Stepwise deterministic and stochastic calibration of an energy simulation model for an existing building

Building simulation tools have been widely used for performance assessment. However, many studies [1] have reported that a performance gap exists between the reality and simulation output, mainly caused by unknown simulation inputs. Therefore, model calibration needs to be introduced. Calibration attempts can fail for the following reasons: coarse initial simulation model, long sampling time, uncertainty in the model, and sensor errors. The aim of this paper is to address the abovementioned issues. For this study, an existing office building was selected and two calibration approaches were presented: deterministic vs. stochastic. For stochastic calibration, a Gaussian Process Emulator (GPE) was introduced as a surrogate of the EnergyPlus model. The stochastically calibrated model performs better than the deterministically calibrated model. It is concluded in the paper that (1) the calibration quality is influenced by the degree of the details of the initial model, (2) the accumulated measured data under a sampling time of up to one day (e.g. gas energy consumption) might be unsuitable for calibration work due to the lack of 'time-series trend', and (3) the calibration quality is also influenced by sensor errors and further calibration needs to take these into account.