A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks

Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.

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Published
2024-10-14
DOI
https://doi.org/10.1109/isgteurope62998.2024.10863139
Primary Topic
Embedded Systems Design Techniques
Type
article
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article

A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks

Jochen Cremer, Adrian Kelly, Panagiotis N. Papadopoulos, Ricardo Jorge Bessa et al.
Embedded Systems Design Techniques
article

A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks

Jochen Cremer, Adrian Kelly, Panagiotis N. Papadopoulos, Ricardo Jorge Bessa, Antoine Marot, Milos Subasic, Samuel Young, Amar Sagar
article en

Abstract

Advanced control, operation, and planning tools of electrical networks with ML are not straightforward. 110 experts were surveyed to show where and how ML algorithms could advance. This paper assesses this survey and research environment. Then, it develops an innovation roadmap that helps align our research community with a goal-oriented realisation of the opportunities that AI upholds. This paper finds that the R&D environment of system operators (and the surrounding research ecosystem) needs adaptation to enable faster developments with AI while maintaining high testing quality and safety. This roadmap serves system operators, academics, and labs advancing next-generation electrical network tools.

University of Manchester (GB), Hitachi (Germany) (DE), INESC TEC (PT), Arizona State University (US), Delft University of Technology (NL)
Openalex Percentile: Top 70%
Embedded Systems Design Techniques
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A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks — Jochen Cremer, Adrian Kelly, et al. · (2024) | TGRS Research Map | TGRS