Real-Time Digital Twins for Building Energy Optimization Through Blind Control: Functional Mock-Up Units, Docker Container-Based Simulation, and Surrogate Models

The transition toward energy-efficient and smart buildings requires Digital Twins (DTs) that can couple real-time data with physics-based Building Energy Models (BEMs) for predictive and adaptive operation. Yet, despite rapid digitalisation, there remains a lack of practical guidance and real-world implementations demonstrating how calibrated BEMs can be effectively integrated into Building Management Systems (BMSs). This study addresses that gap by presenting a complete and reproducible end-to-end framework for embedding physics-based BEMs into operational DTs using two setups: (i) encapsulation as Functional Mock-up Units (FMUs) and (ii) containerisation via Docker. Both approaches were deployed and tested in a real educational building in Cáceres (Spain), equipped with a LoRaWAN-based sensing and actuation infrastructure. A systematic comparison highlights their respective trade-offs: FMUs offer faster execution but limited weather inputs and higher implementation effort, whereas Docker-based workflows provide full portability, scalability, and native interoperability with Internet of Things (IoT) and BMS architectures. To enable real-time operation, a surrogate modelling framework was embedded within the Docker architecture to replicate the optimisation logic of the calibrated BEM and generate predictive blind control schedules in milliseconds—bypassing simulation overhead and enabling continuous actuation. The combined Docker + surrogate setup achieved 10–15% heating energy savings during winter operation without any HVAC retrofit. Beyond the case study, this work provides a step-by-step, in-depth guideline for practitioners to integrate calibrated BEMs into real-time control loops using existing toolchains. The proposed approach demonstrates how hybrid physics- and data-driven DTs can transform building management into a scalable, energy-efficient, and operationally deployable reality.