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Wind Power Prediction Based on LS-SVM Model with Error CorrectionZHANG, Y. , WANG, P., NI, T., CHENG, P., LEI, S.
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computer errors, error correction, support vector machines, power engineering computing, wind energy generation
wind(19), energy(15), prediction(11), speed(10), wang(9), power(7), renewable(6), jrenene(6), term(5), short(5)
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About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 3 - 8
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01001
Web of Science Accession Number: 000396335900001
SCOPUS ID: 85014266751
As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM) model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM). Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.
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 Wind energy prediction with LS-SVM based on Lorenz perturbation, Zhang, Yagang, Wang, Penghui, Zhang, Chenhong, Lei, Shuang, The Journal of Engineering, ISSN 2051-3305, Issue 13, Volume 2017, 2017.
Digital Object Identifier: 10.1049/joe.2017.0626 [CrossRef]
 Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 3, Volume 17, 2017.
Digital Object Identifier: 10.4316/AECE.2017.03014 [CrossRef] [Full text]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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