Description du projet
Les étapes vers un apprentissage automatique digne de confiance
L’apprentissage automatique (AA) est en passe de contribuer à la décarbonisation profonde du secteur de l’énergie. Fort de sa capacité à apprendre dans des environnements complexes et à fournir des solutions, l’AT est à même de transformer radicalement les systèmes électriques. Toutefois, les nouvelles normes de vérification de l’UE exigeront que toutes les applications d’AT et d’apprentissage par renforcement (AR) utilisées dans les applications critiques pour la sécurité soient dignes de confiance. Le projet TRUST-ML, financé par l’UE, vise à développer un cadre unifié d’évaluation de la fiabilité quantitative des modèles de réseaux neuronaux couramment utilisés dans les systèmes électriques. TRUST-ML utilisera une nouvelle approche d’optimisation convexe pour évaluer la fiabilité de l’AT en termes de performance, de robustesse et d’interprétabilité. Il est également conçu pour répondre aux besoins émergents des réseaux électriques actuels.
Objectif
Deep decarbonization of the energy sector will require massive penetration of stochastic renewable energy resources and an enormous amount of grid asset coordination; this represents a challenging paradigm for power system operators. With its ability to learn in complex environments and provide predictive solutions on fast timescales, machine learning (ML) is posed to help overcome these challenges and dramatically transform power systems in coming decades. Emerging EU verification standards, however, will require that all ML and Reinforcement Learning (RL) used in safety critical applications be demonstrably trustworthy. In this project, we develop a unified framework, known as Trust-ML, for assessing the quantitative trustworthiness of the neural network models commonly used in power systems. Trust-ML uses a novel, convex optimization approach to assess ML trustworthiness across three key dimensions: performance, robustness, and interpretability. The approach is engineered to be scalable, and by design, it generates exact verification guarantees. Furthermore, Trust-ML is designed to meet the emerging needs of actual power systems. In particular, it can verify the performance of multi-agent RL systems in rigorous ways, and its relaxed counterpart can offer tractable, worst-case performance guarantees in the context of online learning. The resulting verification tools will be published as open-source software packages and shared widely with researchers and industry. This project will advance state-of-the-art methods across several interdisciplinary fields, it will help remove the barriers associated with machine learning deployment in power systems, and its outcomes will help push European power grids into competitive spaces. Coming from MIT with advanced training in power systems, the project PI, Samuel Chevalier, is characteristically well-suited to build Trust-ML, and his team of advisors represents a mixture of experts across power, optimization, and learning.
Champ scientifique
- natural sciencescomputer and information sciencessoftware
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencescomputer and information sciencesartificial intelligencemachine learningreinforcement learning
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectrical engineeringpower engineeringelectric power transmission
- natural sciencescomputer and information sciencesartificial intelligencecomputational intelligence
Mots‑clés
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
MSCA-PF - MSCA-PFCoordinateur
2800 Kongens Lyngby
Danemark