Building Information Science & Technology Lab.
성균관대학교 지능형건축설비연구실
BIST lab. is intended to realize intelligent buildings based on the building science, informatics, and building life-cycle data. We are actively pursuing progress on the different levels of sensors, equipment, systems, buildings, and a city and their interactions.
Research Areas
We focus on building informatics in
1. Sensing-level
건물 내 다양한 센싱기술 문제를 해결하기 위한 연구
✅ 현장중심 가상센서(In-situ virtual sensor)
✅ 현장 보정(Virtual in-situ calibration)
✅ 자율 모델링(Autonomous in-situ modeling)
BIST Insights & Updates
94 | GPT-based intelligent urban digital twins (I-UDT): Concept, methodology, and case studies, Smart Cities (under review) |
93 | DT-BEMS: Digital twin-enabled building energy management system for building energy efficiency and operational informatics, ENERGY (submitted) |
92 | GPT-based intelligent digital twins for building operations and maintenance, Advanced Engineering Informatics (submitted) |
91 | Nonintrusive in-situ modeling for unobserved virtual models in digital twin-enabled building HVAC systems: A one-year comparison of physics-based and data-driven approaches in a living laboratory, Journal of Building Engineering (under review) |
90 | Virtual in-situ calibration for building digital twins, Renewable and Sustainable Energy Reviews (submitted) |
89 | S. Choi, S. Yoon, GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies, Energy and Buildings 325 (2024) 115042, https://doi.org/10.1016/j.enbuild.2024.115042 |
88 | Autonomous In-situ Modeling for Virtual Building Models in Digital Twins, Automation in Construction (under review) |
87 | Demonstrating the use of absolute pressure sensors for monitoring stack-driven pressure differences in high-rise buildings, Building and Environment (under review) |
86 | J. Song, S. Yoon, Ontology-assisted GPT-based building performance simulation and assessment: Implementation of multizone airflow simulation, Energy and Buildings 325 (2024) 114983. https://doi.org/10.1016/j.enbuild.2024.114983 |
85 | S. Yoon, J. Lee, J. Li, P. Wang, Virtual In-situ Modeling between Digital Twin and BIM for Advanced Building Operations and Maintenance, Automation in Construction 168 (2024) 105823. https://doi.org/10.1016/j.autcon.2024.105823 |
84 | Multi-source data fusion-driven urban building energy modeling, Sustainable Cities & Society (under review) |
83 | Metadata schema for virtual sensors in digital twin-enabled building systems using Brick schema, Engineering Applications of Artificial Intelligence (submitted) |
82 | Enhancing 1D Convolutional Neural Network for Fault Detection and Diagnosis of Fan Coil Unit Using Multiple Datasets and Models, Journal of Building Engineering (under review) |
81 | Research on the sensor fault diagnosis and abnormal data repair of environmental control system in a terminal, Journal of Building Engineering (under review) |
80 | In-situ sensor calibration and fault-tolerant building HVAC control: an EnergyPlus-Python co-simulation testbed, Building and Environment (under review) |
79 | A digital twin platform-based sensor fault prediction and reverse traceability approach for smart energy systems, Energy and Buildings (under review) |
78 | Enhancing In-situ model accuracy in building systems with augmentation-based synthetic operation data, Journal of Building Engineering (under review) |
77 | Sensor fault diagnosis and calibration based on voting mechanism for online application using virtual in-situ calibration and time series prediction, Building and Environment (under review) |
76 | Model fusion algorithms for digital twinning in built environments, Sustainable Cities and Society (under review) |
75 | J. Jing, K.H. Ji, S. Yoon, J.H. Jo, A novel method for evaluating stack pressure in real high-rise buildings: optimization of measurement points, Building and Environment 259 (2024) 111661. https://doi.org/10.1016/j.buildenv.2024.111661 |
74 | J. Lee, S. Yoon, Metadata schema for virtual building models in digital twins: VB schema implemented in GPT-based applications, Energy and Buildings 327 (2025) 115039, https://doi.org/10.1016/j.enbuild.2024.115039 |
73 | S. Yoon, Virtual Building Models in Built Environments, Developments in the Built Environment 18 (2024) 100453. https://doi.org/10.1016/j.dibe.2024.100453 |
72 | G. Li, Y. Wu, S. Yoon*, X. Fang, Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning, Energy 299 (2024) 131395, https://doi.org/10.1016/j.energy.2024.131395 |
71 | S. Choi, S. Yoon, Change-point model-based clustering for urban building energy analysis: A case study on electricity energy data in commercial buildings, Renewable and Sustainable Energy Reviews 199 114514, https://doi.org/10.1016/j.rser.2024.114514. |
70 | In-situ backup virtual sensor application in building automation systems toward virtual sensing-enabled digital twins, Case Studies in Thermal Engineering (under review) |
69 | J. Wang, Y. Tian, Z. Qi, L. Zeng, P. Wang, S. Yoon, Sensor fault diagnosis and correction for data center cooling system using hybrid multi-label random Forest and Bayesian Inference, Building and Environment 249 (2024) 111124, https://doi-org-ssl.sa.skku.edu/10.1016/j.buildenv.2023.111124. |
68 | J. Li, P. Wang, Y. Li, Y. Rezgui, S. Yoon, T. Zhao, Analysis of sensor offset characteristics in building energy systems based on redundant sensors: A case study on variable air volume system, Energy and Buildings 306 (2024) 113957. https://doi.org/10.1016/j.enbuild.2024.113957 |
67 | S. Choi, H. Lim, J. Lim, S. Yoon, Retrofit building energy performance evaluation using an energy signature-based symbolic hierarchical clustering method, Building and Environment 251 (2024) 111206. https://doi.org/10.1016/j.buildenv.2024.111206 |
66 | S. Yoon, J. Lee, Perspective for waste upcycling-driven zero energy buildings, Energy 289 (2024) 130029, https://doi.org/10.1016/j.energy.2023.130029. |
65 | J. Koo, S. Yoon, Neural network-based nonintrusive calibration for an unobserved model in digital twin-enabled building operations, Automation in Construction 159 (2024). https://doi.org/10.1016/j.autcon.2023.105261 |
64 | P. Wang, J. Sun, S. Yoon, L. Zhao, R. Liang, A global optimization method for data center air conditioning water systems based on predictive optimization control, Energy 295 (2024) 130925, https://doi.org/10.1016/j.energy.2024.130925 |
63 | J. Koo, S. Yoon, Simultaneous in-situ calibration for physical and virtual sensors towards digital twin-enabled building operations, Advanced Engineering Informatics 59 (2024) 102239, https://doi.org/10.1016/j.aei.2023.102239 |
성균관대학교 지능형건축설비연구실 (Building Information Science & Technology lab.)
Address: (16419) 경기 수원시 장안구 서부로 2066 성균관대학교 자연과학캠퍼스 제1공학관 21동 21402호
Natural Science Campus: 21402A, 2066, Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea
Tel: 031) 290-7581