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In this study, 12 cases were investigated in an office-layout room using experiments and computational fluid dynamics (CFD) simulations. The heating performances of four ventilation methods (i.e. mixing ventilation (MV), stratum ventilation (SV), deflection ventilation (DeV) and impinging jet ventilation (IJV)) were comprehensively compared by various evaluation indexes (i.e. predicted mean vote (PMV), draught rate (DR), vertical air temperature difference (△T), air diffusion performance index (ADPI), energy utilization coefficient (EUC), air change efficiency (ACE) and contaminant removal efficiency (CRE)). Better thermal comfort was found in rooms heated by SV and DeV. The PMV, DR and △T under SV and DeV complied with Category B of ISO 7730:2005, and the ADPI was in full compliance with the stipulation of ANSI/ASHRAE 113-2022. For the energy-saving characteristic, the targeted-occupied-zone ventilation methods (i.e. SV, DeV and IJV) can effectively deliver warm air to the occupied zone, with the EUC values higher than unity and thus providing a good potential for energy saving. SV and IJV showed slightly higher ACEs in the breathing zone. The contaminant removal effectiveness of SV, DeV and IJV was comparable. Under the combined influence of occupant thermal plumes and locations of exhausts, MV showed a high CRE. However, the CRE under MV decreased significantly when the exhausts were not above occupants. In the case of supply air parameters in this study, the entropy-weight method indicated that DeV and SV had a better overall performance for winter heating, followed by IJV and then MV. [ABSTRACT FROM AUTHOR]

ASHRAE Global Thermal Comfort Database has been extensively used for analyzing specific thermal comfort parameters or models, evaluating subjective metrics, and integrating with machine learning algorithms. Outlier detection is regarded as an essential step in data preprocessing, but current publications related to this database paid less attention to the influence of outliers in raw datasets. This study aims to investigate the filter performance of different outlier detection methods. Three stochastic-based approaches have been performed and analyzed based on the example of predicting thermal preference using the Support Vector Machine (SVM) algorithm as a case study to compare the predictions before and after outlier removal. Results show that all three rules can filter some obvious outliers, and the Boxplot rule produces the most moderate filer results, whereas the 3-Sigma rule sometimes fails to detect outliers and the Hampel rule may provide an aggressive solution that causes a false alarm. It has also been discovered that a small reduction in establishing machine learning models can result in less complicated and smoother decision boundaries, which has the potential to provide more energy-efficient and conflict-free solutions. [Display omitted] • Three stochastic-based outlier removal rules were applied to the ASHRAE database. • The filtering performance was validated using simulation and ASHRAE database data. • The data distribution influences the selection of outlier removal rules. • SVM was chosen to demonstrate the effects of outlier removal predictive outcomes. • This work selects the optimal outlier removal rule for data distribution. [ABSTRACT FROM AUTHOR]

This research aims to investigate human thermal responses to air humidity in warm and hot environments and to evaluate the effect of humidity on human thermal comfort. 20 subjects were involved in 12 exposure experiments in a well-controlled climate chamber at three relative humidity levels (40%, 60%, 80%) and four air temperature levels (26 °C, 28 °C, 30 °C, 32 °C) with little indoor airflow. The physical environmental and physiological parameters, as well as subjective questionnaires, were collected simultaneously with the on-going experiments. The results show that in hot environments, particularly when the air temperature exceeds 30 °C, the relative humidity has a significant effect on human thermal responses both physiologically and subjectively. The Standard Effective Temperature (SET) is biased when evaluating human thermal comfort in hot-humid environments without considering human thermal adaptation to humidity. Hence, a humidity correction coefficient e RH is proposed to modify the deviation of the SET under different relative humidity levels, and to quantify the effect of humidity on human acceptable air temperatures. The modified acceptable temperature-humidity zone has been obtained using the modified method. [ABSTRACT FROM AUTHOR]