Cheol Soo Park 
- (2024): Local vs. federated cooling control for an office space with heat pump and photovoltaic systems. In: Energy and Buildings, v. 321 (October 2024).
- (2024): Performance gap analysis for Korean building energy efficiency certification. In: Energy and Buildings, v. 314 (July 2024).
- (2024): Application of transfer learning to overcome data imbalance and extrapolation for model predictive control: A real-life case. In: Energy and Buildings, v. 311 (May 2024).
- (2023): Limitations and issues of conventional artificial neural network-based surrogate models for building energy retrofit. In: Journal of Building Performance Simulation, v. 17, n. 3 (October 2023).
- (2023): Integrated control of radiant floor heating systems in residential buildings. In: Journal of Building Performance Simulation, v. 17, n. 2 (September 2023).
- (2023): Predictability and periodicity of occupant lighting control in high-rise residential buildings. In: Energy and Buildings, v. 297 (October 2023).
- (2023): Quantification of occupant response to influencing factors of window adjustment behavior using explainable AI. In: Energy and Buildings, v. 296 (October 2023).
- (2023): Linear regression based indoor daylight illuminance estimation with simple measurements for daylight-linked lighting control. In: Journal of Building Performance Simulation, v. 16, n. 5 (March 2023).
- (2023): Real-time predictive control of HVAC systems for factory building using lightweight data-driven model. In: Journal of Building Performance Simulation, v. 16, n. 5 (March 2023).
- (2023): Real-time model predictive cooling control for an HVAC system in a factory building. In: Energy and Buildings, v. 285 (April 2023).
- (2022): Implementation of real-time model predictive heating control for a factory building using ANN-based lumped modelling approach. In: Journal of Building Performance Simulation, v. 16, n. 2 (October 2022).
- (2022): Predictability quantification of occupant presence in high-rise residential apartment buildings. In: Energy and Buildings, v. 275 (November 2022).
- (2022): Rule reduction for control of a building cooling system using explainable AI. In: Journal of Building Performance Simulation, v. 15, n. 6 (4 August 2022).
- (2022): Model predictive lighting control for a factory building using a deep deterministic policy gradient. In: Journal of Building Performance Simulation, v. 15, n. 2 (28 January 2022).
- (2021): Hybrid model using Bayesian neural network for variable refrigerant flow system. In: Journal of Building Performance Simulation, v. 15, n. 1 (16 December 2021).
- (2021): Model selection for parameter identifiability problem in Bayesian inference of building energy model. In: Energy and Buildings, v. 245 (August 2021).
- (2019): Optimal control strategies of eight parallel heat pumps using Gaussian process emulator. In: Journal of Building Performance Simulation, v. 12, n. 5 (June 2019).
- (2021): Elimination of multidimensional outliers for a compression chiller using a support vector data description. In: Science and Technology for the Built Environment, v. 27, n. 5 (April 2021).
- (2020): Local vs. integrated control of a variable refrigerant flow system using artificial neural networks. In: Science and Technology for the Built Environment, v. 26, n. 8 (July 2020).
- (2020): Application of deep Q-networks for model-free optimal control balancing between different HVAC systems. In: Science and Technology for the Built Environment, v. 26, n. 1 (2 January 2020).
- (2019): Machine learning vs. hybrid machine learning model for optimal operation of a chiller. In: Science and Technology for the Built Environment, v. 25, n. 2 (February 2019).
- (2020): Prior selection method using likelihood confidence region and Dirichlet process Gaussian mixture model for Bayesian inference of building energy models. In: Energy and Buildings, v. 224 (October 2020).
- (2019): Parameter identifiability in Bayesian inference for building energy models. In: Energy and Buildings, v. 198 (September 2019).
- (2019): Temporal and spatial variation in the predictability of building occupancy. In: Building and Environment, v. 149 (February 2019).
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