AI for REAL-world NETwork operation

Informacje na temat projektu

AI4REALNET

Identyfikator umowy o grant: 101119527

DOI

10.3030/101119527

Data podpisania przez KE 29 Czerwca 2023

Data rozpoczęcia 1 Października 2023

Data zakończenia 31 Marca 2027

Finansowanie w ramach

• Digital, Industry and Space

Koszt całkowity

€ 3 999 976,25

Wkład UE

€ 3 999 976,25

Koordynowany przez

INESC TEC - INSTITUTO DE ENGENHARIADE SISTEMAS E COMPUTADORES, TECNOLOGIA E CIENCIA
Portugal

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Rezultaty

Documents, reports (4)

Communication and dissemination plan

This report will establish the basis for the development of a common communication and dissemination, plan in the project.

Exploitation plan and strategy phase 1

This report will establish a first basis for the development of individual and common exploitation plan in the project, including IPR considerations and open-source strategy.

AI4REALNET framework and use cases

This report will provide a qualitative description of human real-world decision-making and requirements for AI-based decision systems and understanding how context shapes individual decision-making. Moreover, it will present the use cases description and key performance indicators.

Position paper on “AI for operation of critical energy and mobility network infrastructures”

Position paper describing the AI4REALNET view on AI for the operation of critical infrastructures, including scientific research challenges.

Data Management Plan (1)

Data management plan V1

Report that will define how research data will be handled during and after the project. This plan will define procedures on how to handle, store and share datasets according to the FAIR principles. It will describe which data will be collected, processed, or generated, methodologies and standards to follow, whether and how this data will be shared and/or made open, and how it will be curated and preserved. Furthermore, it will give consideration to the ethics issues raised by the proposed work, in particular, it will provide a description of the ethical procedures in order to allow a Ethics Check during the Project Review.

Publikacje

Conference proceedings (16)

Multi-Objective Reinforcement Learning for Power Grid Topology Control

Autorzy: Thomas Lautenbacher, Ali Rajaei, Davide Barbieri, Jan Viebahn, Jochen L. Cremer
Opublikowane w: Power Tech 2025, 2025
Wydawca: https://www.arxiv.org/abs/2502.00040
DOI: 10.48550/ARXIV.2502.00040

Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning

Autorzy: Riccardo Poiani, Curti Gabriele, Alberto Maria Metelli, Marcello Restelli
Opublikowane w: Neural Information Processing Systems (NeurIPS) 2024, 2024
Wydawca: https://arxiv.org/abs/2401.03857
DOI: 10.48550/ARXIV.2401.03857

A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures

Autorzy: Milad Leyli-abadi, Ricardo J Bessa, Jan Viebahn, Daniel Boos, Clark Borst, Alberto Castagna, Ricardo Chavarriaga, Mohamed Hassouna, Bruno Lemetayer, Giulia Leto, Antoine Marot, Maroua Meddeb, Manuel Meyer, Viola Schiaffonati, Manuel Schneider, Toni Waefle
Opublikowane w: IEEE SMC 2025, 2025
Wydawca: https://zenodo.org/records/16905470
DOI: 10.48550/ARXIV.2504.16133

On the Definition of Robustness and Resilience of AI Agents for Real-time Congestion Management

Autorzy: Timothy Tjhay, Ricardo J. Bessa, Jose Paulos
Opublikowane w: IEEE Power Tech 2025, 2024
Wydawca: https://zenodo.org/records/15237788
DOI: 10.48550/ARXIV.2504.13314

User experience evaluation of an AI-based decision-support tool for power grid congestion management

Autorzy: Jan Viebahn, Abdullah Ayedh, Jonas Lundberg, Magnus Bång, Jeroen Keijzers
Opublikowane w: AHFE International, Human Interaction and Emerging Technologies (IHIET 2025), Numer -5, 2025
Wydawca: AHFE International
DOI: 10.54941/AHFE1006694

Generation of Power Network Operating Scenarios for an AI-friendly Digital Environment

Autorzy: Jose Paulos, Pedro R. Silva, Ricardo J. Bessa, Antoine Marot, Jerome Dejaegher, Benjamin Donnot
Opublikowane w: IEEE PowerTech 2025 Conference, 2025
Wydawca: https://zenodo.org/records/15184237
DOI: 10.5281/ZENODO.15184237

Continuous Assessment Driven Requirements Elicitation For Trustworthy AI Systems

Autorzy: Wolfgang, Stefani; Heitz, Christoph; Chavarriaga, Ricardo
Opublikowane w: ECML PKDD AI-SCI 2025 workshop, 2025
Wydawca: https://zenodo.org/records/17099001
DOI: 10.5281/ZENODO.17099001

Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation

Autorzy: Milad Leyli-abadi , Antoine Marot , Jérôme Picault
Opublikowane w: ECML PKDD ML4SPS 2025 workshop, 2025
Wydawca: https://arxiv.org/abs/2509.19233
DOI: 10.48550/ARXIV.2509.19233

Power Grid Control with Graph-Based Distributed Reinforcement Learning

Autorzy: Carlo Fabrizio, Gianvito Losapio, Marco Mussi, Alberto Maria Metelli, Marcello Restelli
Opublikowane w: ECML PKDD 2025 ML4SPS workshop, 2025
Wydawca: https://arxiv.org/abs/2509.02861
DOI: 10.48550/ARXIV.2509.02861

Last-Iterate Global Convergence of Policy Gradients for Constrained Reinforcement Learning

Autorzy: Alessandro Montenegro, Marco Mussi, Matteo Papini, Alberto Maria Metelli
Opublikowane w: Neural Information Processing Systems (NeurIPS) 2024, 2024
Wydawca: https://arxiv.org/abs/2407.10775
DOI: 10.48550/ARXIV.2407.10775

Learning Topology Actions for Power Grid Control: A Graph-Based Soft-Label Imitation Learning Approach

Autorzy: Mohamed Hassouna, Clara Holzhüter, Malte Lehna, Matthijs de Jong, Jan Viebahn, Bernhard Sick, Christoph Scholz
Opublikowane w: ECML PKDD 2025, 2025
Wydawca: https://arxiv.org/abs/2503.15190
DOI: 10.48550/ARXIV.2503.15190

How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach

Autorzy: Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
Opublikowane w: Neural Information Processing Systems (NeurIPS) 2024, 2024
Wydawca: https://arxiv.org/abs/2406.03812
DOI: 10.48550/ARXIV.2406.03812

Pioneering roadmap for ML-driven algorithmic advancements in electrical networks

Autorzy: Jochen L. Cremer, Adrian Kelly, Ricardo J. Bessa, Milos Subasic, Panagiotis N. Papadopoulos, Samuel Young, Amar Sagar, Antoine Marot
Opublikowane w: IEEE ISGT Europe 2024, 2024
Wydawca: https://arxiv.org/abs/2405.17184
DOI: 10.48550/ARXIV.2405.17184

Centrally Coordinated Multi-Agent Reinforcement Learning for Power Grid Topology Control

Autorzy: Barbera de Mol, Davide Barbieri, Jan Viebahn, Davide Grossi
Opublikowane w: ACM e-Energy 2025, 2025
Wydawca: https://arxiv.org/abs/2502.08681

Applying Job Design Criteria for Effective Human-AI Collaboration

Autorzy: Samira Hamouche, Nerissa Dettling, Toni Waefler
Opublikowane w: AHFE International, Human Interaction and Emerging Technologies (IHIET 2025), Numer -5, 2025
Wydawca: AHFE International
DOI: 10.54941/AHFE1006695

The Supportive AI Framework: From Recommending to Supporting

Autorzy: Toni Waefler, Samira Hamouche, Andrina Eisenegger
Opublikowane w: Lecture Notes in Computer Science, Augmented Cognition, 2025
Wydawca: Springer Nature Switzerland
DOI: 10.1007/978-3-031-93724-8_22

Peer reviewed articles (1)

Human-AI interaction in safety-critical network infrastructures

Autorzy: Marco Mussi, Alberto Maria Metelli, Marcello Restelli, Gianvito Losapio, Ricardo J. Bessa, Daniel Boos, Clark Borst, Giulia Leto, Alberto Castagna, Ricardo Chavarriaga, Duarte Dias, Adrian Egli, Andrina Eisenegger, Yassine El Manyari, Anton Fuxjäger, Joaquim Geraldes, Samira Hamouche, Mohamed Hassouna, Bruno Lemetayer, Milad Leyli-Abadi, Roman Liessner, Jonas Lundberg, Antoine Marot, Maroua Meddeb, Viola Schiaffonati, Manuel Schneider, Thilo Stadelmann, Julia Usher, Herke Van Hoof, Jan Viebahn, Toni Waefler, Giacomo Zanotti
Opublikowane w: iScience, Numer 28, 2025, ISSN 2589-0042
Wydawca: Elsevier BV
DOI: 10.1016/J.ISCI.2025.113400