Large-scale field demonstration of an interoperable and ontology-based energy modeling framework for building digital twins

Digital twins have emerged as a promising concept for improving building energy efficiency, but their implementation faces challenges in interoperability and adaptability. This paper presents a large-scale field demonstration of an interoperable energy modeling framework for building digital twins, using ontology-based semantic models as data sources for automated model generation and calibration of data-driven component models. The study focuses on a single floor of a hospital building, comprising 12 conditioned zones and data from 45 measuring devices. Across the 45 sensors, the model achieved average mean absolute errors of 0.40 ∘ C for temperature, 32 ppm for CO 2 concentration, 0.06 for valve position, and 0.04 for damper position predictions. These results demonstrate the framework’s ability to generate and calibrate accurate and flexible building energy models with reduced effort. The paper also showcases the framework’s practical application in exploring system modifications to improve indoor comfort, highlighting its potential for scenario analysis and decision support. The proposed approach significantly streamlines the process of creating and maintaining accurate, up-to-date energy models, offering a robust foundation for digital twin applications in the built environment. • Novel ontology-driven framework automates building energy model generation and reduces manual modeling effort. • Integration between semantic models and energy models enables digital twin adaptability. • Accuracy and scalability are demonstrated through a hospital case study, achieving mean errors of 0.4 ∘ C for temperature, 32 ppm for CO 2 , 6% for valve positions, and 4% for damper positions across 45 sensors. • Automated model generation reduces the need for energy modeling expertise, as demonstrated through rapid scenario testing of building modifications.