arXiv Machine Learning July 9, 2026 By Juan Manuel Garcia-Perez, Carlos Mateo

Creating Power Distribution Network Layouts Using Generative Adversarial Networks and Image-Based Representations

Read the original on arXiv Machine Learning →

arXiv:2607. 06622v1 Announce Type: cross Abstract: Utilities increasingly rely on planning and operational tools to cope with the increased penetrations of distributed energy resources, yet the lack of realistic, openly available datasets remains a major barrier for benchmarking and comparison.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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