Synthetic Time-Series Data Generation for Smart Grids Using 3D Autoencoder GAN

Given the growing significance of data-driven approaches in analysis and decision-making in smart grid, the availability of diverse and representative datasets is paramount. However, challenges such as privacy concerns, data size limitations, and data quality issues have constrained the usage of rea...

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Veröffentlicht in:	IEEE transactions on industrial informatics Jg. 21; H. 7; S. 5047 - 5058
Hauptverfasser:	Zhang, Guihai, Sikdar, Biplab
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.07.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	3D convolution Autoencoders Correlation Data models Data privacy Datasets Deep learning Electric vehicles Generative adversarial networks generative adversarial networks (GANs) Households Load modeling Multivariate analysis Power demand power grid data Prediction models Smart grid Synthetic data Three-dimensional displays Time series time series generation Training
ISSN:	1551-3203, 1941-0050
Online-Zugang:	Volltext
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