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“Dust in the wind...”, deep learning application to wind energy time series forecasting

Author
Manero, J.; Bejar, J.; Cortes, U.
Type of activity
Journal article
Journal
Energies
Date of publication
2019-06-21
Volume
12
Number
12
First page
1
Last page
20
DOI
10.3390/en12122385
Project funding
Computación de Altas Prestaciones VII
Models de Programacio i Entorns d'eXecució PARal.lels
Repository
http://hdl.handle.net/2117/166842 Open in new window
URL
https://www.mdpi.com/1996-1073/12/12/2385 Open in new window
Abstract
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 electricity systems around the world like generators, distributors, retailers, or consumers. Wind forecasting can be done under two major approaches, using meteorological numerical prediction models or based...
Citation
Manero, J.; Béjar, J.; Cortés, U. “Dust in the wind...”, deep learning application to wind energy time series forecasting. "Energies", 21 Juny 2019, vol. 12, núm. 12, p. 1-20.
Keywords
CNN, Deep learning, MLP, Multi-step forecasting, RNN, Time series, Wind energy forecasting, Wind speed forecasting, Wind time series
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

Participants

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