Korea University · Engineering
Professor Yeonsook Heo's research lab specializes in building energy modeling, simulation, and risk-informed decision-making for energy efficiency. The lab focuses on advancing Bayesian calibration techniques to improve the accuracy and reliability of building energy predictions, particularly in the context of retrofitting and energy service contracts. Key research directions include probabilistic modeling, uncertainty quantification, and the development of scalable, data-driven methods for large building portfolios.
Figures are computed from collected data and may differ slightly.
This paper examines how calibration performs under different levels of uncertainty in model input data. It specifically assesses the efficacy of Bayesian calibration to enhance the reliability of EnergyPlus model predictions. A Bayesian approach can be used to update uncertain values of parameters, given measured energy-use data, and to quantify the associated uncertainty. We assess the efficacy of Bayesian calibration under a controlled virtual-reality setup, which enables rigorous validation o
Retrofitting of existing buildings is essential to reach reduction targets in energy consumption and greenhouse gas emission. In the current practice of a retrofit decision process, professionals perform energy audits, and construct dynamic simulation models to benchmark the performance of existing buildings and predict the effect of retrofit interventions. In order to enhance the reliability of simulation models, they typically calibrate simulation models based on monitored energy use data. The
AbstractThis article presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This article demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the energy service company industry. A case study illustrates the importance
This paper presents a risk analysis method based on Bayesian calibration of building energy models. The Bayesian approach enables probabilistic outputs from the energy model, which are used to quantify risks associated with investing in energy conservation measures in existing buildings. This paper demonstrates the applicability of the proposed methodology to support energy saving contracts in the context of the ESCO industry. A case study illustrates the importance of quantifying relative risks
hospital unit; nurses ' movement; statistical analysis; space planning Optimization of nurses ’ movement is an important means for improving organizational productivity in healthcare units. Studies on movement and behavior of nurses have often found that the spatial layout of nursing unit floors has a significant effect on nurses ’ mobility. However, efforts to correlate types of hospital layouts with nurses ’ movement have not met with consistent success. We show that the effect of spa
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