A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network

The proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain t...

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Detalles Bibliográficos
Autores: Wu, Jingxuan, Li, Shuting, Vasquez, Juan, Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/419912
Acceso en línea:https://hdl.handle.net/2117/419912
https://dx.doi.org/10.1016/j.ecmx.2024.100650
Access Level:acceso abierto
Palabra clave:BiLSTM
Deep learning
Mode decomposition
Wind power prediction
Àrees temàtiques de la UPC::Energies
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spelling A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural networkWu, JingxuanLi, ShutingVasquez, JuanGuerrero Zapata, Josep Maria|||0000-0001-5236-4592BiLSTMDeep learningMode decompositionWind power predictionÀrees temàtiques de la UPC::EnergiesThe proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain the accuracy of multi-step prediction performance. A multi-step WPF (MS-WPF) approach based on a time series bi-level empirical mode decomposition (BLEMD) method and BiLSTM neural network is proposed in this paper to improve the WPF accuracy of regional wind power generators. Since the uncertainty is always generated through coupled factors from both wind and weather-to-power conversion, the linearity feature is first introduced as an aspect apart from the frequency in the proposed approach to decompose the wind power time sequence data. The proposed BLEMD introduces Pearson product-moment correlation coefficient to evaluate the linearity of time series and a linearity-based decomposition algorithm is designed accordingly. To further enhance the precision and release computation burdens, a DL-based prediction strategy, including a BiLSTM network, a CNN-BiLSTM network, and a mean weight estimation method are implemented to predict the components separately. The proposed method only relies on local data, greatly reducing the data acquisition and computation cost. The precision of the proposed MS-WPF is verified by a 2.5 kW wind turbine with horizons from 5 s to 30 s, a 1.5 MW wind turbine with horizons from 10 min to 1 h, and a 51 MW wind farm with horizons from 1 h to 6 h. The comparative experimental results with other cutting-edge methods indicated that the proposed MS-WPF has superior prediction accuracy and stable performance for multi-step prediction.Peer ReviewedElsevier20242024-06-2220242024-12-05journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/419912https://dx.doi.org/10.1016/j.ecmx.2024.100650reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4199122026-05-27T15:37:01Z
dc.title.none.fl_str_mv A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
title A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
spellingShingle A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
Wu, Jingxuan
BiLSTM
Deep learning
Mode decomposition
Wind power prediction
Àrees temàtiques de la UPC::Energies
title_short A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
title_full A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
title_fullStr A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
title_full_unstemmed A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
title_sort A bi-level mode decomposition framework for multi-step wind power forecasting using deep neural network
dc.creator.none.fl_str_mv Wu, Jingxuan
Li, Shuting
Vasquez, Juan
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author Wu, Jingxuan
author_facet Wu, Jingxuan
Li, Shuting
Vasquez, Juan
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author_role author
author2 Li, Shuting
Vasquez, Juan
Guerrero Zapata, Josep Maria|||0000-0001-5236-4592
author2_role author
author
author
dc.subject.none.fl_str_mv BiLSTM
Deep learning
Mode decomposition
Wind power prediction
Àrees temàtiques de la UPC::Energies
topic BiLSTM
Deep learning
Mode decomposition
Wind power prediction
Àrees temàtiques de la UPC::Energies
description The proportion of wind energy in global energy structure is growing rapidly, promoting the development of wind power forecasting (WPF) technologies to solve the uncertainty and intermittence of wind power generation. However, the nonlinear and stochastic features of wind power time series restrain the accuracy of multi-step prediction performance. A multi-step WPF (MS-WPF) approach based on a time series bi-level empirical mode decomposition (BLEMD) method and BiLSTM neural network is proposed in this paper to improve the WPF accuracy of regional wind power generators. Since the uncertainty is always generated through coupled factors from both wind and weather-to-power conversion, the linearity feature is first introduced as an aspect apart from the frequency in the proposed approach to decompose the wind power time sequence data. The proposed BLEMD introduces Pearson product-moment correlation coefficient to evaluate the linearity of time series and a linearity-based decomposition algorithm is designed accordingly. To further enhance the precision and release computation burdens, a DL-based prediction strategy, including a BiLSTM network, a CNN-BiLSTM network, and a mean weight estimation method are implemented to predict the components separately. The proposed method only relies on local data, greatly reducing the data acquisition and computation cost. The precision of the proposed MS-WPF is verified by a 2.5 kW wind turbine with horizons from 5 s to 30 s, a 1.5 MW wind turbine with horizons from 10 min to 1 h, and a 51 MW wind farm with horizons from 1 h to 6 h. The comparative experimental results with other cutting-edge methods indicated that the proposed MS-WPF has superior prediction accuracy and stable performance for multi-step prediction.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-06-22
2024
2024-12-05
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/419912
https://dx.doi.org/10.1016/j.ecmx.2024.100650
url https://hdl.handle.net/2117/419912
https://dx.doi.org/10.1016/j.ecmx.2024.100650
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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