Zero-Shot Parameter Estimation of Modelica Models using Patch Transformer Networks

Abstract

This paper introduces a transformer-based generativenetwork for rapid parameter estimation of Modelica buildingmodels using simulation data from a Functional Mock-up Unit(FMU). Utilizing the \texttt{MixedAirCO2} model from theModelica Buildings library, we simulate a single-zonemixed-air volume with detailed thermal and \cotwo dynamics.By varying eight physical parameters and randomizingoccupancy profiles across 100 simulated systems, wegenerate a comprehensive dataset. The transformer encoder,informed by room temperature and \cotwo concentrationoutputs, predicts the underlying physical parameters withhigh accuracy and without re-tuning (hence, ``zero-shot'').This approach eliminates the need for iterativeoptimization or can be used to warm-start suchoptimization-based approaches, enabling real-time control,monitoring, and fault detection in FMU-based workflows.