Abstract

Buildings account for nearly 40% of total global energy consumption. It is predicted that by 2050 the combined energy consumptions of the residential and commercial sectors will have increased to 22% of the world's total delivered energy. Moreover, requirements for indoor health, safety, thermal comfort, and air quality have become more demanding due to more intensive and frequent extreme climate events, such as heatwaves and cold waves. Such issues have become challenging for the building energy and environment field, especially during the COVID-19 pandemic.
Computer simulations play a crucial role in achieving a safe, healthy, comfortable, and sustainable indoor environment. As an integral step in the development of the building models, model calibration can significantly affect simulation results, model accuracy, and model-based decision-making. Conventional calibration methods, however, are often deterministic. As a result, the uncertainties that have been investigated for a building computer model, and those from the inputs have not been given adequate attention and are thus worth studying in more depth.
Bayesian Inference is one of the most effective approaches to calibrating computer models with uncertainties. Several studies have explored its application in building energy modeling, but a comprehensive application in the general field of building energy and environment has not been adequate. This thesis started with a comprehensive literature review of Bayesian Inference calibration focusing on building energy modeling. Then, a systematic Bayesian calibration workflow and a new platform were developed. As well as a general study of its application for the predictions of building energy performance, the thesis investigated how to use the platform to calibrate thermal models of buildings and indoor air quality models. To solve the issue of the computing cost of Bayesian Inference, another calibration and prediction method, Ensemble Kalman Filter (EnKF), was proposed and applied to the estimation of ventilation performance and predictions of free cooling load. The conclusion includes a summary of the contributions of this thesis and suggestions for future work.