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

2024-05-27 · Jochen L. Cremer, Adrian Kelly, Ricardo J. Bessa, Milos Subasic, Panagiotis N. Papadopoulos, Samuel Young, Amar Sagar, Antoine Marot

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.

📄 PDF Abstract BibTeX arXiv:2405.17184

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ALIGN In the ALIGN method, visual and language representations are jointly trained from noisy image alt-text data. The image and text encoders are learned via contrastive loss…

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