Archetype Modeling Uncertainty and Bayesian Calibration for Campus-scale Analysis of Seoul National University Buildings Energy Use

Abstract

Purpose: Considering the gap in research on uncertainties in urban building energy modeling, with specific regard to archetype building development, this study seeks to first throw light on archetype modeling’ sources of uncertainty, and second, solve these issues with the Bayesian calibration method. Method: By using a case area of Seoul National University, this paper models five 3D archetype buildings and estimates their monthly energy use intensities on EnergyPlus. The simulation results are modified with the Bayesian calibration technique, compared against existing, observational data before and after the Bayesian calibration, and further validated by computing the coefficient of the variation of the root mean square error. Result: Findings show that the Bayesian calibration may be considered to address uncertainty as it improves the simulation results; however, it is not a one-size-fits-all solution to uncertainties, caused by the lack of some parameters, not added to the model, which are crucial to the archetype characterization. Overall, this paper contributes to the research on urban building energy modeling-related uncertainties, hoping for urban planners to consider sources of uncertainty during the archetype characterization stage and make the adequate modeling decisions in the name of sustainable urban growth.