Bayesian calibration of building energy models with large datasets

Application of the proposed method is demonstrated using two cases studies: (1) a TRNSYS model of a water-cooled chiller in a mixed-use building in Singapore, and (2) an EnergyPlus model of the cooling system of an office building in Pennsylvania, U.S.A. In both case studies, convergence was achieved for all parameters of the posterior distribution, with Gelman-Rubin statistics Rˆ within 1 ± 0.1. The coefficient of variation of the root mean squared error (CVRMSE) and normalized mean biased error (NMBE) were also within the thresholds set by ASHRAE Guideline 14 [1].