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

Informazioni relative al progetto

TRUST-ML

ID dell’accordo di sovvenzione: 101066991

DOI

10.3030/101066991

Progetto concluso il 22 Gennaio 2024

Data della firma CE 10 Giugno 2022

Data di avvio 15 Giugno 2022

Data di completamento 14 Giugno 2024

Finanziato da

• Marie Skłodowska-Curie Actions (MSCA)

Costo totale

Nessun dato

Contributo UE

€ 230 774,40

Coordinato da

DANMARKS TEKNISKE UNIVERSITET
Denmark

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

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.

Pubblicazioni

Conference proceedings (6)

GPU-Accelerated Verification of Machine Learning Models for Power Systems

Autori: Chevalier, Samuel and Murzakhanov, Ilgiz and Chatzivasileiadis, Spyros
Pubblicato in: Proceedings of the 57th Hawaii International Conference on System Sciences, 2023
Editore: 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

Autori: Nadal, Ignasi Ventura; Chevalier, Samuel
Pubblicato in: ISGT Europe, 2023
Editore: ISGT Europe
DOI: 10.48550/arxiv.2304.10912

11th Bulk Power Systems Dynamics and Control Symposium

Autori: Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis
Pubblicato in: 11th Bulk Power Systems Dynamics and Control Symposium, 2022
Editore: 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

Autori: Dvorkin, Vladimir; Chevalier, Samuel; Chatzivasileiadis, Spyros
Pubblicato in: Conference on Decision and Control, 2022
Editore: Conference on Decision and Control
DOI: 10.48550/arxiv.2209.08645

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

Autori: Nadal, Ignasi Ventura; Chevalier, Samuel
Pubblicato in: Smart Grid Comm, 2022
Editore: Smart Grid Comm
DOI: 10.48550/arxiv.2206.12214

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

Autori: Samuel Chevalier and Mads R. Almassalkhi
Pubblicato in: Conference on Decision and Control, 2022
Editore: 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

Autori: Chevalier, Samuel
Pubblicato in: EPSR, 2024, ISSN 1873-2046
Editore: EPSR
DOI: 10.48550/arxiv.2310.06650

IEEE Transactions on Industry Applications

Autori: Samuel Chevalier and Spyros Chatzivasileiadis
Pubblicato in: IEEE Industry Applications Society (Under Review), 2024, ISSN 0093-9994
Editore: 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

Autori: Hamilton, Robert I.; Stiasny, Jochen; Ahmad, Tabia; Chevalier, Samuel; Nellikkath, Rahul; Murzakhanov, Ilgiz; Chatzivasileiadis, Spyros; Papadopoulos, Panagiotis N.
Pubblicato in: ICLR 2024, 2024
Editore: ICLR 2024
DOI: 10.48550/arxiv.2209.05793