Choongwan Koo

Associate Professor

Incheon National University

South Korea

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Choongwan Koo is an Associate Professor at Incheon National University in South Korea. His research areas include machine learning applications for thermal comfort, cognitive states under heat stress, and energy-efficient building designs. He has published several articles focusing on innovative strategies for enhancing residential welfare, particularly for older adults, and employing technology in construction safety training and energy management.

Articles (20)

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2025

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2022

Collaborators (7)

Tae Wan Kim

Assistant Professor

Incheon National University

SOUTH KOREA

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Daniel Castro-Lacouture

Purdue University

UNITED STATES

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Han-Guk Ryu

Sahmyook University

SOUTH KOREA

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Seung Hyun Cha

Associate Professor

Korea Advanced Institute of Science and Technology

SOUTH KOREA

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Jung In Kim

Kyunghee University

SOUTH KOREA

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Jeehee Lee

Assistant Professor

University of Nevada

UNITED STATES

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Kwangbok Jeong

Assistant Professor

Sejong University

SOUTH KOREA

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Choongwan Koo

Research Interests

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About

Articles (20)

A simplified machine learning model to forecast individual thermal comfort in older adults’ residential spaces without relying on wearable devices

Managing energy consumption and indoor environment quality using augmented reality based on the occupants’ satisfaction and characteristics

A living lab to develop smart home services for the residential welfare of older adults

Impact of Heat Stress on Individual Cognitive States: Utilizing EEG Metrics in Immersive VR–Based Construction Safety Training

Impact of interactive learning elements on personal learning performance in immersive virtual reality for construction safety training

Thermal and visual comforts of occupants for a naturally ventilated educational building in low-income economies: A machine learning approach

Empirical adaption of Work/Rest schedules to physiological variability in heat response

Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning

Energy consumption prediction and household feature analysis for different residential building types using machine learning and SHAP: Toward energy-efficient buildings

A Classification Model Using Personal Biometric Characteristics to Identify Individuals Vulnerable to an Extremely Hot Environment

Indoor environmental quality improvement in green building: Occupant perception and behavioral impact

Clustering-based Model for Identifying Individual Differences in Vulnerability to Heat Strain-Using a Means Difference Analysis of Personal Biometric Characteristics-

Simulating travel paths of construction site workers via deep reinforcement learning considering their spatial cognition and wayfinding behavior

Automated Components–Vehicle Allocation Planning for Precast Concrete Projects

Automated Generation of Precast Concrete Slab Stacks for Transportation in Offsite Construction Projects

EEG-Based Circumplex Model of Affect for Identifying Interindividual Differences in Thermal Comfort

Integrated Approach to Evaluating the Effect of Indoor CO2 Concentration on Human Cognitive Performance and Neural Responses in Office Environment

A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns

An interpretable machine learning approach for evaluating the feature importance affecting lost workdays at construction sites

Integrated approach to evaluating the impact of feed-in tariffs on the life cycle economic performance of photovoltaic systems in China: A case study of educational facilities

Collaborators (7)

Tae Wan Kim

Daniel Castro-Lacouture

Han-Guk Ryu

Seung Hyun Cha

Jung In Kim

Jeehee Lee

Kwangbok Jeong

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