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

<p>This repository contains processed data and result files for the paper <a href="https://arxiv.org/abs/2204.10727">Revealing interactions between HVDC cross-area flows and frequency stability with explainable AI</a>.&nbsp;The code for producing the processed data and the results is&nbsp;<a href="https://github.com/sebastianptz/XAI-for-HVDC">available at github</a>.</p> <p><strong>Data</strong></p> <p>The data folder contains the feature and target data used to train the ML model:</p> <ul> <li>stability_input: A Folder containing training and test sets for the stability model for each area.</li> <li>flow_input: A Folder containing training and test sets for the flow model for each&nbsp;link.</li> <li><em>raw_input_data.h5</em><strong>&nbsp;</strong>:<strong>&nbsp;</strong>The aggregated external features without additional engineered features.</li> <li><em>input_forecast.h5&nbsp;</em>and<em>&nbsp;input_actual.h5:&nbsp;</em>The day-ahead available (forecast) and ex-post available (actual) data of external features including the engineered features.</li> <li><em>indicators.h5&nbsp;</em>:&nbsp; The grid frequency stability indicators.</li> <li><em>documentation_of_data_download</em>: Information files concerning the ENTSO-E raw data and its aggregation.</li> <li>HVDClinks: A Folder containing preprocessed time series for scheduled and unscheduled HVDC flows</li> </ul> <p><strong>Data sources</strong></p> <p>Most of the&nbsp;data is derived from ENTSO-E Transparency Platform data [1]. The grid stability indicators&nbsp;(indicators<em>.h5</em>) are&nbsp;based on publicly available data from the German Transmission System Operators (TSOs) [2].</p> <p><strong>Results</strong></p> <p>The stability_results and the flow_results folder contain&nbsp;the results of hyperparameter optimization, model prediction and interpretation via SHAP for the respective models.</p> <ul> <li><em>cv_results_gtb_full.csv</em>&nbsp;: Performance results for each combination in the hyperparameter optimization.</li> <li><em>cv_best_params_gtb_full.csv</em>&nbsp;: Hyperparameters used in the final (optimized) model.</li> <li><em>shap_values_gtb_full.npy</em>&nbsp;: First-order SHAP values calculated on different data sets: The train set, the randomized test set and the continuous test set.</li> <li><em>y_pred.h5/y_pred_links.h5</em>&nbsp;: Predictions of daily profile predictor and Machine Learning models.</li> </ul> <p><strong>Disclaimer</strong></p> <p>The data might be subject to copyright or related rights. Please consult the primary data owner.</p> <p>&nbsp;</p> <p>&nbsp;</p> <div>&nbsp;</div>