Optimized graph-based modelling of multi-zone building energy systems: a sensitivity–identifiability-driven calibration approach

Buildings consume a large share of global energy, making accurate building energy models (BEMs) critical for improving efficiency. Existing calibration methods often face high computational demands, overlook parameter identifiability, and lack independent validation, limiting their practical reliability. This study proposes a comprehensive framework for the efficient calibration of multi-zone BEMs, integrating graph-based modelling (GBM), global sensitivity analysis (GSA), parameter identifiability assessment, and optimization. GBM captures complex thermal interactions across zones with interpretability and accuracy. Sobol'-based GSA identifies the most influential parameters, improving calibration efficiency, while parameter identifiability assessment ensures uniqueness of parameter estimates before optimization. Calibration is conducted using the golden search optimization algorithm, balancing exploration and exploitation. Validation through both statistical metrics and independent validation data not used during model calibration demonstrates the robustness and generalizability of the proposed approach. Results demonstrate that explicitly addressing sensitivity, identifiability yields more accurate and reliable BEMs, supporting energy-efficient operation and control strategies.Highlights Developing the graph-based model of multi-zone building dynamics with a reduced-complexity structure.Optimized calibration integrating GSA, identifiability, and golden search method. Validation robustness and generalizability under static metrics and independent data not used during model calibration.Providing a foundation method for energy-efficient operation and control strategies.