Calibration of Dynamic Building Energy Models with Multiple Responses Using Bayesian Inference and Linear Regression Models

Abstract

This paper proposes a lightweight Bayesian calibration of dynamic models that accounts for model parameter uncertainties. A regression model was built to represent the dynamic model using parameter screening and multiple linear regression. Given this regression model and prior probability distributions of its input parameters, a Bayesian calibration method is developed to provide their posterior distributions. A case study is presented and the result shows considerable alignment between model prediction and measurement after calibration. This indicates its capability to perform fast risk-conscious calibration for most current retrofit practice where only monthly consumption and demand data are available.