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Data efficiency assessment of generative adversarial networks in energy applications

This study investigates the data requirements of generative artificial intelligence (AI), particularly generative adversarial networks (GANs), for reliable data augmentation in energy applications. Generative AI, though seen as a solution to data limitations, requires substantial data to learn meani...

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Autori principali: Umme Mahbuba Nabila, Linyu Lin, Xingang Zhao, William L. Gurecky, Pradeep Ramuhalli, Majdi I. Radaideh
Natura: Artigo
Lingua:Inglês
Pubblicazione: Elsevier 2025-05-01
Serie:Energy and AI
Soggetti:
Generative AI
Generative adversarial networks
Critical heat flux
Data augmentation
Power grid energy forecasting
Accesso online:http://www.sciencedirect.com/science/article/pii/S2666546825000333
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