A Meta Model Based Bayesian Approach for Building Energy Models Calibration

Buildings contribute a large proportion of energy-related emissions. In order to characterize the buildings' energy performance, building energy models have been widely used due to their flexibility and convenience. When building energy models are developed to represent the existing buildings, there always exist some unknown or unmeasurable parameters which need to be specified in the simulation models. It is important to calibrate these parameters before applying the building energy models for intended use. In addition, there are various uncertainties in the using of the building energy models. To provide more reliable and confident results for decision making, it is necessary to account for these uncertainties in the calibration procedure. In this paper, a metamodel based Bayesian approach is proposed to calibrate the building energy models. This method is efficient by using the metamodel and can also take into account various uncertainties. To further improve the computational efficiency of the proposed method, a posterior approximation method is proposed to analytically evaluate the posterior distributions in the Bayesian approach. The proposed method is applied to calibrate an EnergyPlus model which is developed to simulate an office building located in Singapore. The numerical results indicate that it is accurate and efficient to use the proposed method for building energy models calibration. (C) 2017 The Authors. Published by Elsevier Ltd.