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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance

ZHANG, Y. See more information about ZHANG, Y. on SCOPUS See more information about ZHANG, Y. on IEEExplore See more information about ZHANG, Y. on Web of Science, WANG, P. See more information about  WANG, P. on SCOPUS See more information about  WANG, P. on SCOPUS See more information about WANG, P. on Web of Science, CHENG, P. See more information about  CHENG, P. on SCOPUS See more information about  CHENG, P. on SCOPUS See more information about CHENG, P. on Web of Science, LEI, S. See more information about LEI, S. on SCOPUS See more information about LEI, S. on SCOPUS See more information about LEI, S. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,200 KB) | Citation | Downloads: 377 | Views: 1,723

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

References keywords
wind(14), energy(10), speed(8), prediction(8), time(6), power(6), systems(5), series(5), forecasting(5), models(4)
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
Web of Science Accession Number: 000410369500014
SCOPUS ID: 85030118150

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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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[2] C. D. Zuluaga, M. A. Álvarez, E. Giraldo, "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, vol. 156, pp. 321–330, Jul. 2015.
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[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 8]

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[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 47]

[6] H. R. Zhao, S. Guo, "An optimized grey model for annual power load forecasting," Energy, vol. 107, pp. 272-286, Jul. 2016.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 50]

[7] H. P. Liu, J. Shi, E. Erdem, "Prediction of wind speed time series using modified Taylor Kriging method," Energy, vol. pp. 35, 4870-4879, Dec. 2010.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 64]

[8] E. Erdem, J. Shi, "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, vol. 88, pp. 1405–1414, Oct. 2011.
[CrossRef] [Web of Science Times Cited 234] [SCOPUS Times Cited 283]

[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 Times Cited 10] [SCOPUS Times Cited 11]

[10] J. Heinermann, O. Kramer, "Machine learning ensembles for wind power prediction," Renewable Energy, vol. 89, pp. 671-679, Dec. 2016.
[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 38]

[11] A. Glowacz. "Recognition of Acoustic Signals of Loaded Synchronous Motor Using FFT, MSAF-5 and LSVM," Archives of Acoustics, vol. 40, pp. 197-203, Feb. 2015.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 20]

[12] L. Karthikeyan, D. N. Kumar, "Predictability of nonstationary time series using wavelet and EMD based ARMA models," Journal of Hydrology, vol. 502, pp. 103–119, Aug. 2013.
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 65]

[13] H. K. Lam, F. H. F. Leung, and P. K. S. Tam. "Stable and Robust Fuzzy Control for Uncertain Nonlinear Systems," IEEE Transactions on Systems, Man, and Cybernetics-part A: Systems and Humans, vol. 30, pp. 825-839, Nov. 2000.
[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 72]

[14] R. E. Precup, S. Preitl. "PI-Fuzzy controllers for integral plants to ensure robust stability," Information Sciences, vol. 177, pp. 4410–4429, May, 2007.
[CrossRef] [Web of Science Times Cited 47] [SCOPUS Times Cited 67]

[15] A. El-Gohary, F. Bukhari, "Optimal control of Lorenz system during different time intervals," Applied Mathematics and Computation, vol. 144, pp. 337–351, Dec. 2003.
[CrossRef] [Web of Science Times Cited 12] [SCOPUS Times Cited 15]

[16] J. Lu, J.H. Lv, J. Xie, G. R. Chen, "Reconstruction of the Lorenz and Chen Systems with Noisy Observations," Computers and Mathematics with Applications, vol. 46, pp. 1427-1434, Oct. 2003.

[17] D. C. Kiplangat, K. Asokan, K. S. Kumar, "Improved week-ahead predictions of wind speed using simple linear models with wavelet decomposition," Renewable Energy, vol. 93, pp. 38-44, Aug. 2016.
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 29]

[18] X.L. An, D.X. Jiang, C. Liu, M.H. Zhao, "Wind farm power prediction based on wavelet decomposition and chaotic time series," Expert Systems with Applications, vol. 38, pp. 11280–11285, Sep. 2011.
[CrossRef] [Web of Science Times Cited 45] [SCOPUS Times Cited 55]


[20] A. Glowacz. "Recognition of acoustic signals of induction motor using FTF, SMOFS-10 and LSVM," Eksploatacja i Niezawodnosc-Maintenance and Reliability, vol. 17, pp. 569-574, Sep. 2015.
[CrossRef] [Web of Science Times Cited 14] [SCOPUS Times Cited 13]

[21] Y. G. Zhang, J. Y. Yang, K. C. Wang, Z. P. Wang, "Wind Power Prediction Considering Nonlinear Atmospheric Disturbances," Energies, vol. 8, pp. 475-489, Jan. 2015.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 15]

[22] W. Tucker, "The Lorenz attractor exists," Comptes Rendus de l'Académie des Sciences - Series I - Mathematics, vol. 328, pp. 1197-1202, Jun. 1999.
[CrossRef] [Web of Science Times Cited 223] [SCOPUS Times Cited 245]

[23] Y. G. Zhang, J. Y. Yang, K. C. Wang, Y. D. Wang, "Lorenz Wind Disturbance Model Based on Grey Generated Components," Energies, vol. 7, pp. 7178-7193, Nov. 2014.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 9]

[24] P. M. T. Broersen. "Automatic Time Series Identification Spectral Analysis with MATLAB Toolbox ARMASA," IFAC Proceedings Volumes, vol. 36, pp. 1435-1440, Sep. 2003.

References Weight

Web of Science® Citations for all references: 1,025 TCR
SCOPUS® Citations for all references: 1,198 TCR

Web of Science® Average Citations per reference: 41 ACR
SCOPUS® Average Citations per reference: 48 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 2019-04-21 19:35 in 162 seconds.

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Note2: SCOPUS® is a registered trademark of Elsevier B.V.
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