Wind Power Forecasting Using Neural Network Ensembles With Feature Selection

In this paper, a novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a wind farm. Based on the conditional mutual information, a feature selection technique is developed t...

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Main Authors:	Li, Song, Wang, Peng, Goel, Lalit
Other Authors:	School of Electrical and Electronic Engineering
Format:	Article
Language:	English
Published:	2016
Subjects:	Wind forecasting
Online Access:	https://hdl.handle.net/10356/80608 http://hdl.handle.net/10220/40549
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-806082020-03-07T13:57:22Z Wind Power Forecasting Using Neural Network Ensembles With Feature Selection Li, Song Wang, Peng Goel, Lalit School of Electrical and Electronic Engineering Wind forecasting In this paper, a novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a wind farm. Based on the conditional mutual information, a feature selection technique is developed to choose a compact set of input features for the forecasting model. In order to overcome the nonstationarity of wind power series and improve the forecasting accuracy, a new wavelet-based ensemble scheme is integrated into the model. The individual forecasters are featured with different mixtures of the mother wavelet and the number of decomposition levels. The individual outputs are combined to form the ensemble forecast output using the PLSR method. To confirm the effectiveness, the proposed method is examined on real-world datasets and compared with other forecasting methods. Accepted version 2016-05-20T02:49:41Z 2019-12-06T13:53:10Z 2016-05-20T02:49:41Z 2019-12-06T13:53:10Z 2015 Journal Article Li, S., Wang, P., & Goel, L. (2015). Wind Power Forecasting Using Neural Network Ensembles With Feature Selection. IEEE Transactions on Sustainable Energy, 6(4), 1447-1456. 1949-3029 https://hdl.handle.net/10356/80608 http://hdl.handle.net/10220/40549 10.1109/TSTE.2015.2441747 en IEEE Transactions on Sustainable Energy © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TSTE.2015.2441747]. 10 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	Wind forecasting
spellingShingle	Wind forecasting Li, Song Wang, Peng Goel, Lalit Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
description	In this paper, a novel ensemble method consisting of neural networks, wavelet transform, feature selection, and partial least-squares regression (PLSR) is proposed for the generation forecasting of a wind farm. Based on the conditional mutual information, a feature selection technique is developed to choose a compact set of input features for the forecasting model. In order to overcome the nonstationarity of wind power series and improve the forecasting accuracy, a new wavelet-based ensemble scheme is integrated into the model. The individual forecasters are featured with different mixtures of the mother wavelet and the number of decomposition levels. The individual outputs are combined to form the ensemble forecast output using the PLSR method. To confirm the effectiveness, the proposed method is examined on real-world datasets and compared with other forecasting methods.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Li, Song Wang, Peng Goel, Lalit
format	Article
author	Li, Song Wang, Peng Goel, Lalit
author_sort	Li, Song
title	Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
title_short	Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
title_full	Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
title_fullStr	Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
title_full_unstemmed	Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
title_sort	wind power forecasting using neural network ensembles with feature selection
publishDate	2016
url	https://hdl.handle.net/10356/80608 http://hdl.handle.net/10220/40549
_version_	1681040477801938944

Wind Power Forecasting Using Neural Network Ensembles With Feature Selection

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