Trust-ML: An Optimization-based Platform for Building Trust in Machine Learning Models used for Power Systems

Informations projet

TRUST-ML

N° de convention de subvention: 101066991

DOI

10.3030/101066991

Projet clôturé le 22 Janvier 2024

Date de signature de la CE 10 Juin 2022

Date de début 15 Juin 2022

Date de fin 14 Juin 2024

Financé au titre de

• Marie Skłodowska-Curie Actions (MSCA)

Coût total

Aucune donnée

Contribution de l’UE

€ 230 774,40

Coordonné par

DANMARKS TEKNISKE UNIVERSITET
Denmark

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Livrables

Data Management Plan (1)

Data Management Plan

The Data Management Plan describes the data management life cycle for all data sets that will be collected, processed or generated by the action. It is a document describing what data will be collected, processed or generated and following what methodology and standards, whether and how this data will be shared and/or made open, and how it will be curated and preserved.

Documents, reports (1)

Communication, Dissemination & Outreach Plan

The plan describes the planned measures to maximize the impact of the project, including the dissemination and exploitation measures that are planned, and the target group(s) addressed. Regarding communication measures and public engagement strategy, the aim is to inform and reach out to society and show the activities performed, and the use and the benefits the project will have for citizens.

Publications

Conference proceedings (6)

GPU-Accelerated Verification of Machine Learning Models for Power Systems

Auteurs: Chevalier, Samuel and Murzakhanov, Ilgiz and Chatzivasileiadis, Spyros
Publié dans: Proceedings of the 57th Hawaii International Conference on System Sciences, 2023
Éditeur: Proceedings of the 57th Hawaii International Conference on System Sciences
DOI: 10.48550/arXiv.2306.10617

Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets

Auteurs: Nadal, Ignasi Ventura; Chevalier, Samuel
Publié dans: ISGT Europe, 2023
Éditeur: ISGT Europe
DOI: 10.48550/arxiv.2304.10912

11th Bulk Power Systems Dynamics and Control Symposium

Auteurs: Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis
Publié dans: 11th Bulk Power Systems Dynamics and Control Symposium, 2022
Éditeur: 11th Bulk Power Systems Dynamics and Control Symposium
DOI: 10.48550/arXiv.2203.07505

Emission-Aware Optimization of Gas Networks: Input-Convex Neural Network Approach

Auteurs: Dvorkin, Vladimir; Chevalier, Samuel; Chatzivasileiadis, Spyros
Publié dans: Conference on Decision and Control, 2022
Éditeur: Conference on Decision and Control
DOI: 10.48550/arxiv.2209.08645

Optimization-Based Exploration of the Feasible Power Flow Space for Rapid Data Collection

Auteurs: Nadal, Ignasi Ventura; Chevalier, Samuel
Publié dans: Smart Grid Comm, 2022
Éditeur: Smart Grid Comm
DOI: 10.48550/arxiv.2206.12214

Towards Optimal Kron-based Reduction Of Networks (Opti-KRON) for the Electric Power Grid

Auteurs: Samuel Chevalier and Mads R. Almassalkhi
Publié dans: Conference on Decision and Control, 2022
Éditeur: Conference on Decision and Control
DOI: 10.48550/arXiv.2204.05554

Peer reviewed articles (2)

A Parallelized, Adam-Based Solver for Reserve and Security Constrained AC Unit Commitment

Auteurs: Chevalier, Samuel
Publié dans: EPSR, 2024, ISSN 1873-2046
Éditeur: EPSR
DOI: 10.48550/arxiv.2310.06650

IEEE Transactions on Industry Applications

Auteurs: Samuel Chevalier and Spyros Chatzivasileiadis
Publié dans: IEEE Industry Applications Society (Under Review), 2024, ISSN 0093-9994
Éditeur: Institute of Electrical and Electronics Engineers
DOI: 10.48550/arXiv.2211.07125

Monographic books (1)

Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations

Auteurs: Hamilton, Robert I.; Stiasny, Jochen; Ahmad, Tabia; Chevalier, Samuel; Nellikkath, Rahul; Murzakhanov, Ilgiz; Chatzivasileiadis, Spyros; Papadopoulos, Panagiotis N.
Publié dans: ICLR 2024, 2024
Éditeur: ICLR 2024
DOI: 10.48550/arxiv.2209.05793