室内温熱感分布を考慮したペリメータゾーンの快適性と省エネルギー評価方法の提案

The predicted mean vote (PMV) model predicts the mean thermal response of a large group of people, according to the ASHRAE thermal sensation scale, and has been proven to agree well with many field studies. However, the model may not predict well the mean response of a small number of people or individuals because it does not take into account the physiological variations of people due to changing thermal environments. In this paper, the thermal comfort sensations of twenty subjects (ten males and ten females) aged 21 to 30 were investigated by Fanger's PMV model. This experimental study found that the PMV model failed to predict the subjects' thermal sensations. The reason may be that the mean skin temperatures (MSTs) of the subjects were fixed uniformly, regardless of the variations of the thermal environments, which results in errors using the PMV model, especially for a small number of subjects. To obtain the MSTs of the subjects under different thermal environments, fourteen measurement methods were employed. Afterwards, the PMV model was used to predict the thermal response of the twenty subjects based on their measured MSTs. The results prove that the prediction accuracy of the PMV model is improved greatly when the MSTs are obtained through measurements instead of theoretical calculations. In addition, this study concludes that the Burton 3-point Method may be the best to measure MSTs for the PMV model.

Enhancement of thermal comfort in a large space building

The purpose of this study is to infer an acceptable range of thermal comfort for Japanese people during summer season with a view to the energy conservation in buildings. From September to October, 1984 six college-age Japanese males were exposed for 2.5 hours under seven different conditions in the Waseda University environmental test chamber so as to determine the effects of humidity on thermal and comfort sensations. The results analyzed from the 42 experiments in total are discribed. Fig. 1 shows plan and section of the test chamber. The volume of the test chamber is 19.2 m^3 and floor area is 8.5 m^2. Ceiling and right side wall panels have 5 cm thick air spaces behind the panels. Table 1 shows design specification of the chamber to attain hot and humid summer conditions. Room air temperature can be varied between 20℃ and 39℃ and absolute humidity between 8 g/kg and 27 g/kg. Maximum air change rate is about 100 h^<-1>. The scheme of the air conditioning system is shown in Fig. 2. Table 3 shows the test conditions and the deviation from the set point temperatures and humidities which were measured during actual experiments. Subjects wore almost the same clothings and the mean clo value was 0.45. The mean radiant temperature was equal to the air temperature and air velocity in the occupied zone around 0.25 m/s. Subjects were sitting on the chairs against the air movement direction. Antropometric data for the subjects are listed in Table 2. Fig.3 shows the experimental plan. At the beginning of the test (1) weight, (2) body temperature at armpit, (3) heart beat, blood pressure, (4) skin blood flow were measured. Every five minutes skin temperatures, room air temperature, humidity and room surface temperatures were measured. Remeasuring the above four items at the end of the test, thermal sensation and comfort sensation were asked to be answered as shown in Table 4. Modified temperature (MT) defined as the air temperature of 50 %rh, 0.1 m/s mean air velocity, 0.6 clo, 1.0 met and mean radiant temerature equaled to the air temperature, which would provide the same thermal sensation as under the actual thermal condition was used for data analyses. MT was calculated by Fanger's comfort equation. Fig. 5 shows experimental results of thermal sensation vote. There is no significant difference between 60 %rh and 80 %rh at the air temperature of 25℃. The difference of about 0.7 in thermal sensation vote was found between 40 %rh and 80 %rh at the air temperature of 28℃ and also about 0. 7 difference between 40 %rh and 60 %rh at 31℃. It was found that higher the temperature greater the difference in thermal sensation vote. This is considered to be caused by difference in humidity. In Table 5 the calculated regression equation for the estimated mean thermal sensation vote by Waseda collegeage Japanese male subjects is given as a function of modified temperature. The results with American collegeage male subjects and Danish college-age male subjects are also shown for comparison. Correlation coefficient between modified temperature and thermal sensation for all observations (n=42) was 0.807 and that for the mean of six subjects was 0.968. It was found that thermal sensation has a good relation to the modified temperature. The temperature corresponding to the neutral mean vote can be determined by substituting Y=4 (neutral) into each regression equation. The calculated results for different groups are given in Table 6. Fig. 6 shows the mean thermal sensation vote versus modified temperature for Waseda college-age Japanese male subjects, Danish college-age male subjects and American college-age male subjects. The regression line for Waseda subjects is found very similar to the one for Americans. The mean themal sensation vote versus ET for Waseda college-age Japanese male subjects is shown in Fig. 7. Correlation coefficient between ET and thermal sensation vote for the mean

Researchers have reached a consensus on the thermal discomfort known as the major cause of sick building syndrome, which hurts people’s health and working efficiency greatly. As a result, the thermal environment satisfaction is important and thus many studies have been dedicated to thermal comfort over the past few decades. Predicted Mean Vote (PMV) is one of the globally used standards to express users’ comfort satisfaction with the given thermal moderate environments. It has been widely used in most of the Heating, Ventilation and Air Conditioning (HVAC) systems to maintain this standard of thermal comfort for occupants of buildings. However, the PMV model is developed on indoor experimental data without taking into account conditions of outdoor space, which greatly affects the performance of the existing HVAC systems and varies with the seasons. In this paper, an enhanced Model-based Predictive Control practical system for maintaining the indoor thermal comfort is demonstrated, including a multiple linear regression predictive model and an innovative fuzzy controller considering both the PMV index and the outdoor environment conditions. To verify the usability of the designed system, an Internet of Things (IoT) smart space prototype was chosen and experimentally tested in a building in Jeju, Korea. Moreover, thermal comfort regulation performances using the proposed approach have been compared with the existing one. The results of our work indicate that the proposed solution is capable of optimizing the thermal comfort condition according to seasonality and outperforms the conventional approaches in different performance indexes.

The healthcare sector is a significant contributor to global energy consumption, particularly within the heating, ventilation, and air conditioning (HVAC) market, due to stringent requirements for maintaining indoor thermal comfort for patients, staff, and visitors in hospital wards, rooms, and intensive care units. This study presents a novel approach employing a Machine Learning-based Linear Regression Algorithm to predict indoor adaptive thermal comfort within the inpatient medical wards. The methodology establishes a robust correlation between key indoor environmental parameters including air temperature, relative humidity, and air velocity, and thermal comfort indices, including Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD). Real-time measurements of indoor environmental conditions were conducted in selected hospitals in Islamabad, Pakistan, utilizing calibrated sensors to capture ambient temperature, wet bulb globe temperature, relative humidity, air velocity, light intensity, and CO₂ levels. This empirical data was integrated with responses from thermal comfort questionnaires, assessing the perceptions of patients, medical staff, and visitors regarding thermal sensation, acceptability, preference, and overall comfort. The adaptive ML-based, predictive analysis identified optimal thermal comfort ranges for hospital wards, recommending indoor air temperatures between 22.0°C and 23.0°C, relative humidity levels between 50% and 55%, and air velocities between 0.1 and 0.2 m/s. The findings revealed a significant impact of overcooling and undercooling on PMV and PPD levels, emphasizing the need for precise HVAC system control to enhance both energy efficiency and occupant comfort. This research contributes to advancing adaptive thermal comfort modeling in healthcare facilities, offering insights for sustainable HVAC management and improved patient outcomes.

Fanger’s predicted mean vote (PMV) model which is as a result of climate-chamber-based experiments is a good tool to evaluate indoor thermal comfort for air-conditioned buildings in global wide. However, PMV model has defect of predicting people’s real thermal sensation under non-air-conditioned conditions. It is reflected by the significant discrepancies between PMV values and Actual Mean Vote (AMV) values. The aim of this study is to develop an Adaptive Predicted Mean Vote (aPMV) Model on the basis of ‘black box’ theory considering occupants’ adaptations to improve prediction performance. A field study was carried out in naturally ventilated educational buildings in Zunyi, China. The developed aPMV model produces more reliable results and shows better prediction performance, comparing with values predicted by PMV model. It indicates that aPMV model is of great benefit to connect traditional PMV model and adaptive comfort model and consequently to provide guidance on building design, operation and maintenance, which contribute to achieve building energy conservation and emission reduction target.

Modeling thermal comfort holistically: Bayesian estimation of thermal sensation, acceptability, and preference distributions for office building occupants

Evaluating the effects of passive cooling and heating techniques on building energy consumption in Kandahar using CLTD method

Methodology for calculating an atmospheric pressure-sensitive thermal comfort index PMVaps

Ventilation impacts on infection risk mitigation, improvement of environmental quality and energy efficiency for subway carriages

Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings

To address the critical need for accurate human thermal comfort prediction in winter heating environments, this study established a comprehensive thermal comfort dataset containing 2089 valid samples through experiments. On this basis, thermal comfort prediction models were constructed using three multi-class machine learning algorithms: Support Vector Classification, K-Nearest Neighbors, and Random Forest. The predictive performance of 63 different feature combinations was systematically evaluated. The results indicate that the feature subset comprising indoor air temperature, forehead temperature, cheek temperature, dorsal hand temperature, heart rate, and systolic blood pressure yields the optimal prediction performance. Among the evaluated models, the Random Forest model demonstrated superior overall performance, achieving an accuracy exceeding 90% and an AUC ranging from 96% to 99%, significantly outperforming the SVC and KNN models. Compared with the traditional Predicted Mean Vote (PMV) model, the machine learning models developed in this study showed a substantial improvement in prediction accuracy under identical conditions; notably, the Random Forest model improved accuracy by approximately 40% over the PMV model. Based on these findings, a smart heating system framework integrating environmental sensors, wearable devices, and intelligent control valves is proposed, providing a theoretical basis and technical approach for realizing personalized and energy-efficient heating control.

Recognition and prediction of individual thermal comfort requirement based on local skin temperature

ABSTRACTHospital facilities are normally very complex, which combined with patient requirements promote conditions for potential development of uncomfortable working conditions. Thermal discomfort is one such example. This study aimed to determine levels of thermal comfort, sensations, and preferences, from a field investigation conducted in two sterilization services (SS) of two hospitals from Porto and Aveiro, Portugal. The analytical determination and interpretation of thermal comfort was based upon assumptions of ISO 7726:1998 and ISO 7730:2005. The predicted mean vote (PMV) and predicted percentage of dissatisfaction (PPD) indices were obtained by measurement and estimation of environmental and personal variables, respectively, and calculated according to ISO 7730 equations. The subjective variables were obtained from thermal sensation (subjective PMV) and affective assessment (subjective PPD), reported by a questionnaire based upon ISO 10551:1995. Both approaches confirmed thermal discomfort in both SS (codified as SS1 and SS2). For all areas, PMV and PPD exceeded in all periods of the day the recommended range of –0.5 to +0.5 and <10%, respectively. No significant differences were found between day periods. The questionnaire results showed that SS2 workers reported a higher level of thermal discomfort. There were no significant differences between PMV and thermal sensations, as well as between PPD and affective assessment. The PMV/PPD model was found suitable to predict thermal sensations of occupants in hospital SS located in areas with a mild climate in Portugal.

Thermal comfort is requisite for creating interior environments, especially in educational facilities in tropical humid zones. It's important to appraise how well current thermal comfort models, especially the Predicted Mean Vote, work in actual classrooms, taking into account the different needs of users, including gender disparities. Thermal comfort states in five university classrooms (both naturally ventilated and air-conditioned) in an educational building were assessed during the hot season of 2024. In-situ assessments of indoor temperature, relative humidity, and air velocity were performed in conjunction with standardized questionnaires. A total of 336 valid student responses (177 male, 159 female) were analyzed to assess thermal sensation, preference, and acceptability. A comparative analysis was conducted on PMV predictions, emphasizing gender-based disparities. The majority of students reported feeling comfortable inside the ASHRAE-defined comfort range (−1 to +1), with statistically negligible variations (p > 0.05) found between male and female perceptions. However, female students generally favored slightly cooler indoor ambiances than male students. The PMV model matched well with real data in naturally ventilated classrooms but greatly overestimated comfort in air-conditioned classrooms, with differences of up to 1.32 points from the actual Thermal Sensation Votes (TSV). This study is unique because it combines a focus on gender with real-world testing of the PMV model in tropical buildings, which is something not often seen in today's research on building performances. This study emphasized the constraints of employing common thermal comfort models in tropical humid atmosphere. It reinforces the significance of using gender-sensitive viewpoints and adaptive comfort strategies in architectural design. The results offer experimental proof for reevaluating thermal comfort standards in the tropical humid zones.