Buildings

As a major component of the building envelope, the energy-saving design of exterior windows is key to energy savings in office buildings. The conventional design of exterior windows mainly focused on their impact on heating and cooling energy but ignored the indoor thermal comfort problems caused by the direct solar radiation transmitted by windows and the fluctuation of their internal surface temperatures. This study analyzed the influence of exterior windows on the indoor thermal environment of office buildings by carrying out field experiments. The experiments were carried out in a typical office building in Xi’an during December and January. The impact of exterior windows on human thermal comfort was studied from two perspectives: longwave radiation from the surface of window glass and solar shortwave radiation. It was found that solar radiation was the main cause of temperature fluctuations on the internal surface of windows and created non-uniform thermal environments. The mean radiant temperature fluctuations in the near-window area could reach up to 7.8 °C due to outdoor solar radiation in winter. Solar radiation transmitted by windows directly affects thermal sensations. Since conventional thermal comfort models or indices underestimated the thermal sensations of occupants in the presence of solar radiation, the additional thermal loads caused by solar radiation needed to be taken into account. The allowable operative temperature range for maintaining thermal comfort should be reduced by 0.5 °C when occupants are exposed to solar radiation.

An investigation of indoor thermal environments and thermal comfort in naturally ventilated educational buildings

Indoor thermal conditions are essential in educational buildings. The health and well-being of students can be affected by a poor thermal environment, which also has a clear impact on building energy consumption. In this context, the indoor thermal environment in naturally ventilated university classrooms is explored in this study during a complete academic year. A monitoring campaign and a questionnaire survey were conducted simultaneously in higher education buildings in Spain. A total of 2115 sets of data were collected. Thermal sensation prediction indices (predicted mean vote, extended predicted mean vote and adaptive predicted mean vote) were applied to evaluate student’s thermal perception and their prediction accuracy was assessed. Additionally, two machine-learning models, based on Artificial neural network (ANN) and random forest (RF) algorithms, were formulated to predict occupants’ thermal sensation. The obtained results evidenced that the proposed ANN and RF models outperform traditional indices. Finally, it is also proposed an adaptive thermal comfort model. The results obtained suggest that students have a greater adaptive capacity to changes in environmental conditions than suggested by the ASHRAE-55 adaptive model and that they preferred an environment with lower temperatures than those suggested by the EN-16798 adaptive model.

Thermal comfort in Indian naturally ventilated buildings: A comprehensive review

A significant amount of global energy is expended to meet the cooling requirements in buildings. There has been a huge shift from naturally ventilated buildings to air-conditioning with an annual shift of 15.75% considering an overall increase in extreme heat incidences in cities during summer. Studies show that reducing cooling energy consumption significantly is possible by applying passive approaches in buildings. In this study, thermal comfort in naturally ventilated buildings on a global scale, with a specific emphasis on India, has been examined through a review of existing studies. Bibliometric analysis, followed by comparative analysis, is used to identify and evaluate research on methods for assessing thermal comfort and developing adaptive models for occupants in diverse buildings and climates. It is observed that Griffith’s method is popularly used along with PMV-PPD to obtain thermal comfort interpretations in Indian naturally ventilated buildings. Moreover, the major contributors to thermal comfort studies are Europe and Southeast Asia. Further, Indian subcontinent has a wider comfort range than what international field studies have suggested. While the residents of naturally ventilated buildings in cold climates have a comfort range of 12.5–32 °C, for composite climates, the range is 15.3–33.8 °C, and for hot and humid climates, the range is 18.4–33.5 °C. Additionally, the variation in the thermal comfort range is also significantly influenced by local cultural factors. Therefore, a customized thermal comfort model that should not only reflect the unique climatic conditions but also include cultural factors of the Indian subcontinent can ensure that building occupants are provided with a comfortable and healthy indoor environment.