A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911982524825600 |
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| author | Cremer, Jochen L. Kelly, Adrian Bessa, Ricardo J. Subasic, Milos Papadopoulos, Panagiotis N. Young, Samuel Sagar, Amar Marot, Antoine |
| author_facet | Cremer, Jochen L. Kelly, Adrian Bessa, Ricardo J. Subasic, Milos Papadopoulos, Panagiotis N. Young, Samuel Sagar, Amar Marot, Antoine |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_17184 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks Cremer, Jochen L. Kelly, Adrian Bessa, Ricardo J. Subasic, Milos Papadopoulos, Panagiotis N. Young, Samuel Sagar, Amar Marot, Antoine Systems and Control Signal Processing 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. |
| title | A Pioneering Roadmap for ML-Driven Algorithmic Advancements in Electrical Networks |
| topic | Systems and Control Signal Processing |
| url | https://arxiv.org/abs/2405.17184 |