Search Results (31)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

Since gaining independence in 1962, Algeria has significantly developed its tourism infrastructure, including notable projects by Fernand Pouillon. The thermal performance of hotel buildings, measured by discomfort hours and considering the design parameters for both PMV-PPD and adaptive comfort models, is a crucial study area across Algeria’s eight climate zones. This research focuses on the M’Zab Hotel in Ghardaïa, designed by Pouillon, utilising in situ measurements and dynamic simulations with EnergyPlus. After validating the simulation model, the performance of the optimised model, derived from sensitivity analysis parameters, is explored. A comparative study is conducted, analysing results obtained through simulation and psychrometric charts for both comfort models across Algeria’s climate zones. The findings indicate that the optimised design significantly reduces discomfort hours by 27.9% to 54.8% for the PMV-PPD model and 38.8% to 90.3% for the adaptive model, compared to the actual design performance. Strong correlations are observed between the simulation and psychrometric chart results for the PMV-PPD model, while the correlation for the adaptive model requires further investigation. Full article

Local data about indoor thermal comfort are in short supply, which are always different from the predicted results produced by models shown in previous studies. Shopping malls that consume substantial energy need to save energy, provided that thermal comfort is maintained. Therefore, this research investigated indoor thermal comfort using field measurements and questionnaires in a typical shopping mall in Danyang, China, with a hot summer and cold winter climate in order to explore local demands and energy-saving potential. The findings are as follows: (1) The average air temperature ($T_a$) and operative temperature ($T_{op}$) are 26.7 °C and 26.4 °C, which implies a minor influence from radiation and other factors on $T_a$. Women are more sensitive to changes in outdoor temperature since clothing insulation ($I_{cl}$) varies by gender: 0.31 clo and 0.36 clo for male and female individuals, respectively. (2) The thermal neutral temperature (TNT) derived from the thermal sensation vote (TSV) is 25.26 °C, which is significantly higher than the 21.77 °C obtained from the predicted mean vote (PMV) model. (3) There is a wide range of acceptable temperatures for thermal comfort because the highest temperature was identified by the thermal comfort vote (TCV) at 27.55 °C, followed closely by 27.48 °C, 26.78 °C, and 25.32 °C, which were separately derived from the thermal acceptance vote (TAV), TSV, and predicted percentage of dissatisfied (PPD) people; these were based on an upper limit of the acceptable 80% range. (4) In total, 94.85% of respondents accepted the indoor air quality, although the median concentration of CO₂ was 772 ppm, and the neutral relative humidity level was 70.60%. Meanwhile, there is an important relationship between air quality satisfaction and operative temperature; thus, the temperature (26.93 °C) with peak satisfaction can enhance air quality perception and thermal comfort. (5) The energy savings that can be achieved are 25.77% and 9.12% at most based on acceptable thermal comfort compared with baseline energy consumption at 23 °C and 26 °C, respectively. Full article

Researchers have increasingly focused on thermal comfort, examining both individuals’ thermal sensations and the percentage of people dissatisfied with the thermal environment. Most studies rely on the widely used PMV (Predicted Mean Vote) model and the PPD (Predicted Percentage of Dissatisfied) value derived from it, both defined by the ISO 7730:2005 standard. However, previous studies have shown that this standardized method only applies under steady-state conditions, which do not reflect the dynamic nature of everyday environments. As closed-loop control technologies gain prominence in building services, the need to evaluate thermal comfort under time-varying conditions has grown. The standard method does not account for the thermal inertia of the human body, which limits its applicability in such dynamic contexts. In this study, we develop a method to estimate instantaneous thermal sensation under non-stationary conditions by incorporating thermal inertia through signal processing techniques. This approach addresses a well-recognized limitation of the standard PMV–PPD model and provides a way to assess thermal comfort in real time. We collected experimental data using a thermal comfort measurement station, a thermal manikin, and human subjects in a controlled climate chamber. The proposed method enables real-time evaluation of thermal comfort in dynamic environments and offers a foundation for integration into HVAC control and comfort optimization systems. Full article

There are three main approaches to human thermal comfort; a psychological approach, a thermo-physiological approach, and an approach based on human energy balance. According to the ISO 7730 and ASHRAE Standard 55-2023 standards, the psychological approach defines thermal comfort as a mental state in which individuals feel satisfied with their surrounding environment. According to this definition, thermal comfort is very subjective and may vary between individuals, as well as according to the environment and climate. This study aimed to evaluate the thermal comfort levels of students in primary and high school classrooms situated within the semi-arid climatic conditions of Şanlıurfa. For this purpose, 15 Temmuz Şehitleri Secondary School, Kadir Evliyaoğlu College, and TOBB Science High School in Şanlıurfa were chosen as fieldwork locations. Within the scope of the study, the climatic conditions (classroom temperature, air velocity, humidity, radiant temperature, Tw, Tg carbon dioxide) were measured, and how the students felt under the thermal conditions of these classrooms was evaluated. The study encompasses both the heating season (winter) and the non-heating season (summer). Based on the findings obtained from the study, PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) values and whether they are suitable thermal comfort for the people in these places tried to be determined by mathematical modeling and standards such as ASHRAE Standard 55-2023. While PMV values ranged between −0.58 (North) and 2.53 (East+South+West), PPD values were observed between 5% (South and some North facades) and 94% (East+South+West). While the South facade offers values close to the comfort range of 0.01–0.02 in terms of PMV, the East+South+West facade shows serious thermal discomfort with a PMV value of 2.53 and a PPD value of 94%. Full article

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems. Full article

Different spatial forms affect the indoor thermal environment and human thermal comfort. A good living environment largely depends on the flexibility of spatial forms, and spatial scale and proportion are the key factors affecting these forms. We selected typical residential houses in the middle reaches of the Hanjiang River in the hot summer and cold winter climate area as an example. Through on-site measurements and questionnaire surveys, we studied the impact of residential form indicators on the thermal environment and thermal comfort. We also established a multivariate model to explore the correlation among various parameters. The results showed that the spatial-real ratio of the residential spatial form index in the middle reaches of Hanjiang River was 5–58%. The height from the ground was 2.23–6.92 m. The open-space ratio was 0.04–4.55. The explanatory power of the spatial form index to indoor air temperature was 57.5%, with a strong correlation (R² = 0.675). The explanatory power for humidity was 38.2%, with a weak correlation (R² = 0.525). The explanatory power of SET was 30.6–50.1%, with a weak correlation (R² = 0.466). The explanatory power of PMV was 6.5–31.7%, and PMV1.0 was weakly correlated (R² = 0.474). The explanatory power for PPD was 15.5%, where PPD 1.0 was close to a weak correlation (R² = 0.508). The results of this study provide reference values for the design methods of and decision-making process for green and energy-saving regional buildings. Full article

This paper considers microclimate to be one of the main contributors to thermal comfort in educational buildings. The influence of microclimate on well-being and productivity is considered. The role of microclimatic parameters is assessed from the perspective of building design, focusing on approaches to regulating these parameters. We also describe the formation of microclimate and the factors directly affecting it. The state of the microclimate of classrooms of an educational institution was analyzed, providing estimates of people’s real thermal sensations. The microclimate was assessed by the Fanger method. The PMV and PPD comfort indices were calculated for this purpose. The calculations were carried out thrice, i.e., based on the data obtained by using measuring equipment, based on the data from the survey and based on a SolidWorks model. Calculations in the program were carried out to validate the measured values and visualize the process of the distribution and localization of comfort indices. The results confirm that the indoor microclimate was generally favorable, and the PMV values obtained from the survey of people’s real sensations of thermal comfort were higher than the calculated PMV values. It was established that the PMV and PPD values corresponding to the largest deviations from the norm were as follows: −0.74/17% (PMV/PPD) for the calculation based on the real values of microclimatic parameters and 0.70/15.3% (PMV/PPD) for the calculation based on people’s thermal sensations. For applying the Fanger method for thermal comfort analysis in an educational institution in St. Petersburg, we upgraded the procedure, introducing a questionnaire survey. The mean PMV values calculated by the Fanger method were 0.16 lower than the PMV values obtained by the survey. Full article

Thermal comfort is increasingly recognized as vital in healthcare facilities, where patients spend 80–90% of their time indoors. Sensing, controlling, and predicting indoor air quality should be monitored for thermal comfort. This study examines the effects of ventilation design on thermal comfort in hospital rooms, proposing four distinct ventilation configurations, each with three airflow rates of 9, 12, and 15 Air Changes per Hour (ACH). The study conducted various ventilation simulation scenarios for a hospital room. The objective is to determine the effect of airflow and the diffuser location distribution on thermal comfort. The Reynolds-Averaged Navier–Stokes (RANS) equations, along with the k–ε turbulence model, were used as the underlying mathematical representation for the airflow. The boundary conditions for the simulations were derived from the ventilation standards set by the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) and insights from previous studies. Thermal comfort and temperature distribution were assessed using indices like Predicted Percentage Dissatisfaction (PPD), Predicted Mean Vote (PMV), and Air Diffusion Performance Index (ADPI). Although most of the twelve scenarios failed to attain thermal comfort, two of those instances were optimal in this simulation. Those instances involved the return diffuser behind the patient and airflow of 9 ACH, the minimum recommended by previous studies. It should be noted that the ADPI remained unmet in these cases, revealing complexities in achieving ideal thermal conditions in healthcare environments. This study extends the insights from our prior research, advancing our understanding of ventilation impacts on thermal comfort in healthcare facilities. It underscores the need for comprehensive approaches to environmental control, setting the stage for future research to refine these findings further. Full article

In electric buses, heating, ventilation and air conditioning are responsible for up to 50% of the energy consumption. It is therefore necessary to identify improved thermal settings to minimize the energy consumption, while guaranteeing good thermal comfort. Hence, an accurate prediction of the passengers’ thermal sensation (TS) is needed. One of the most widely used models for TS prediction is the PMV-PPD model, which has been shown to provide reliable results in uniform, steady-state climatic conditions. Since these are not present in an urban bus, the accuracy of the PMV-PPD model diminishes. Additionally, some of the parameters needed are difficult to obtain (i.e., clothing insulation). This paper presents seven different machine learning models (ML) for the prediction of TS using three different sets of parameters. The first set comprises five parameters similar to the PMV-PPD model, the second uses only two, and the third uses all parameters available. To obtain the necessary data, climatic measurements in an electric bus in Berlin, Germany, were made. These measurements were performed in summer for ambient temperatures between 14.7 °C and 32.0 °C. Person-related information as well as the thermal comfort assessment were obtained via surveys. Despite the relatively small data set, four of our seven ML models performed well with a median accuracy between 70.3% and 69.4%. This could also be observed when using only two parameters. Hence, the efforts to gain experimental data can be reduced significantly. For the PMV-PPD model, a median shift of +1 was observed for mild and warm TS. The median accuracy rises from 48.8% without shift to 68.8% with shift. Full article

Thermal discomfort body language has been shown to be a psychological representation of personnel’s particular thermal comfort. Individual thermal comfort differences are ignored in public building settings with random personnel flow. To solve this issue, we suggested a Bayesian group thermal dissatisfaction rate prediction model based on thermal discomfort body language expression and subsequently implemented intelligent indoor temperature and humidity control. The PMV-PPD model was utilized to represent the group’s overall thermal comfort and to create a prior distribution of thermal dissatisfaction rate. To acquire the dynamic distribution of temperature discomfort body language, data on thermal discomfort body language expression were collected in a real-world office setting experiment. Based on Bayesian theory, we used personalized thermal discomfort body language expressions to modify the group’s universal thermal comfort and realized the assessment of the thermal dissatisfaction rate by combining commonality and personalization. Finally, a deep reinforcement learning system was employed to achieve intelligent indoor temperature and humidity control. The results show that when commonality and personalized thermal comfort differences are combined, real-time prediction of thermal dissatisfaction rate has high prediction accuracy and good model performance, and the prediction model provides a reference basis for reasonable indoor temperature and humidity settings. Full article

Human performance and health are among the most relevant topics in modern society, especially at young ages, when academic performance is indispensable. Thus, as humans spend most of their life inside a building, thermal comfort and indoor air quality are essential aspects of a room. The aim of the current study is to numerically evaluate the main thermal comfort parameters such as PMV and PPD as well as indoor air quality, i.e., CO₂ concentration, in library group study rooms at the University of Gävle in Sweden. Rotroninc Measurement Solutions CL11 sensors were utilized for temperature measurements. Simulation models were created and validated based on building data as well as temperature measurements. Several simulations were conducted throughout the year, covering different periods. The results show that even though the ventilation system, with only temperature control, works as intended for maintaining thermal comfort, the CO₂ concentration rises above 1000 ppm when more than one student occupies the rooms, which is not recommended by different thermal comfort ruling institutions. Consequently, a modification to the ventilation system control is recommended, changing it from temperature control to CO₂ and temperature control. Full article

Forced ventilation is applied in clean rooms, specifically operating rooms, to ensure the health of both the patient and the medical staff. Ventilation reduces the risk of patient contamination, and its parameters are legally prescribed. In addition to preventing contamination, the ventilation system also ensures the creation of a comfortable environment for personnel who spend a large amount of working time in the operating room. This research focuses on the appropriate design of the air flow rate from the distribution element to the operating room. The PMV and PPD indexes were used to evaluate thermal comfort for human beings. The measurements of parameters determining thermal comfort took place in a laboratory with an accurate model of the operating room, including equipment and HVAC system, during the summer months, in cooling mode. Discharge speeds in the range of 0.15–0.175 m/s were evaluated as the most comfortable, with the PPD index ranging up to 22%. There was a significant increase in user dissatisfaction up to the limit of 70% at higher discharge speeds. Full article

Urban green space can improve the local thermal environment and thus the quality of the urban residential environment. Taking the green space of Beijing Olympic Forest Park (BOFP) as an example, this study analysed sample points representing different plant community structures, plant community types, and landscape environments based on 15 years of continuous dynamic measurement and selected typical annual data (from 2020). The study analysed and explained the spatial differentiation characteristics of human thermal comfort (HTC) in green space areas of BOFP using the predicted mean vote (PMV)–predicted percentage dissatisfied (PPD) physical comfort index model, which comprehensively considers both the objective environment and people’s subjective feelings and psychological states. The results showed that the level of HTC in the park’s green space, across community types and across typical landscape environments, differed between areas with different community structures. PMV–PPD mathematical model fitting further verified the above results. Full article

The thermal comfort of preschool children was assumed to be similar to that of young adults, which may cause inaccuracy. This study tested and analyzed the thermal response characteristics of young adults and preschool children (4–6 years old) and the differences in thermal sensation and thermal physiology between the two groups of participants in a room with a radiant floor heating system using the difference analysis methods (the paired data t-test, the Mann–Whitney U test and the Kruskal–Wallis H test). Participants were divided into two groups, young adults and preschoolers, and were sat in each condition while wearing winter clothing with a thermal resistance of 1.02 clo. The results showed that when the indoor temperature changed, there was a significant difference in the local skin temperature of the calf between the two groups of participants (p < 0.05). Preschool children adapt to the thermal environment better than adults, and the difference in metabolic rate is one of the influencing factors. The overall thermal sensation with mean skin temperature of the different populations was linearly correlated; correlation coefficients were 0.944 and 0.932, respectively. The overall thermal sensation of the participants was linear with respect to the indoor operative temperature. Preschool children have a higher thermal sensitivity to temperature change than young adults under low-temperature radiant floor heating systems, indicating that children have different thermal awareness from adults. There were significant differences in preschoolers’ subjective assessments of thermal sensation when the predicted mean vote (PMV) model was used as the evaluation standard; the difference ranged from 0.77 to 2.33. Thus, the PMV-predicted percentage dissatisfied (PPD) model is not suitable for preschool children. Full article