Short-term forecasting of wind energy: A comparison of deep learning frameworks

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Show simple item record Mora, Elianne Cifuentes Quintero, Jenny Alexandra Marulanda, Geovanny 2022-05-20T07:36:20Z 2022-05-20T07:36:20Z 2021-12-01
dc.identifier.bibliographicCitation Mora, E., Cifuentes, J., & Marulanda, G. (2021). Short-Term Forecasting of Wind Energy: A Comparison of Deep Learning Frameworks. In Energies (Vol. 14, Issue 23, p. 7943). MDPI AG.
dc.identifier.issn 1996-1073
dc.description.abstract Wind energy has been recognized as the most promising and economical renewable energy source, attracting increasing attention in recent years. However, considering the variability and uncertainty of wind energy, accurate forecasting is crucial to propel high levels of wind energy penetration within electricity markets. In this paper, a comparative framework is proposed where a suite of long short-term memory (LSTM) recurrent neural networks (RNN) models, inclusive of standard, bidirectional, stacked, convolutional, and autoencoder architectures, are implemented to address the existing gaps and limitations of reported wind power forecasting methodologies. These integrated networks are implemented through an iterative process of varying hyperparameters to better assess their effect, and the overall performance of each architecture, when tackling one-hour to three-hours ahead wind power forecasting. The corresponding validation is carried out through hourly wind power data from the Spanish electricity market, collected between 2014 and 2020. The proposed comparative error analysis shows that, overall, the models tend to showcase low error variability and better performance when the networks are able to learn in weekly sequences. The model with the best performance in forecasting one-hour ahead wind power is the stacked LSTM, implemented with weekly learning input sequences, with an average MAPE improvement of roughly 6, 7, and 49%, when compared to standard, bidirectional, and convolutional LSTM models, respectively. In the case of two to three-hours ahead forecasting, the model with the best overall performance is the bidirectional LSTM implemented with weekly learning input sequences, showcasing an average improved MAPE performance from 2 to 23% when compared to the other LSTM architectures implemented.
dc.format.extent 26
dc.language.iso eng
dc.publisher MDPI AG
dc.rights © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
dc.rights Atribución 3.0 España
dc.subject.other Deep learning
dc.subject.other Long short-term memory
dc.subject.other Time series forecasting
dc.subject.other Wind power forecasting
dc.title Short-term forecasting of wind energy: A comparison of deep learning frameworks
dc.type article
dc.subject.eciencia Estadística
dc.subject.eciencia Ingeniería Mecánica
dc.rights.accessRights openAccess
dc.type.version publishedVersion
dc.identifier.publicationfirstpage 7943
dc.identifier.publicationissue 23
dc.identifier.publicationlastpage 7969
dc.identifier.publicationtitle Energies (Energies)
dc.identifier.publicationvolume 14
dc.identifier.uxxi AR/0000030537
dc.affiliation.dpto UC3M. Departamento de Ingeniería Eléctrica
dc.affiliation.grupoinv UC3M. Grupo de Investigación: Redes y Sistemas de Energía Eléctrica (REDES)
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