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Deep learning is blowing in the wind. Deep models applied to wind prediction at turbine level

Author
Manero, J.; Bejar, J.; Cortes, U.
Type of activity
Presentation of work at congresses
Name of edition
WindEurope Conference and Exhibition 2019
Date of publication
2019
Presentation's date
2019-05
Book of congress proceedings
Journal of physics: conference series, vol. 1222, Maig 2019, article 012037
First page
1
Last page
11
Publisher
Institute of Physics (IOP)
DOI
10.1088/1742-6596/1222/1/012037
Project funding
Computación de Altas Prestaciones VII
Models de Programacio i Entorns d'eXecució PARal.lels
Repository
http://hdl.handle.net/2117/135298 Open in new window
URL
https://iopscience.iop.org/article/10.1088/1742-6596/1222/1/012037/meta Open in new window
Abstract
Wind Energy generation depends on the existence of wind, a meteorological phenomena intermittent by nature, with the consequence of generating uncertainty on the availability of wind energy in the future. The grid stability processes require continuous forecasting of wind energy generated. Forecasting wind energy can be performed either by using weather forecast data or by projecting (or regressing) the past time-series data observations into the future. This last method is the statistical or ti...
Citation
Manero, J.; Béjar, J.; Cortés, U. Deep learning is blowing in the wind. Deep models applied to wind prediction at turbine level. A: WindEurope Conference and Exhibition. "Journal of physics: conference series, vol. 1222, Maig 2019, article 012037". Londres: Institute of Physics (IOP), p. 1-11.
Keywords
Complex networks, Convolutional networks, Deep learning, Learning architectures, Meteorological phenomena, Multi-layer perceptrons, Network architecture, Statistical methodologies, Supercomputing centers, Time series, Weather forecasting, Wind, Wind energy generation, Wind power, Wind speed prediction
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
KEMLG - Knowledge Engineering and Machine Learning Group

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