Paper Contents
Abstract
The Architecture, Engineering, and Construction (AEC) sector is undergoing rapid digital transformation, with the Digital Twin (DT) emerging as a key paradigm for bridging design and operation. Unlike conventional BIM or stand-alone simulations, a DT provides a continuously updated virtual replica of a building, integrating IoT-based sensing, semantic interoperability, and AI-driven predictive models. This research develops and tests a comprehensive DT framework that combines BIM (IFC standard), Brick Schema and Project Haystack ontologies, IoT data streams, and hybrid predictive analytics (EnergyPlus + LSTM, XGBoost). A virtual medium-sized office building equipped with HVAC and lighting systems is used as a case study.Results show that the DT improves energy prediction accuracy to 5% MAPE, increases ASHRAE 55 thermal comfort compliance by 13%, and reduces unplanned HVAC failures by 35%. Annual energy use is lowered by 7.2%, equivalent to 18 tonnes of avoided CO emissions. Beyond these technical outcomes, the study highlights the importance of integrating costbenefit considerations, occupant behaviour variability, and cybersecurity safeguards (aligned with NIST and IEC 62443) to ensure real-world applicability. The findings confirm the transformative potential of DTs for sustainable, resilient, and economically viable building management, while also providing a replicable framework for future large-scale adoption.
Copyright
Copyright © 2025 Osamah A. Al-Tameemi. This is an open access article distributed under the Creative Commons Attribution License.
