Building Information Science & Technology Lab.

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