3. Simulate your model

Model validation requires simulated data along with the dataset (i.e. your target environment logs) and therefore you have to simulate your model.

How? Well, you shall feed it with the input signals contained in the Dataset object that you prepared so far and that contains the target environment logs.

To extract the input signals from your Dataset object you can use the method dataset_values() and then you can use them to feed your model as long as you are working in a Python environment.

Otherwise, you can export your Dataset object in the format you want and import it into your modeling tool. To facilitate with this task, Dymoval allows you to dump Dataset objects into Signal objects through the method dump_to_signals() but then, you will have to manually export in an appropriate format depending on your modeling tool.

Note

Given the popularity of Matlab, Dymoval has a builtin function that exports Signals directly in .mat format.

Once you have simulated your model you should import the simulated data back into Python and then you are now ready to validate your model, and guess what? Dymoval is here for that!

Go to the next Section to discover more.

Note

Exporting/importing signals from/to Python to/from your modeling tool may be fairly annoying. For this reason, we recommend to compile your model into an FMU and use the packages like pyfmu or fmpy to simulate your model directly from a Python environment, so you have everything in one place.

Independently of your modeling tool (Simulink, Dymola, GT-Power, etc), you most likely have an option for compiling models into FMU:s. Check the documentation of your modeling tool.