Real name:
Congratulations! You have been automatically subscribed to IEEE DataPort and can access all datasets on IEEE DataPort!
First Name:
Juan
Last Name:
Cuenca Silva
Datasets & Competitions
This repository contains the datasets produced using different data generation strategies to train data driven models (e.g., decision trees, gradient tree boosting, and deep neural networks), and to evaluate their performances. The data generation strategies are described, and the results are presented in the conference paper: "Training Data Generation Strategies for Data-driven Security Assessment of Low Voltage Smart Grids" J. Cuenca, E. Aldea, E. Le Guern-Dall'o, R. Féraud, G. Camilleri, and A. Blavette. IEEE ISGT EU 2024, Dubrovnik, Croatia, Oct 2024.
- Categories:
53 Views
This repository contains datasets and code with a novel numerical approach aimed at finding a distribution network expansion plan (DNEP) that prevents future congestion and voltage issues. This approach is tested using the modified IEEE 33-bus network. Electricity demand and PV production data for a leap year with a 1-minute resolution was generated using the CREST model from the Loughborough University and is provided as a dataset of future high-load and high-production scenario.
- Categories:
241 Views