“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

“Dust in the Wind...”, Deep Learning Application to Wind Energy Time Series Forecasting

To balance electricity production and demand, it is required to use different prediction techniques extensively. Renewable energy, due to its intermittency, increases the complexity and uncertainty of forecasting, and the resulting accuracy impacts all the different players acting around the electri...

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Bibliographic Details
Main Authors: Jaume Manero, Javier Béjar, Ulises Cortés
Format: Article
Language:English
Published: MDPI AG 2019-06-01
Series:Energies
Subjects:
wind energy forecasting
time series
deep learning
RNN
MLP
CNN
wind speed forecasting
wind time series
multi-step forecasting
Online Access:https://www.mdpi.com/1996-1073/12/12/2385

Internet

https://www.mdpi.com/1996-1073/12/12/2385

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