In situ model fusion for building digital twinning

Abstract

Building digital twins are crucial in holistic, synthetic, and supervisory digital environments for intelligent and energy-efficient building operations. As buildings are designed, constructed, and operated for long-term periods with uncertainties, it is essential to model, verify, extend, and calibrate the digital twin models in situ over the building life cycle, unlike digital twin modeling in the manufacturing industries. Therefore, this study proposes in situ model fusion techniques for building digital twinning, which include (1) model coupling and (2) model assembly. The first model coupling technique enables nonintrusive in situ verification and calibration for prediction models without the model observations (Y). The second model assembly enables in situ modeling or a more accurate model construction by connecting the verified models through the model coupling technique to the input layer of the target model. In the case studies conducted in a central heating system, the prediction model for the secondary-side return water temperature was modeled and calibrated without relying on the model observation data (Y), thanks to the model coupling technique. The nonintrusive model showed an accuracy with a root mean square error (RMSE) of 0.53 °C, which was comparable to the intrusive gray-box models (around 0.5 °C RMSE) calibrated using intrusive datasets (Y). Additionally, the prediction model could improve the benchmark model's performance from 0.44 °C to 0.26 °C (RMSE) using the model assembly technique. The in situ model fusion would enable and enhance nonintrusive modeling approaches, providing a reliable and extensive model environment for digital twins in the building sector.