QCAE: A quadruple branch CNN autoencoder for real-time electricity price forecasting

Real-time electricity market data is highly volatile and very noisy. The properties of such data make forecasting models difficult to develop, with traditional statistical models in particular affected by the “curse of dimensionality” for such data. However, autoencoders, or neural networks specific...

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Veröffentlicht in:	International journal of electrical power & energy systems Jg. 141; S. 108092
Hauptverfasser:	Yang, Haolin, Schell, Kristen R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.10.2022
Schlagworte:	Autoencoder Conventional neural network Deep learning Electricity price forecasting Deep learning Electricity price forecasting Autoencoder Conventional neural network
ISSN:	0142-0615, 1879-3517
Online-Zugang:	Volltext
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