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

Projektinformationen

TRUST-ML

ID Finanzhilfevereinbarung: 101066991

DOI

10.3030/101066991

Das Projekt endete am 22 Januar 2024

EK-Unterschriftsdatum 10 Juni 2022

Startdatum 15 Juni 2022

Enddatum 14 Juni 2024

Finanziert unter

• Marie Skłodowska-Curie Actions (MSCA)

Gesamtkosten

Keine Daten

EU-Beitrag

€ 230 774,40

Koordiniert durch

DANMARKS TEKNISKE UNIVERSITET
Denmark

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

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.

Veröffentlichungen

Conference proceedings (6)

GPU-Accelerated Verification of Machine Learning Models for Power Systems

Autoren: Chevalier, Samuel and Murzakhanov, Ilgiz and Chatzivasileiadis, Spyros
Veröffentlicht in: Proceedings of the 57th Hawaii International Conference on System Sciences, 2023
Herausgeber: 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

Autoren: Nadal, Ignasi Ventura; Chevalier, Samuel
Veröffentlicht in: ISGT Europe, 2023
Herausgeber: ISGT Europe
DOI: 10.48550/arxiv.2304.10912

11th Bulk Power Systems Dynamics and Control Symposium

Autoren: Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis
Veröffentlicht in: 11th Bulk Power Systems Dynamics and Control Symposium, 2022
Herausgeber: 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

Autoren: Dvorkin, Vladimir; Chevalier, Samuel; Chatzivasileiadis, Spyros
Veröffentlicht in: Conference on Decision and Control, 2022
Herausgeber: Conference on Decision and Control
DOI: 10.48550/arxiv.2209.08645

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

Autoren: Nadal, Ignasi Ventura; Chevalier, Samuel
Veröffentlicht in: Smart Grid Comm, 2022
Herausgeber: Smart Grid Comm
DOI: 10.48550/arxiv.2206.12214

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

Autoren: Samuel Chevalier and Mads R. Almassalkhi
Veröffentlicht in: Conference on Decision and Control, 2022
Herausgeber: 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

Autoren: Chevalier, Samuel
Veröffentlicht in: EPSR, 2024, ISSN 1873-2046
Herausgeber: EPSR
DOI: 10.48550/arxiv.2310.06650

IEEE Transactions on Industry Applications

Autoren: Samuel Chevalier and Spyros Chatzivasileiadis
Veröffentlicht in: IEEE Industry Applications Society (Under Review), 2024, ISSN 0093-9994
Herausgeber: 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

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