Szczegóły publikacji
Opis bibliograficzny
Machine learning-based nodal loadability estimation for power system / Krzysztof Chmielowiec, David Marino, Paweł Dawidowski // W: IEEE EUROCON 2025 [Dokument elektroniczny] : 21st international conference on Smart technologies : June 4-6, 2025, Gdynia, Poland : proceedings / eds. Ireneusz Czarnowski, Marek Jasiński. — Wersja do Windows. — Dane tekstowe. — [Piscataway] : Institute of Electrical and Electronics Engineers, cop. 2025. — ( International Conference on Computer as a Tool ; ISSN 2837-7990 ). — Print on Demand (PoD) ISBN: 979-8-3315-0879-1. — e-ISBN: 979-8-3315-0878-4. — S. [1–6]. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. [6], Abstr. — Publikacja dostępna online od: 2025-07-15. — K. Chmielowiec - afiliacja: Hitachi Energy Research, Krakow
Autorzy (3)
- Chmielowiec Krzysztof
- Marino David
- Dawidowski Paweł
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164975 |
|---|---|
| Data dodania do BaDAP | 2025-12-15 |
| Tekst źródłowy | URL |
| DOI | 10.1109/EUROCON64445.2025.11073199 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Czasopismo/seria | International Conference on Computer as a Tool |
Abstract
This paper presents the results of experiments that investigated using a machine learning approach for nodal loadability estimation in power systems. It defines the concept of nodal loadability and the methodology of its estimation using a graph neural network, including dataset generation, model architecture, and training pipeline. The results obtained indicate a significant improvement in computation speed, as well as an average estimation error of up to 48% for 25,000-bus networks.
