10 citations · 10 across the 3 of their papers we have counts for
3 papers
Hierarchical GNNs for power flow: letting physics shape the hierarchy
Carmine Delle Femine, Leire Garin Atxaga, Asier Diaz-Iglesias +5
Hierarchical latent communication improves the generalization of a power-flow model, shared across three grids, to new operating scenarios. The module exchanges information through…
Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
Julen Cestero, Carmine Delle Femine, Kenji S. Muro +2
Optimizing the energy management within a smart grids scenario presents significant challenges, primarily due to the complexity of real-world systems and the intricate interactions…
Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Julen Cestero, Carmine Delle Femine, Kenji S. Muro +2
Physics-Informed Neural Networks (PINNs) present a transformative approach for smart grid modeling by integrating physical laws directly into learning frameworks, addressing critic…