Data efficiency assessment of generative adversarial networks in energy applications

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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Bibliographic Details
Main Authors:	Umme Mahbuba Nabila, Linyu Lin, Xingang Zhao, William L. Gurecky, Pradeep Ramuhalli, Majdi I. Radaideh
Format:	Article
Language:	English
Published:	Elsevier 2025-05-01
Series:	Energy and AI
Subjects:	Generative AI Generative adversarial networks Critical heat flux Data augmentation Power grid energy forecasting
Online Access:	http://www.sciencedirect.com/science/article/pii/S2666546825000333
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