Calibration of building energy models is widely used in building energy audits and retrofit practices. Li et al. (2015) proposed a lightweight approach for the Bayesian calibration of dynamic building energy models, which alleviate the computation issues by the use of a linear regression emulator. As a further extension, this paper has the following contributions. First, it provides a brief literature review that motivates the original work. Second, it explained the detailed calibration methodology and its mathematical formulas, and in addition the prediction using meta-models. Third, it introduced new performance metrics for evaluating predictive distributions under uncertainty. Fourth, it used the standard Bayesian calibration method as the benchmark, assessed the capability of regression emulators of different complexity, and showed the comparison result in a case study. Compared to the standard Gaussian process emulator, the linear regression emulator including main and interaction effects is much simpler both in interpretation and implementation, calibrations are performed much more quickly, and the calibration performances are similar. This indicates a capability to perform fast risk-conscious calibration for most current retrofit practice where only monthly consumption and demand data from utility bills are available. (C) 2016 Elsevier B.V. All rights reserved.