Seungjae Lee

Assistant Professor

Department of Civil & Mineral Engineering

Email: sjae.lee@utoronto.ca
Office: GB228
Tel: 416-946-8408

Hub for Advancing Buildings

Expertise:

Areas of Specialization:

Building Science

Building Engineering

Background Research Interests Education Affiliations

Background

Prof. Seungjae Lee’s research focuses on developing scalable Artificial Intelligence (AI) solutions for building energy systems to improve building energy performance, indoor environmental quality, and grid reliability and resilience. His research pursues seamless integration of building science domain knowledge and data to develop feasible, effective, reliable, and human-understandable solutions. Modern probabilistic machine learning, causal inference, and stochastic optimal control technologies are core tools in his research. Prof. Seungjae Lee’s research interests include optimizing the design and operation of building energy systems and developing effective user interfaces and eco-feedback programs based on a holistic understanding of human-building interactions.

Research Interests

Education

Affiliations

Publications

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Journal Publications
X Liu, S Lee, I Bilionis, P Karava, J Joe, SA Sadeghi (2021). A user-interactive system for smart thermal environment control in office buildings. Applied Energy 298, 117005.
L Zhang, M Leach, Y Bae, B Cui, S Bhattacharya, S Lee, P Im, V Adetola, D Vrabie, T Kuruganti (2021). Sensor impact evaluation and verification for fault detection and diagnostics in building energy systems: A review. Advances in Applied Energy, 100055.
D Lee, N He, S Lee, P Karava, J Hu (2021). Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction. arXiv preprint arXiv:2103.07919.
SA Sadeghi, S Lee, P Karava, I Bilionis, A Tzempelikos. (2020). A smart and less intrusive feedback request algorithm towards human-centered HVAC operation. Building and Environment 184, 107190
S Lee, P Karava (2020). Towards smart buildings with self-tuned indoor thermal environments–A critical review. Energy and Buildings 224, 110172
P Im, Y Bae, S Lee, JR New (2020). Empirical Validation of Multi-Zone HVAC System Model: Evaluation of Existing Infiltration Models used in Building Energy Simulation. Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States)
P Im, Y Bae, B Cui, S Lee, S Bhattacharya, V Adetola, D Vrabie, L Zhang (2020) Literature Review for Sensor Impact Evaluation and Verification Use Cases-Building Controls and Fault Detection and Diagnosis (FDD). Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States)
S Lee (2019). DEVELOPMENT OF SELF-TUNED INDOOR THERMAL ENVIRONMENTS. Purdue University Graduate School
S Lee, P Karava, A Tzempelikos, I Bilionis. (2019) Integrating occupants’ voluntary thermal preference responses into personalized thermal control in office buildings. Journal of Physics: Conference Series 1343 (1), 012138
S Lee, J Joe, P Karava, I Bilionis, A Tzempelikos (2019). Implementation of a self-tuned HVAC controller to satisfy occupant thermal preferences and optimize energy use. Energy and Buildings 194, 301-316