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

Información del proyecto

TRUST-ML

Identificador del acuerdo de subvención: 101066991

DOI

10.3030/101066991

Proyecto finalizado el 22 Enero 2024

Fecha de la firma de la CE 10 Junio 2022

Fecha de inicio 15 Junio 2022

Fecha de finalización 14 Junio 2024

Financiado con arreglo a

• Marie Skłodowska-Curie Actions (MSCA)

Coste total

Sin datos

Aportación de la UE

€ 230 774,40

Coordinado por

DANMARKS TEKNISKE UNIVERSITET
Denmark

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

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.

Publicaciones

Conference proceedings (6)

GPU-Accelerated Verification of Machine Learning Models for Power Systems

Autores: Chevalier, Samuel and Murzakhanov, Ilgiz and Chatzivasileiadis, Spyros
Publicado en: Proceedings of the 57th Hawaii International Conference on System Sciences, 2023
Editor: 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

Autores: Nadal, Ignasi Ventura; Chevalier, Samuel
Publicado en: ISGT Europe, 2023
Editor: ISGT Europe
DOI: 10.48550/arxiv.2304.10912

11th Bulk Power Systems Dynamics and Control Symposium

Autores: Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis
Publicado en: 11th Bulk Power Systems Dynamics and Control Symposium, 2022
Editor: 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

Autores: Dvorkin, Vladimir; Chevalier, Samuel; Chatzivasileiadis, Spyros
Publicado en: Conference on Decision and Control, 2022
Editor: Conference on Decision and Control
DOI: 10.48550/arxiv.2209.08645

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

Autores: Nadal, Ignasi Ventura; Chevalier, Samuel
Publicado en: Smart Grid Comm, 2022
Editor: Smart Grid Comm
DOI: 10.48550/arxiv.2206.12214

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

Autores: Samuel Chevalier and Mads R. Almassalkhi
Publicado en: Conference on Decision and Control, 2022
Editor: 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

Autores: Chevalier, Samuel
Publicado en: EPSR, 2024, ISSN 1873-2046
Editor: EPSR
DOI: 10.48550/arxiv.2310.06650

IEEE Transactions on Industry Applications

Autores: Samuel Chevalier and Spyros Chatzivasileiadis
Publicado en: IEEE Industry Applications Society (Under Review), 2024, ISSN 0093-9994
Editor: 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

Autores: Hamilton, Robert I.; Stiasny, Jochen; Ahmad, Tabia; Chevalier, Samuel; Nellikkath, Rahul; Murzakhanov, Ilgiz; Chatzivasileiadis, Spyros; Papadopoulos, Panagiotis N.
Publicado en: ICLR 2024, 2024
Editor: ICLR 2024
DOI: 10.48550/arxiv.2209.05793