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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2017 - 14
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 HIGH-IMPACT PAPER 

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
 
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Download PDF pdficon (1,200 KB) | Citation | Downloads: 1,308 | Views: 4,113

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

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

[1] J. Z. Wang, Y. L. Song, F. Liu, R. Hou, "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, vol. 60, pp. 960-981, Feb. 2016.
[CrossRef] [Web of Science Times Cited 177] [SCOPUS Times Cited 208]


[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.
[CrossRef] [Web of Science Times Cited 131] [SCOPUS Times Cited 149]


[3] J. Koo, G. D. Han, H. J. Choi, J. H. Shim, "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, vol. 93, pp. 1296-1302, Nov. 2015.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 33]


[4] Ü. B. Filik, T. Filik, "Wind Speed Prediction Using Artificial Neural Networks Based on Multiple Local Measurements in Eskisehir," Energy Procedia, vol. 107, pp. 264 - 269, Sep. 2017.
[CrossRef] [Web of Science Times Cited 62] [SCOPUS Times Cited 78]


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[CrossRef] [Web of Science Times Cited 126] [SCOPUS Times Cited 145]


[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 157] [SCOPUS Times Cited 174]


[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 87] [SCOPUS Times Cited 103]


[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 638] [SCOPUS Times Cited 752]


[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 54] [SCOPUS Times Cited 62]


[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 149] [SCOPUS Times Cited 182]


[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 37] [SCOPUS Times Cited 39]


[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 123] [SCOPUS Times Cited 149]


[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 85] [SCOPUS Times Cited 97]


[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 66] [SCOPUS Times Cited 86]


[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 14] [SCOPUS Times Cited 16]


[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.
[CrossRef]


[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 70] [SCOPUS Times Cited 84]


[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 68] [SCOPUS Times Cited 86]


[19] http://www.sotaventogalicia.com/en/real-time-data/historical

[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 43] [SCOPUS Times Cited 38]


[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 22] [SCOPUS Times Cited 22]


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[CrossRef] [Web of Science Times Cited 343] [SCOPUS Times Cited 381]


[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 13] [SCOPUS Times Cited 14]


[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.
[CrossRef] [SCOPUS Times Cited 1]




References Weight

Web of Science® Citations for all references: 2,492 TCR
SCOPUS® Citations for all references: 2,899 TCR

Web of Science® Average Citations per reference: 100 ACR
SCOPUS® Average Citations per reference: 116 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 2024-07-14 06:42 in 156 seconds.




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