Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/80608
Title: Wind Power Forecasting Using Neural Network Ensembles With Feature Selection
Authors: Li, Song
Wang, Peng
Goel, Lalit
Keywords: Wind forecasting
Issue Date: 2015
Source: 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.
Series/Report no.: IEEE Transactions on Sustainable Energy
Abstract: 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.
URI: https://hdl.handle.net/10356/80608
http://hdl.handle.net/10220/40549
ISSN: 1949-3029
DOI: http://dx.doi.org/10.1109/TSTE.2015.2441747
Rights: © 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].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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