An Optimized DRL-GAN Approach for Robust Anomaly Detection in Multi-Scale Energy Systems: Insights from PSML and LEAD1. 0

Anita Ershadi Oskouei; Maral Keramat Dashliboroun; Pardis Sadatian Moghaddam; Nuria Serrano; F. Hernando-Galego; Diego Martín; José Vicente Álvarez-Bravo

doi:10.20944/preprints202512.0508.v1

Publication record · 2025

An Optimized DRL-GAN Approach for Robust Anomaly Detection in Multi-Scale Energy Systems: Insights from PSML and LEAD1. 0

Authors: Anita Ershadi Oskouei · Maral Keramat Dashliboroun · Pardis Sadatian Moghaddam · Nuria Serrano · F. Hernando-Galego · Diego Martín · José Vicente Álvarez-Bravo
Year: 2025
Publisher: MDPI
Journal or publication: Energies 19 (1), 198, 2025
Google Scholar citations: 1

Research summary

An optimized DRL-GAN combines adaptive decisions with reconstruction-based anomaly detection for electricity, hydrogen and renewable-energy systems. Tests on PSML and LEAD1.0 examine accuracy, convergence, runtime and robustness across heterogeneous operating conditions.

Original description prepared for this website; consult the publication for its authoritative abstract.

DOI 10.20944/preprints202512.0508.v1 ↗ · Open-access version ↗ · View Google Scholar record ↗