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Sungmin Yoon

Associate Professor

Sungkyunkwan University

South Korea

Research Interests

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About

Dr. Sungmin Yoon is an Associate Professor at Sungkyunkwan University in South Korea. His research primarily focuses on urban building energy systems, artificial intelligence applications in building operations, and digital twin technologies. Recent publications include works on Bayesian inference for energy data resilience and AI agent-driven solutions for improving building management efficiency.

Articles (20)

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2027

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2026

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Collaborators (2)

Deuk-Woo Kim

Senior Researcher

Korea Institute of Civil Engineering and Building Technology

SOUTH KOREA

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Jiteng Li

Sungkyunkwan University

SOUTH KOREA

View details

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Sungmin Yoon

Research Interests

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About

Articles (20)

Autonomous Process Generator (APG): decoupling process design and execution for agentic building services

Agentic fault detection and diagnosis for building HVAC systems using AI agent, ontology, and diagnosis rules

A knowledge-based multi-agent system using large language models for automated building energy modeling and simulation service

Metadata schema for virtual sensors in digital twin-enabled building systems using Brick schema

Quantifying urban building shading effect for data-driven building energy modeling

A two-stage imputation method for enhancing urban building energy data resilience using Bayesian inference

Fault diagnosis method for data imbalance in chiller systems based on CWGAN-GP and DS evidence theory fusion

Ontology-enabled AI agent-driven intelligent digital twins for building operations and maintenance

Virtual in-situ calibration for digital twin-synchronized building operations

DT-BEMS: Digital twin-enabled building energy management system for information fusion and energy efficiency

AI agent-based indoor environmental informatics: Concept, methodology, and case study

Autonomous in-situ modeling for virtual building models in digital twins

Identifying occupant behavioral impacts on stack effect in high-rise residential buildings: Field measurements

Sensor fault diagnosis and calibration based on voting mechanism for online application using virtual in-situ calibration and time series prediction

Enhancing in-situ model accuracy in operational building systems with augmentation-based synthetic operational data

AI agent-driven virtual in-situ calibration for intelligent building digital twins

AI agent-based virtual model development, diagnosis, and calibration for building digital twins

Anomaly detection using convolutional autoencoder with residual gated recurrent unit and weak supervision for photovoltaic thermal heat pump system

Nonintrusive in-situ modeling for unobserved virtual models in digital twin-enabled building HVAC systems: A one-year comparison of data-driven and physics-based approaches in a living laboratory

Model fusion algorithms for digital twinning in built environments: Extending behavioral models in a real HVAC system

Collaborators (2)

Deuk-Woo Kim

Jiteng Li

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