Yeonsook Heo
Korea University · 工学
研究室紹介
Professor Yeonsook Heo's research lab specializes in building energy modeling, with a focus on enhancing the accuracy and reliability of energy performance predictions through advanced calibration techniques such as Bayesian calibration. The lab investigates the integration of monitored energy data with simulation models to quantify uncertainty and support data-driven decision-making in building retrofitting and energy efficiency projects. Key research directions include probabilistic risk analysis for energy conservation measures, optimization of building simulation workflows, and the development of scalable, automated calibration methods for large building portfolios.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
15This 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