Continuous-time Bayesian calibration of energy models using BIM and energy data

Abstract

The present study proposes a framework for the continuous Bayesian calibration of whole building energy simulation (BES) models utilizing data from building information models (BIM) and building energy management systems (BEMS). The ability to import data from BIM and BEMS provides the potential to significantly reduce the time and effort needed for the continuous calibration of BES models. First, five gbXML geometric test cases were used to check the BIM to BES model translation. Translation of the test cases indicates good geometric agreement between the native BIM and the gbXML-based BES model. An actual building calibration case study (with BIM and three years of monthly electrical energy consumption data) was then used to evaluate the proposed continuous calibration method. The results suggest that compared to a non-continuous approach, the continuous Bayesian calibration method showed reduced prediction uncertainty and improved prediction accuracy on a test dataset. The paper also presents information and comparison of the coefficient of variance of the root mean square error (CVRMSE) and the normalized mean biased error (NMBE), recommending looking at their distributions when working with probabilistic BES predictions.