Young M. Lee
- Data-driven control of airborne infection risk and energy use in buildings. In: Building and Environment, v. 245 (November 2023). (2023):
- A timeseries supervised learning framework for fault prediction in chiller systems. In: Energy and Buildings, v. 285 (April 2023). (2023):
- Modeling and multiobjective optimization of indoor airborne disease transmission risk and associated energy consumption for building HVAC systems. In: Energy and Buildings, v. 253 (Dezember 2021). (2021):
- Quantifying the Tradeoff Between Energy Consumption and the Risk of Airborne Disease Transmission for Building HVAC Systems. In: Science and Technology for the Built Environment, v. 28, n. 2 (Dezember 2021). (2021):
- Building HVAC control with reinforcement learning for reduction of energy cost and demand charge. In: Energy and Buildings, v. 239 (Mai 2021). (2021):
- Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings. In: Energy and Buildings, v. 111 (Januar 2016). (2016):