The Human Behaviour and Energy Laboratory (HuBEL) at the Kyung Hee University was founded in 2010. A central aim of the HuBEL is to contribute to creating carbon efficient and healthy buildings and cities by carrying out innovative research in the area of adaptive comfort, occupant behaviour, energy efficient control algorithm, building energy management system, deep neural network models, the energy-efficiency and renewable energy potentials of buildings and urban space, the health performance of buildings, urban climatic maps, and implications of climate change.
Human factors in building performance is a key research area of HuBEL. The HuBEL has developed and adaptive comfort and behavioural models of control systems for thermal and luminous environments through the rigorous statistical analysis of field measurement data. The energy efficient control algorithms, which enable the application of the behavioural models to Building Energy Management System (BEMS), have been created and tested. The recent research of the HuBEL includes the development of real-time simulation with BEMS, the use of machine learning (deep learning, reinforcement learning) for the design and operation of buildings at urban scales.
Another key research area is the visual perception of occupants and the application of advanced lighting systems. The HuBEL has investigated the visual effects of different spectral power distributions of light sources, the use of lighting systems in practice, and their energy implications through a series of experiments, field measurements, and simulations. Recently, the HuBEL has been testing the subjective responses of people to organic lighting emitting device (OLED) lighting and been developing the lighting strategies to apply OLED in buildings in a carbon efficient way, which can maximise the comfort and well-being of building occupants.
Adilkhanova, I., Jeong, J. H., Yun, G. Y., Lee, K. S., Kim, H. A., Kim, S. J., & Lee, S. H. (2025). Development of Reference Energy Models for Office Buildings in Korea. Journal of the Korean Institute of Architectural Sustainable Environment and Building Systems, 19(3).
Raj, S., Yerim, L., Yun, G. Y., & Santamouris, M. (2025). Contrasting urban heat disparities across income levels in Seoul and London. Sustainable Cities and Society, 121, 106215.
Bae, K. W., Choi, E. J., Choi, Y. J., Yun, J. Y., Yun, G. Y., Moon, H. J., & Moon, J. W. (2025). Real-time ventilation control for indoor CO2 management using occupant information. Building and Environment, 262, 113568.
Adilkhanova, I., Jeong, J. H., & Yun, G. Y. (2025). The role of geographic scale of weather data in urban building energy models. Sustainable Cities and Society, 125, 106339.
Ngarambe, J., Raj, S., & Yun, G. Y. (2025). Subsurface urban heat islands: From prevalence and drivers to implications for geothermal energy and a proposed new framework based on machine learning. Sustainable Cities and Society, 106153.
Jalbuena, R., Yee, J. J., Yun, G. Y., & Raj, S. (2025). Addressing landcover bias in spatial downscaling of MODIS land surface temperature using generative adversarial network-based regression model (RGAN). Advances in Space Research, 76(6), 3445-3464.
Murtaza, S., Raj, S., Yun, G. Y., Park, D. J., Kim, J. H., Park, G., & Moon, J. W. (2025). Adaptive neural temporal hybridization for missing data imputation in building energy use datasets: An integrated LNN-LSTM weighted model. Journal of Building Engineering, 102, 113774.
Zo, C. H., Nzarigema, J. D. A., Ngarambe, J., Raj, S., Muhammad, S., Yoo, G., & Yun, G. Y. (2025). Integrating deep learning into quantile regression models for enhanced building energy benchmarking. Journal of Building Engineering, 109, 113044.
Raj, S., & Yun, G. Y. (2025). Exploring the role of strategic urban planning and greening in decreasing surface urban heat island intensity. Journal of Asian Architecture and Building Engineering, 24(2), 866-879.