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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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FEATURED ARTICLE

Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
Issue 3/2017

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  3/2017 - 14
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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance

ZHANG, Y., WANG, P., CHENG, P., LEI, S.
 
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Download PDF pdficon (1,200 KB) | Citation | Downloads: 49 | Views: 67

Author keywords
ARMA model, Lorenz system, renewable energy, wavelet decomposition, wind speed prediction

References keywords
wind(14), energy(10), speed(8), prediction(8), wang(6), time(6), power(6), systems(5), series(5), forecasting(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-08-31
Volume 17, Issue 3, Year 2017, On page(s): 107 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.03014

Abstract
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Due to the sustainable and pollution-free characteristics, wind energy has been one of the fastest growing renewable energy sources. However, the intermittent and random fluctuation of wind speed presents many challenges for reliable wind power integration and normal operation of wind farm. Accurate wind speed prediction is the key to ensure the safe operation of power system and to develop wind energy resources. Therefore, this paper has presented a wavelet time series wind speed prediction model based on Lorenz disturbance. Therefore, in this paper, combined with the atmospheric dynamical system, a wavelet-time series improved wind speed prediction model based on Lorenz disturbance is proposed and the wind turbines of different climate types in Spain and China are used to simulate the disturbances of Lorenz equations with different initial values. The prediction results show that the improved model can effectively correct the preliminary prediction of wind speed, improving the prediction. In a word, the research work in this paper will be helpful to arrange the electric power dispatching plan and ensure the normal operation of the wind farm.


References | Cited By  «-- Click to see who has cited this paper

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[9] Y. G. Zhang, P. H. Wang, T. Ni, P. L. Cheng, S. Lei. "Wind Power Prediction Based on LS-SVM Model with Error Correction," Advances in Electrical and Computer Engineering, vol. 17, pp. 3-8, Feb. 2017.
[CrossRef] [Full Text] [Web of Science Record] [SCOPUS Record]


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[CrossRef]


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References Weight

Web of Science® Citations for all references: 592 TCR
SCOPUS® Citations for all references: 502 TCR

Web of Science® Average Citations per reference: 24 ACR
SCOPUS® Average Citations per reference: 20 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2017-09-17 02:26 in 241 seconds.




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