Xiaojun Fan


Last Name

Fan

First Name

Xiaojun

ORCID

0000-0001-5775-7715

Organisational unit

Filters

2024 - 20253
Reset filters

Search Results

Publications 1 - 3 of 3
  • Comparison of psychophysiological responses and cognitive performance between radiant- and air- based systems at varied temperature and ventilation conditions: A living laboratory study
    Item type: Journal Article
    Fan, Xiaojun; Silvestri, Alberto; Lydon, Gearóid Patrick; et al. (2025)
    Energy and Buildings
    Radiant-based systems are often assumed to provide superior thermal comfort over air-based systems, yet the evidence remains unclear. Moreover, how these systems affect comfort and cognitive performance under varying temperature and ventilation settings is still under investigation. To address these knowledge gaps, we conducted a study in which sixteen subjects experienced six experimental conditions in the HiLo living laboratory. These include two temperatures (24 and 27 °C), established by either a thermally activated building system (TABS − a radiant-based system) or a hybrid variable refrigerant flow system (HVRF − an air-based system). Two ventilation scenarios were tested using an independent mechanical ventilation system (on vs. off). Each condition was experienced by four subjects at a time for 125 min in a balanced order. Skin temperature, core body temperature, and heart rate and variability were continuously monitored. Mental arithmetic ability, executive function, and logical reasoning tasks were used as measures of cognitive performance. Thermal comfort, perceived air quality, sick building syndrome symptoms, and fatigue were rated by the subjects. While a few significant differences in human responses between TABS and HVRF were observed, the effect sizes were small (d < 0.5), and the patterns were inconsistent across different temperature and ventilation conditions. These results suggest that air-based systems can provide comfort levels comparable to radiant-based systems. When the environmental conditions are equivalent, cognitive performance remains unaffected by the type of system used.
  • Mediating effects of ventilation on the impacts of temperature on human comfort, health and cognitive performance: A living lab study
    Item type: Journal Article
    Fan, Xiaojun; Wargocki , Pawel; Silvestri , Alberto; et al. (2025)
    Building and Environment
    The independent effects of increased temperature and reduced ventilation on human comfort, health, and cognitive performance are well documented. However, how ventilation moderates the impacts of temperature on humans remains underexplored. This living lab study investigated thermal comfort, acute health symptoms, physiological responses and cognitive performance under three experimental conditions: two temperatures (24 and 27 °C) and two ventilation scenarios (mechanical ventilation on at 215 m3/h and off). Sixteen subjects were exposed to each condition for 125 min in a balanced order. At 27 °C, when the ventilation system was turned off, the mean CO₂ concentration increased from 800 ppm to 1900 ppm. Subjects felt warmer and rated the thermal environment as less acceptable compared to the ventilated condition. The increase in mean skin temperature and decrease in core body temperature were further significantly aggravated. Compared to 24 °C with ventilation, subjects reported greater fatigue and sleepiness and rated the air quality even worse, along with a declining trend in cognitive performance (P < 0.05, d < 0.5), even though no significant independent effects of increased temperature or reduced ventilation were observed within the short exposure duration. These findings suggest that ventilation mediates the effects of temperature, even within typical indoor ranges, and highlight noteworthy interactions between temperature and ventilation. These results support the revision of the current temperature and ventilation requirements in existing standards, particularly in the context of a warming world.
  • A Comparative Analysis of Multi-Target Feature Selection Methods in Data-Driven Models for Building Energy and Thermal Performance Prediction
    Item type: Conference Paper
    Borkowski, Esther; Fan, Xiaojun; Schlueter, Arno (2024)
    Computing in Construction ~ Proceedings of the 2024 European Conference on Computing in Construction
    Building energy management increasingly utilises Machine Learning (ML) to use data from sensor-rich environments. A significant challenge in this context is managing high-dimensional data, which can affect model performance. This study addresses this by applying multi-target feature selection, an underexplored method that reduces dimensionality by analysing inter-feature relationships. From 182 features, two were key for developing three ML models predicting the energy and thermal performance of the HiLo living lab. These models achieved a robust fit with an average Root Mean Squared Error (RMSE) of 0.18 kW and 1.03 °C, demonstrating multi-target feature selection’s effectiveness in enhancing building performance predictions.
Publications 1 - 3 of 3