The Modelica language is one of the most important languages for representing a large class of systems, ranging from vehicles to climate control systems in buildings. Component libraries, containing components like valves, motors, pumps, etc., have been built to facilitate the construction of complex systems, but at present model construction and parameter estimation are entirely manual. We have developed software for (i) automating the process of constructing models that optimise a range of metrics (e.g., model-simulation accuracy or diagnostics accuracy), and (ii) dynamically updating the model parameters due to dynamic changes in the observed data and/or health status of the modelled system.
We assume that in these component libraries a component may be modeled at multiple levels of fidelity, e.g., as a non-linear system (high-fidelity model), linear system, or a qualitative system (low-fidelity model). Choosing the right component model for system simulation is a difficult task and requires a search in the space of all possible component type combinations. In this paper we propose a method that automates this task and computes a system
model that optimizes a set of metrics in a set of simulation scenarios. We describe initial experimental results showing the trade-offs of accuracy and inference time.
This software has the potential to revolutionise how industry uses Modelica, i.e., changing the use from an expensive manual process to a fully automated process that is adaptive to changing external conditions.
model composition; model fidelity and accuracy; simulation accuracy; model complexity
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