Comfort Model Research Articles

Evaluating future building energy efficiency and environmental sustainability with PCM integration in building envelope

The energy demand in the building sector is anticipated to increase with climate change and the high energy consumption is responsible for releasing enormous amounts of CO2 into the environment, intensifying climate change. Phase change material (PCM) integration into the building envelope has a high potential to reduce the building's energy demand without compromising environmental sustainability. This research investigated the impact of PCMs (PCM 18–PCM 30) on building cooling, heating, and annual energy efficiency for future climate scenarios (2095) in 13 different climate zones worldwide. For this purpose, the Fanger comfort model was implemented and numerical simulations were performed using Energyplus. The performance of PCM was evaluated using novel Predicted Mean Vote fluctuation reductions (PMVFR), and the Total Predicted Mean Vote drop (TPMVD). Further, for the first time, a time series-forecasting model for the estimation of CO2 emissions originating from various energy production sources in the future was developed using gene-expression programming (GEP) algorithm. Finally, the two different maps showing the PCM efficiency for energy conservation and environmental sustainability at the global level are presented. The results evidence a maximum of 12.9% reduction in annual energy demand in the future with the integration of PCM. The GEP-based prediction model demonstrated good accuracy as determined by the statistical parameters and satisfied the external validation requirements. The environmental analysis exhibits that PCM integration can reduce carbon emissions up to 204 CO2-e Kg/year. Overall, the integration of PCM is regarded as a sustainable solution for building energy efficiency, considering climate change.

Adaptive Comfort Potential in Different Climate Zones of Ecuador Considering Global Warming

Ecuador is a country with several climate zones. However, their behaviour is similar throughout the year, with no peaks of extreme temperatures in the various seasons. This paper is a first approach to study the adaptive comfort behaviour in several areas and populations of the country. Considering the ASHRAE 55-2020 model, energy simulation programmes are applied not just to the current climate scenario but also to the climate change scenarios of 2050 and 2100. The results of locations are analysed and compared to determine their performance. Thanks to their climate characteristics, adaptive comfort models could be applied as a passive strategy, using natural ventilation for building indoor comfort improvement, particularly social dwellings. According to previous studies, some prototypes have not considered the climate determinants in each region. Given the geographic situation of the study areas, the adaptive comfort model could be applied in all cases. Percentages of application of natural ventilation and heating and cooling degree hours have similar behaviours according to the climatic region, with a variation greater than 30% among them.

Development of ReShadS, a climate-responsive shading system: Conception, design, fabrication, and small-scale testing

Integrating computational technologies to produce dynamic and intelligent architecture that can enhance building thermal performance and meet users' requirements and routines is broadly classified as responsive architecture. This seems to be a viable solution for façades, reducing energy demand during the building operational phase. However, scientific content regarding responsive components' fabrication, operation, and performance evaluation is not yet consolidated. This paper comprehensively presents the design and fabrication processes of a low-cost responsive shading system prototype, accounting for materials' characterization, software, responsive functionality, and its performance evaluation regarding thermal and illuminance management. The analysis is based on the Adaptive Comfort Model and Useful Daylight Illuminance parameters. Preliminary results indicate the system's feasibility in managing temperature and illuminance within the comfort zone by 58 % and 82 % of data points, respectively. The resulting prototype and the responsive management strategy contribute to the emerging responsive architecture field toward reducing building energy consumption.

Adaptative comfort modeling for a typical non-centrifugal cane sugar processing facility

The production of non-centrifuged cane sugar in Colombia takes place in post-harvest facilities that generate significant heat and steam resulting from the evaporation of cane juices during the process. This study aimed to improve the comfort conditions of a facility of this type in the municipality of Pacho, Cundinamarca, Colombia, through bioclimatic simulation, where the enclosure on the walls and the lantern window were modified. The evaluation of adaptative thermal comfort revealed that configurations with open perimeter and lantern window demonstrated the best bioclimatic behavior. This is attributed to the increased ventilation area and chimney effect, which optimizes the transfer of heat and mass. Likewise, it was observed that there is a generalized behavior of thermal discomfort for workers in the thermal zone of the oven, due to the high emissions of heat and steam in this specific area.

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.

Occupant’s Thermal Perception in Mixed-mode Office Buildings of the Tropical Climate

Thermal comfort and energy consumption in office buildings is a global critical concern. This study investigated this challenge in the Faculty of Environmental Technology, Abubakar Tafawa Balewa University, Bauchi-Nigeria. Through a field survey and physical environment measurements. The study explored occupant perceptions of thermal comfort and satisfaction. It evaluated the thermal comfort and satisfaction of occupants in a mixed-mode office building, focusing on potential discrepancies between perceived comfort and internationally recommended standards. Despite air temperatures falling outside the PMV model's comfort range, high thermal comfort, and satisfaction levels were reported by the respondents. These findings align with other studies in Nigeria, suggesting adaptation and acclimatization to local conditions. The study further examined the relationship between thermal comfort and occupant satisfaction. The result revealed a moderate positive association, suggesting increased thermal comfort leads to higher satisfaction among occupants. While thermal comfort explained 25% of the variance in satisfaction scores. Finally, the study suggests the localization of comfort standards, improved mixed-mode system performance, and encouraging evidence-based design interventions that will ultimately benefit both occupants and the environment.

Development of adaptive behaviour models for thermal comfort: Longitudinal investigation in Japanese office buildings and literature review

In the recent year's energy consumptions has increased, especially in the office buildings for heating and cooling use. Adaptive behaviours such as clothing adjustments, window opening, fan use, heating use and cooling use are important contributing factors for thermal comfort of the occupants. The main objectives of this study is to clarify different behavioural adaptations carried out by occupants in Japanese office buildings and to compare with previous studies. The occupant behaviour models were developed for clothing adjustments and air-conditioning use. Occupant behaviour survey was conducted in the five mixed mode (MM) and one heating, ventilation, and air-conditioning (HVAC) Japanese office buildings over a period of fifteen months resulting in 16,411 votes from 45 participants. Range of clothing insulation was wider in MM buildings compared to HVAC buildings which indicate that occupant behaviour in MM buildings required less energy for thermal comfort. The mean proportion of window opening and fan use was 0.54 and 0.41 respectively. Developed logistic model suggested that 80% of occupant used heating and cooling when the outdoor air temperature drops below 11 °C and rise above 27 °C respectively. The obtained results from field investigation are validated by the extensive literature review. The occupant behaviour models in Japanese office buildings are comparable with the behaviours models from different parts of the world having various climatic conditions. It was also confirmed that occupants behaviours are effective for energy saving building designs. The acknowledgement of these behaviour of the occupants is fruitful in designing thermally comfortable and possibly energy saving MM buildings in future.

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.

Optimization Control Strategy for Transition Season Blinds Balancing Daylighting, Thermal Discomfort, and Energy Efficiency

Natural lighting in office buildings is often affected by excessive solar heat gain and discomfort glare, and the transitional seasons are no exception. Therefore, addressing the issue of natural lighting in buildings has always been a challenge in developing shading control strategies. This study designed a model-based calculation method for shading blind control indicators. The method combines existing physical models for sky conditions, solar radiation, heat transfer, thermal comfort, glare, and illuminance calculations and incorporates modifications and additions. Additionally, an equivalent energy-saving model for shading was established. A shading blind control strategy balancing indoor light and the thermal environment with energy savings during transitional seasons was proposed. In transitional seasons, this method can eliminate 100% of glare, reduce discomfort illuminance duration by 81.3% and heat discomfort duration by 87.5% and save 81.3% of lighting energy consumption when air conditioning is used. By comparing the simulation results of an office building in Xi’an with the results obtained from this calculation method, the absolute error percentage was found to be 6.83%, verifying the reliability of the calculation method. Finally, the proposed control strategy was compared with common methods such as no blinds, end-angle control, and fixed-angle control to evaluate its performance in terms of daylighting, thermal comfort, and energy savings. The results showed that the control strategy proposed in this study has significant advantages with respect to various performance indicators.

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.

The COMFORT Model: Moving the Initiative Outside of the Academy

ABSTRACT Reflexive and prospective in nature, the creators of the COMFORT model describe experiences of moving this research outside of the academy. The COMFORT model represents the seven basic principles of healthcare provider communication: C – Connect, O – Options, M – Making Meaning, F – Family Caregivers, O – Openings, R – Relating, T – Team. The COMFORT initiative began as a call for change in healthcare communication education. Originally published as a final chapter in a volume on family and palliative care communication, it was the start of an extensive translational program of trainings and tools addressing healthcare provider communication.

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.

Modelling of human thermal comfort in armored military vehicles: An experimental study of removing heat with spot cooling

Military combat vehicles differ in many ways from civil automotive vehicles because of their aim of usage and structure. One of these differences is using spot cooling technic for the air conditioning system in the vehicle. However, the A/C system in these vehicles is driven by the engine of the vehicle, like in civil automotive vehicles. When the engine of the vehicle is off, the A/C system does not work as well. But in some conditions, the military personnel in a military vehicle may need to work on some duties for some period of time without running the engine of the vehicle. So, military personnel need a secondary system that creates A/C effect for that period of time. In this study, an analytical model is prepared to determine the total amount of heat transfer value that should be removed from the personnel by spot cooling with different properties of blowing air to help in designing this required A/C effective system. This analytical model is validated with an experimental setup that simulates the conditions of a sample military combat vehicle. This validated analytical model has been carried out with respect to different blowing air properties and the range of distances between the louver and the personnel. The values that provide the minimum required amount of heat transfer to be removed from the personnel are determined and used to create thermal balance curves. These curves can be used to design a new A/C system to make military personnel comfortable in the vehicle.

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.