Most thermal comfort standards and guidelines currently in use do not consider occupants' adaptive capabilities associated with real-world situations when predicting occupant thermal comfort in ...mechanically conditioned buildings, although the adaptive approach is commonly applied to naturally ventilated buildings. These standards are generally based on results derived from experiments conducted in climate-controlled chambers. In some cases, this can lead to overcooling of buildings while still not satisfying most of occupants. One common method to reduce peak electricity demand is via temporarily increasing cooling temperature setpoint during peak hours. However, the potential negative impacts on occupant thermal comfort and wellbeing calls for further study on this. This paper describes a study conducted on a university campus in the United States that investigated occupants' thermal sensation, acceptability, and preferences corresponding to increased cooling temperature setpoint in parts of the building that are temporarily occupied. The results revealed a potential for at least temporarily increasing cooling temperature setpoint (at least 2 °C) across this campus without impairing occupant thermal comfort. For operative temperatures between 22 and 24.5 °C, the average Actual Mean Vote (AMV) for the class sections remained in the ASHRAE comfort range and the self-reported thermal acceptability was above 80%. Occupants’ thermal acceptability dropped to less than 80% when the temperature was increased to more than 24.5 °C, and the AMV values increased to more than 0.5 (on ASHRAE 7-point scale). The percentage of occupants who were involved in some sort of adaptive behavior did not considerably change with room temperature.
•By temporarily increasing indoor operative temperature in campus classrooms from 21 to 25 °C, thermal acceptability remains above 80%.•At 23.5 °C, occupants' mean thermal preference and their actual mean votes equals zero, while thermal acceptability is above 90%.•Occupants' adaptive behavior in this study, with limited adaptive options, is not a function of the operative temperature.•Heat balance model under-predicts the percentage of dissatisfied occupants at environmental conditions warmer than neutral.
Throughout this paper, we reviewed the most popular thermal comfort models and methods of assessing thermal comfort in buildings and vehicular spaces. Most of them are limited to specific steady ...state, thermally homogenous environments and only a few of them address human responses to both non-uniform and transient conditions with a detailed thermo-regulation model. Some of them are defined by a series of international standards which stayed unchanged for more than a decade.
The article proposes a global approach, starting from the physiological reaction of the body in thermal stress conditions and ending with the model implementation. The physiological bases of thermal comfort are presented, followed by the main thermal comfort models and standards and finishing with the current methods of assessing thermal comfort in practice. Within the last part we will focus mainly on thermal manikin experimental studies, and on CFD (computational fluid dynamics) numerical approach, as in our opinion these methods will be mostly considered for future development in this field of research.
Research efforts have demonstrated the potentials of improving the performance of Heating, Ventilation, and Air-Conditioning (HVAC) systems by leveraging personalized thermal comfort preferences and ...profiles. However, there are remaining challenges for effective control in collective conditioning in multi-occupancy scenarios. In this study, we have investigated the impact of personal thermal comfort sensitivities – distinct individual reactions to temperature variations– on collective conditioning. To this end, we have explored whether taking the thermal comfort sensitivity into account influences the selection of temperature setpoints and the overall probability of achieving comfort. We have also examined the impact of different thermostat temperature resolutions (0.1, 0.5, and 1.0 °C) on these factors with a hypothesis that finer resolutions could aid in achieving improved overall thermal comfort. In doing so, we have proposed an agent-based control mechanism to simulate the multi-occupancy space, controlled by an HVAC agent to provide air conditioning for multiple human agents using three operational strategies to compare conventional strategies with our proposed approach. The first strategy relies on majority thermal votes, the second one relies on the gap between thermal preferences (i.e., preferred temperature) and ambient temperature, and the third strategy uses thermal comfort sensitivity in addition to preferences. The investigations were conducted by using stochastically modeled comfort profiles (six actual comfort profiles and 15 mathematically synthesized profiles from actual data). These profiles were used to model the behavior of human agents in diverse multi-occupancy scenarios, modeling two to ten occupants in a space for different thermostat temperature resolutions. Our investigations demonstrated that thermal comfort sensitivity plays a statistically significant role in collective conditioning as it resulted in changes of temperature setpoint in 86% of cases and a higher probability of achieving collective comfort.
•The impact of individual thermal sensitivity has been evaluated in multi-occupancy scenarios.•The impact of individual thermal sensitivity on HVAC operational strategy is evaluated.•Agent-based modeling was adopted in evaluating the diverse multi-occupancy scenarios.•The influence of temperature setpoint resolution on collective comfort were evaluated.•Selected setpoint temperature and probability of collective thermal comfort were used as metrics.
•We applied support vector machine algorithms to predict thermal sensation vote.•The new model can distinguish thermal comfort response in different contexts.•It produces high prediction accuracy in ...both air conditioning and natural ventilation cases.•Comfort zones determined by the new model are similar to the existing ones.
Many models can predict building occupants’ thermal comfort, but their accuracies were not always perfect due to the limited self-learning and self-correction capability when varying the application contexts. Advances in machine learning algorithms allow us to reveal the “hidden insights” behind a large amount of data, offering a great opportunity to understand more nuanced aspects of thermal comfort in buildings. This study applied the support vector machine (SVM) algorithm to the RP-884 thermal comfort database and developed a new model with self-learning and self-correction ability. We identified its application range according to the features of the SVM algorithm and the sample distribution characteristics of RP-884. With variables of indoor air temperature, clothing insulation, metabolic rate, air velocity and so forth, the model can largely reduce the previous models’ inaccuracy. Compared to the PMV model, the new model's sum of squares for residuals (SSE) reduced by 96.4%, and the fitting degree (Rnew) increased by 83.7%. It can also quantify the effects of each input variable on building occupants’ thermal sensation. Instead of using two separate models, the data-driven model can automatically distinguish occupants’ thermal comfort responses in natural ventilation (NV) and air-conditioning (AC) buildings. Using the new model, we determined thermally comfortable zones on the psychrometric chart for NV and AC buildings. Moreover, an open-access platform has been developed to help apply machine learning algorithms in thermal comfort data analysis. The work introduced in this paper can be a reference for a more comprehensive comfort model development.
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In the present work, a new environment-adaptive wall is proposed, based on an inside and outside radiant panel with pipes drowned on the panels themselves and hosting a heat-carrying liquid ...pushed by a pump. The purpose consists of transferring heat across the thickness of the wall, in the direction required for energy saving and enhancement of inner indoor thermal comfort. A Computational Fluid Dynamic (CFD) analysis is reported and the results are applied on a single zone box-shaped building, where a whole-year study is implemented by means of the transient simulation tool TRNSYS. The efficiency of this solution respect to the state-of the-art static walls is finally discussed. A concept of a laboratory setup of an adoptive wall along with first result are presented and discussed.
•Univariate regression was applied on the recently released ASHRAE database.•Individual, building, and geographic factors have been examined.•Geographic and HVAC mode are the most influential for ...neutral temperature.•Building type and local climate has the biggest impact on thermal sensitivity.•This work could help select useful features for personal comfort models.
Different thermal demands and preferences between individuals lead to a low occupant satisfaction rate, despite the high energy consumption by HVAC system. This study aims to quantify the difference in thermal demands, and to compare the influential factors which might lead to those differences. With the recently released ASHRAE Database, we quantitatively answered the following two research questions: which factors would lead to marked individual difference, and what the magnitude of this difference is. Linear regression has been applied to describe the macro-trend of how people feel thermally under different temperatures. Three types of factors which might lead to different thermal demands have been studied and compared in this study, i.e. individual factors, building characteristics and geographical factors. It was found that the local climate has the most marked impact on the neutral temperature, with an effect size of 3.5 °C; followed by country, HVAC operation mode and body built, which lead to a difference of more than 1 °C. In terms of the thermal sensitivity, building type and local climate are the most influential factors. Subjects in residential buildings or coming from Dry climate zone could accept 2.5 °C wider temperature range than those in office, education buildings or from Continental climate zone. The findings of this research could help thermal comfort researchers and designers to identify influential factors that might lead to individual difference, and could shed light on the feature selection for the development of personal comfort models.
Advances in heating, ventilation and air conditioning (HVAC) technologies have dramatically improved the indoor thermal environment, but attention should be paid on how this would affect building ...occupants' thermal comfort perception. In this paper, we studied the mutually dependent relationship between indoor climate experience and occupants' comfort expectation. An intriguing experiment was conducted in China where wintertime indoor thermal environments in northern cities (with district heating) are much warmer than in southern region (without district heating). By analyzing the 4411 responses from four college-aged subject groups with different indoor thermal history, two interesting findings emerged. Firstly, people's understandings of thermal comfort change with their indoor thermal experiences. Those permanently live in lower-grade non-neutral thermal environment can achieve similar thermal comfort perception as those who live in long-term comfortable thermal conditions. Secondly, the dynamics of building occupants' thermal comfort adaptation project asymmetric trajectories. It is much quicker for occupants to accept neutral indoor climate than to lower their expectation and adapt to under-conditioned environments. These two phenomena can be well described by the index “demand factor”, which can serve as a reference for future thermal comfort study.
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•This study explores indoor climate experience and thermal comfort expectation.•4411 responses from subject groups with different indoor thermal history were collected.•The results show that people's understandings of thermal comfort are malleable.•The dynamics of building thermal comfort adaptation exhibit asymmetric trajectories.•The index “demand factor” was adopted to describe expectation dynamics.
In urban planning, sustainability is often synonymous to urban livability. Urban livability seeks to improve living conditions of current and future outdoor users and hence it has become a key ...priority for cities authorities. Melbourne, as the world second most livable city, sets out to improve its urban life quality through various policies and initiatives. One area of improvement is the creation of sustainable outdoor spaces that provides comfortable thermal conditions for its residents. The relevant strategies to create such spaces are supported by the knowledge of human thermal comfort requirements, particularly during the summer thermal conditions. Hence, this study aims to develop comprehensive thermal comfort benchmarks for Melbourne during the summer. This study builds on 4717 subjective survey responses collected in seven urban environments with different settings. Data collection was performed in Melbourne's summer from 2012 to 2015. Physiological Equivalent Temperature (PET) was used to predict thermal comfort conditions. The results were based on four thermal comfort measures (neutral temperature, preferred temperature, acceptable thermal range and thermal comfort range). The analysis' outcomes suggested that Melbourne's summer acceptable thermal range is between 11.3 °C and 20.3 °C, the preferred temperature value is 21.5 °C, the neutral temperature value is 16.1 °C. Furthermore, PET index was calibrated against thermal responses collected from the surveys. The results would help to inform policies aiming to create sustainable outdoor spaces that are pleasant to outdoor users in Melbourne.
•Analytical techniques affect the values obtained for outdoor thermal benchmarking.•The thermal comfort benchmarks inform design strategies to create comfortable outdoor spaces within cities.•Summer preferred PET (21.5 °C) is higher than neutral PET (16.1 °C).•Summer acceptable thermal range is between 11.3 °C and 20.3 °C (PET) in Melbourne.•Further studies should cover other comfort requirements such as visual and acoustics.
•Three models for predicting thermal comfort distribution were compared.•The accuracies of ordered probability and multinomial logit models were similar.•Linear regression model was less accurate ...than the other two models.
This study compares the linear regression model, ordered probability model, and multinomial logit model for prediction of the individual thermal sensation votes (TSVs) and TSV distributions under given conditions. Two thermal comfort datasets were used to develop and evaluate the models. One dataset was taken from an indoor thermal comfort survey conducted in Pakistan, and the other was taken from an outdoor thermal comfort survey conducted in Tianjin, China. The data were divided into training and validation datasets. The training datasets were used for model development. The developed models were then used to predict new cases in the validation dataset. The predictive capability of the three models were systematically evaluated and compared to examine how well the developed models predicted individual TSVs and TSV distributions for the validation dataset. The results showed that the ordered probability model and the multinomial logit model correctly predicted around 50% of the individual TSVs, whereas the accuracy of the linear regression model was only around 20 to 40%. In addition, the chi-square statistics show that the ordered probability model and the multinomial logit model better predicted the TSV distributions than the linear regression model.
The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global ...Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS – observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15–25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV–PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.
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•Assessed PMV-PPD accuracy using the ASHRAE Global Thermal Comfort Database II.•PMV predicted thermal sensation correctly only one out of three times.•PMV had a mean absolute error of one unit on the thermal sensation scale.•PPD was not able to predict the dissatisfaction rate.•PMV-PPD accuracy varied strongly between ventilation, building types and climate.
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