A Framework For The Continuous Calibration Of Building Energy Models With Uncertainty

Abstract

With the emergence of the Internet of Things (IoT), there is an opportunity to create a digital twin of a building that continuously learns and updates itself using real-time observations. Model calibration is an essential aspect of the overall process to ensure its reliability. However, the calibration of building energy models (BEM) is typically carried out only once and can quickly become outdated. Continuous Bayesian calibration reduces the effort to maintain an energy model while accounting for its uncertainty. Invariably, the model would be up-to-date for use in applications such as retrofit analysis, fault detection, and model predictive control. The present paper aims to present the concept and implementation of a framework for the continuous calibration of BEM with uncertainty. The proposed framework includes instance selection as a pre-processing step to keep the calibration process computationally tractable.