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

Project Information

TRUST-ML

Grant agreement ID: 101066991

DOI 10.3030/101066991

Project terminated on 22 January 2024

EC signature date 10 June 2022

Start date 15 June 2022

End date 14 June 2024

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost No data

EU contribution € 230 774,40

Coordinated by

DANMARKS TEKNISKE UNIVERSITET
Denmark

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Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

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

Author(s): Chevalier, Samuel and Murzakhanov, Ilgiz and Chatzivasileiadis, Spyros
Published in: Proceedings of the 57th Hawaii International Conference on System Sciences, 2023
Publisher: 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

Author(s): Nadal, Ignasi Ventura; Chevalier, Samuel
Published in: ISGT Europe, 2023
Publisher: ISGT Europe
DOI: 10.48550/arxiv.2304.10912

11th Bulk Power Systems Dynamics and Control Symposium

Author(s): Jochen Stiasny, Samuel Chevalier, Rahul Nellikkath, Brynjar Sævarsson, Spyros Chatzivasileiadis
Published in: 11th Bulk Power Systems Dynamics and Control Symposium, 2022
Publisher: 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

Author(s): Dvorkin, Vladimir; Chevalier, Samuel; Chatzivasileiadis, Spyros
Published in: Conference on Decision and Control, 2022
Publisher: Conference on Decision and Control
DOI: 10.48550/arxiv.2209.08645

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

Author(s): Nadal, Ignasi Ventura; Chevalier, Samuel
Published in: Smart Grid Comm, 2022
Publisher: Smart Grid Comm
DOI: 10.48550/arxiv.2206.12214

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

Author(s): Samuel Chevalier and Mads R. Almassalkhi
Published in: Conference on Decision and Control, 2022
Publisher: 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

Author(s): Chevalier, Samuel
Published in: EPSR, 2024, ISSN 1873-2046
Publisher: EPSR
DOI: 10.48550/arxiv.2310.06650

IEEE Transactions on Industry Applications

Author(s): Samuel Chevalier and Spyros Chatzivasileiadis
Published in: IEEE Industry Applications Society (Under Review), 2024, ISSN 0093-9994
Publisher: 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

Author(s): Hamilton, Robert I.; Stiasny, Jochen; Ahmad, Tabia; Chevalier, Samuel; Nellikkath, Rahul; Murzakhanov, Ilgiz; Chatzivasileiadis, Spyros; Papadopoulos, Panagiotis N.
Published in: ICLR 2024, 2024
Publisher: ICLR 2024
DOI: 10.48550/arxiv.2209.05793