Bayesian Calibrating Educational Building Thermal Models to Hourly Indoor Air Temperature: Methodology and Case Study

Abstract

Abstract With the increase in the frequency and duration of heatwaves and extreme temperatures, global warming becomes one of the most critical environmental issues. Heatwaves pose significant threats to human health, including related diseases and deaths, especially for vulnerable groups. Such as the one during the 2018 summer in Montreal, Canada, caused up to 53 deaths, with most lived in buildings without access to air-conditioning. Unlike building energy models that mainly focus on energy performance, building thermal models emphasizes indoor thermal performance without a mechanical system. It is required an understanding of the complex dynamic building thermal physics in which detailed building parameters need to be specified but challenging to be determined in real life. The uncertainty assessment of the parameters estimates can make the results more reliable. Therefore, in this paper, a Bayesian-based calibration procedure was presented and applied to an educational building. First, the building was modeled in EnergyPlus based on an in-site visit and related information collection. Second, a sensitivity analysis was performed to identify significant parameters affecting the errors between simulated and monitored indoor air temperatures. Then, a Meta-model was developed and used during the calibration process instead of the original EnergyPlus model to decrease the requirement of computing load and time. Subsequently, the Bayesian inference theory was employed to calibrate the model on hourly indoor air temperatures in summer. Finally, the model was validated. It is shown that the Bayesian calibration procedure not only can calibrate the model within the performance tolerance required by international building standards/codes but also predict future thermal performance with a confidence interval, which makes it more reliable.