Juliacon 2024

ModelingToolkit.jl provides a scalable and performant way of building large scale models for industrial systems due to its advanced symbolic manipulation techniques and its acasual nature.
Once these models are constructed, fine-tuning becomes essential to align their behavior with real-world industrial systems. This calibration process entails incorporating design constraints and integrating experimentally measured real-world data into the models.

Calibrating models to data can be difficult due to the behavioral complexity of the models and the challenges that are brought in by the data, such as noise, partial observability or sparsity of the measurements.

The JuliaSim Model Library includes high performance, composable, domain specific tools for industrial systems. JuliaSim-HVAC is one such component, which can be used to describe refrigeration, air conditioning systems etc. Additionally, JuliaSim-Batteries can be used to design and simulate electrochemical models of large battery packs.

In this talk we will explore techniques such as Single Shooting, Multiple Shooting, Collocation and Prediction Error Method exemplified on HVAC and Battery systems and show how they can help avoid local minima during calibration procedures. In order to demonstrate the robustness of our results, we will compare our predictions against validation data sets. Moreover we can investigate the effects of unidentifiability of parameters using Bayesian priors on the parameters.