Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection

Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty, multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed. Th...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Gang Zhang, Benben Xu, Hongchi Liu, Jinwang Hou, Jiangbin Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Wind power prediction feature selection variational mode decomposition (VMD) max-relevance and min-re-dundancy (mRMR) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/c9b0de10f3ce483d9de93f8dcddb7708
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c9b0de10f3ce483d9de93f8dcddb7708
record_format	dspace
spelling	oai:doaj.org-article:c9b0de10f3ce483d9de93f8dcddb77082021-11-27T00:00:35ZWind Power Prediction Based on Variational Mode Decomposition and Feature Selection2196-542010.35833/MPCE.2020.000205https://doaj.org/article/c9b0de10f3ce483d9de93f8dcddb77082021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9205720/https://doaj.org/toc/2196-5420Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty, multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed. Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection. Firstly, the variational mode decomposition (VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy (mRMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network (BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine (LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.Gang ZhangBenben XuHongchi LiuJinwang HouJiangbin ZhangIEEEarticleWind power predictionfeature selectionvariational mode decomposition (VMD)max-relevance and min-re-dundancy (mRMR)Production of electric energy or power. Powerplants. Central stationsTK1001-1841Renewable energy sourcesTJ807-830ENJournal of Modern Power Systems and Clean Energy, Vol 9, Iss 6, Pp 1520-1529 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Wind power prediction feature selection variational mode decomposition (VMD) max-relevance and min-re-dundancy (mRMR) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
spellingShingle	Wind power prediction feature selection variational mode decomposition (VMD) max-relevance and min-re-dundancy (mRMR) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830 Gang Zhang Benben Xu Hongchi Liu Jinwang Hou Jiangbin Zhang Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection
description	Accurate wind power prediction can scientifically arrange wind power output and timely adjust power system dispatching plans. Wind power is associated with its uncertainty, multi-frequency and nonlinearity for it is susceptible to climatic factors such as temperature, air pressure and wind speed. Therefore, this paper proposes a wind power prediction model combining multi-frequency combination and feature selection. Firstly, the variational mode decomposition (VMD) is used to decompose the wind power data, and the sub-components with different fluctuation characteristics are obtained and divided into high-, intermediate-, and low-frequency components according to their fluctuation characteristics. Then, a feature set including historical data of wind power and meteorological factors is established, which chooses the feature sets of each component by using the max-relevance and min-redundancy (mRMR) feature selection method based on mutual information selected from the above set. Each component and its corresponding feature set are used as an input set for prediction afterwards. Thereafter, the high-frequency input set is predicted using back propagation neural network (BPNN), and the intermediate-and low-frequency input sets are predicted using least squares support vector machine (LS-SVM). After obtaining the prediction results of each component, BPNN is used for integration to obtain the final predicted value of wind power, and the ramping rate is verified. Finally, through the comparison, it is found that the proposed model has higher prediction accuracy.
format	article
author	Gang Zhang Benben Xu Hongchi Liu Jinwang Hou Jiangbin Zhang
author_facet	Gang Zhang Benben Xu Hongchi Liu Jinwang Hou Jiangbin Zhang
author_sort	Gang Zhang
title	Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection
title_short	Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection
title_full	Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection
title_fullStr	Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection
title_full_unstemmed	Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection
title_sort	wind power prediction based on variational mode decomposition and feature selection
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/c9b0de10f3ce483d9de93f8dcddb7708
work_keys_str_mv	AT gangzhang windpowerpredictionbasedonvariationalmodedecompositionandfeatureselection AT benbenxu windpowerpredictionbasedonvariationalmodedecompositionandfeatureselection AT hongchiliu windpowerpredictionbasedonvariationalmodedecompositionandfeatureselection AT jinwanghou windpowerpredictionbasedonvariationalmodedecompositionandfeatureselection AT jiangbinzhang windpowerpredictionbasedonvariationalmodedecompositionandfeatureselection
_version_	1718409255197868032

Wind Power Prediction Based on Variational Mode Decomposition and Feature Selection

Ejemplares similares