Supplementary data: "Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI"

This repository contains processed data and result files for the paper Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI. The code for producing the processed data and the results is available at github. <strong>Data</strong> The data folder contains the feature and target data used to train the ML model: stability_input: A Folder containing training and test sets for the stability model for each area. flow_input: A Folder containing training and test sets for the flow model for each link. <em>raw_input_data.h5</em><strong> </strong>:<strong> </strong>The aggregated external features without additional engineered features. <em>input_forecast.h5 </em>and<em> input_actual.h5: </em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features. <em>indicators.h5 </em>: The grid frequency stability indicators. <em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation. HVDClinks: A Folder containing preprocessed time series for scheduled and unscheduled HVDC flows <strong>Data sources</strong> Most of the data is derived from ENTSO-E Transparency Platform data [1]. The grid stability indicators (indicators<em>.h5</em>) are based on publicly available data from the German Transmission System Operators (TSOs) [2]. <strong>Results</strong> The stability_results and the flow_results folder contain the results of hyperparameter optimization, model prediction and interpretation via SHAP for the respective models. <em>cv_results_gtb_full.csv</em> : Performance results for each combination in the hyperparameter optimization. <em>cv_best_params_gtb_full.csv</em> : Hyperparameters used in the final (optimized) model. <em>shap_values_gtb_full.npy</em> : First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set. <em>y_pred.h5/y_pred_links.h5</em> : Predictions of daily profile predictor and Machine Learning models. <strong>Disclaimer</strong> The data might be subject to copyright or related rights. Please consult the primary data owner.