Supplementary data: "Physics-informed machine learning for power grid frequency modelling"

This repository contains result files for the paper "Physics-informed machine learning for power grid frequency modelling" (Preprint). The code for producing the processed data and the results is available at github. <strong>Results</strong> The result folder comprises the results of hyper-parameter optimisation, scaling variation and interpretation via SHAP. In particular, it contains these sub-folders and files: <em>tuning </em>: Results of hyper-parameter tuning. <em>best_model </em>: Weights of the trained model with best hyper-parameters. <em>best_model_&lt;scaling-variation&gt; </em>: Weights of the trained models with best hyper-parameters but with a variation of the parameter scaling. <em>fixed_model_hps.pkl </em>: Hyper-parameters that are not optimised. <em>shap_values_&lt;parameter&gt;_long.h5</em> : SHAP values for the prediction of the system parameters.