Design, Implementation and Calibration of a Building Digital Twin for Energy performance Monitoring and Control Strategy testing

The increasing need to reduce energy consumption has called for novel energy management systems in the building sector, due to its large share of global energy end use. Digital models of the controlled building are required for testing advanced control strategies and optimization of the control actions. In this context, the concept of the digital twin emerges as a promising solution to integrate simulation and monitoring, enabling the prediction, control, and optimization of energy use over time. This thesis proposes a methodological framework for the development and periodic re-calibration of a physics-based digital twin for buildings. The goal is to create a thermal model of the building capable of automatically constructing its own multi-zone structure from input data and maintaining a good level of accuracy over time through recurrent parameter updates. The model, implemented in the Modelica modelling language, adopts a parametric architecture that, starting from information on the building geometry and its material thermophysical properties, automatically generates a model of the building that includes its thermal zones and their interconnections. The periodic re-calibration process is based on the optimization of a reduced set of physically meaningful parameters. This mechanism allows the parameters to be updated over successive time windows, keeping the model aligned with real operating conditions and ensuring consistent performance over time. The framework was also validated under limited data availability, with partial information on internal gains and actual space usage. Despite these constraints, the calibration process proved its ability to compensate for uncertainties and adapt to varying conditions, confirming the robustness of the model and its applicability in real monitoring contexts. The work demonstrates that a generative, periodically re-calibrated physical model can maintain prediction accuracy over time, realistically reproducing the dynamics of the modelled building. This approach provides the foundation for future developments integrating Heating, Ventilation and Air Conditioning systems, facilitating the implementation of advanced control strategies.