AI for REAL-world NETwork operation

Informazioni relative al progetto

AI4REALNET

ID dell’accordo di sovvenzione: 101119527

DOI

10.3030/101119527

Data della firma CE 29 Giugno 2023

Data di avvio 1 Ottobre 2023

Data di completamento 31 Marzo 2027

Finanziato da

• Digital, Industry and Space

Costo totale

€ 3 999 976,25

Contributo UE

€ 3 999 976,25

Coordinato da

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

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

Pubblicazioni

Conference proceedings (16)

Multi-Objective Reinforcement Learning for Power Grid Topology Control

Autori: Thomas Lautenbacher, Ali Rajaei, Davide Barbieri, Jan Viebahn, Jochen L. Cremer
Pubblicato in: Power Tech 2025, 2025
Editore: https://www.arxiv.org/abs/2502.00040
DOI: 10.48550/ARXIV.2502.00040

Sub-optimal Experts mitigate Ambiguity in Inverse Reinforcement Learning

Autori: Riccardo Poiani, Curti Gabriele, Alberto Maria Metelli, Marcello Restelli
Pubblicato in: Neural Information Processing Systems (NeurIPS) 2024, 2024
Editore: https://arxiv.org/abs/2401.03857
DOI: 10.48550/ARXIV.2401.03857

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

Autori: 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
Pubblicato in: IEEE SMC 2025, 2025
Editore: 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

Autori: Timothy Tjhay, Ricardo J. Bessa, Jose Paulos
Pubblicato in: IEEE Power Tech 2025, 2024
Editore: 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

Autori: Jan Viebahn, Abdullah Ayedh, Jonas Lundberg, Magnus Bång, Jeroen Keijzers
Pubblicato in: AHFE International, Human Interaction and Emerging Technologies (IHIET 2025), Numero -5, 2025
Editore: AHFE International
DOI: 10.54941/AHFE1006694

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

Autori: Jose Paulos, Pedro R. Silva, Ricardo J. Bessa, Antoine Marot, Jerome Dejaegher, Benjamin Donnot
Pubblicato in: IEEE PowerTech 2025 Conference, 2025
Editore: https://zenodo.org/records/15184237
DOI: 10.5281/ZENODO.15184237

Continuous Assessment Driven Requirements Elicitation For Trustworthy AI Systems

Autori: Wolfgang, Stefani; Heitz, Christoph; Chavarriaga, Ricardo
Pubblicato in: ECML PKDD AI-SCI 2025 workshop, 2025
Editore: https://zenodo.org/records/17099001
DOI: 10.5281/ZENODO.17099001

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

Autori: Milad Leyli-abadi , Antoine Marot , Jérôme Picault
Pubblicato in: ECML PKDD ML4SPS 2025 workshop, 2025
Editore: https://arxiv.org/abs/2509.19233
DOI: 10.48550/ARXIV.2509.19233

Power Grid Control with Graph-Based Distributed Reinforcement Learning

Autori: Carlo Fabrizio, Gianvito Losapio, Marco Mussi, Alberto Maria Metelli, Marcello Restelli
Pubblicato in: ECML PKDD 2025 ML4SPS workshop, 2025
Editore: https://arxiv.org/abs/2509.02861
DOI: 10.48550/ARXIV.2509.02861

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

Autori: Alessandro Montenegro, Marco Mussi, Matteo Papini, Alberto Maria Metelli
Pubblicato in: Neural Information Processing Systems (NeurIPS) 2024, 2024
Editore: 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

Autori: Mohamed Hassouna, Clara Holzhüter, Malte Lehna, Matthijs de Jong, Jan Viebahn, Bernhard Sick, Christoph Scholz
Pubblicato in: ECML PKDD 2025, 2025
Editore: 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

Autori: Filippo Lazzati, Mirco Mutti, Alberto Maria Metelli
Pubblicato in: Neural Information Processing Systems (NeurIPS) 2024, 2024
Editore: https://arxiv.org/abs/2406.03812
DOI: 10.48550/ARXIV.2406.03812

Pioneering roadmap for ML-driven algorithmic advancements in electrical networks

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

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

Autori: Barbera de Mol, Davide Barbieri, Jan Viebahn, Davide Grossi
Pubblicato in: ACM e-Energy 2025, 2025
Editore: https://arxiv.org/abs/2502.08681

Applying Job Design Criteria for Effective Human-AI Collaboration

Autori: Samira Hamouche, Nerissa Dettling, Toni Waefler
Pubblicato in: AHFE International, Human Interaction and Emerging Technologies (IHIET 2025), Numero -5, 2025
Editore: AHFE International
DOI: 10.54941/AHFE1006695

The Supportive AI Framework: From Recommending to Supporting

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

Peer reviewed articles (1)

Human-AI interaction in safety-critical network infrastructures

Autori: 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
Pubblicato in: iScience, Numero 28, 2025, ISSN 2589-0042
Editore: Elsevier BV
DOI: 10.1016/J.ISCI.2025.113400