|3/2017 - 14|
Wind Speed Prediction with Wavelet Time Series Based on Lorenz DisturbanceZHANG, Y. , WANG, P. , CHENG, P. , LEI, S.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,200 KB) | Citation | Downloads: 867 | Views: 3,346|
ARMA model, Lorenz system, renewable energy, wavelet decomposition, wind speed prediction
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
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|
| 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 140] [SCOPUS Times Cited 159]
 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 112] [SCOPUS Times Cited 126]
 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 20] [SCOPUS Times Cited 25]
 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 48] [SCOPUS Times Cited 56]
 Y. Noorollahi, M. A. Jokar, A. Kalhor, "Using artificial neural networks for temporal and spatial wind speed forecasting in Iran," Energy Conversion and Management, vol. 115, pp. 17-25, May. 2016.
[CrossRef] [Web of Science Times Cited 103] [SCOPUS Times Cited 117]
 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 129] [SCOPUS Times Cited 146]
 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 79] [SCOPUS Times Cited 94]
 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 522] [SCOPUS Times Cited 607]
 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 46] [SCOPUS Times Cited 47]
 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 107] [SCOPUS Times Cited 131]
 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 36] [SCOPUS Times Cited 37]
 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 107] [SCOPUS Times Cited 127]
 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 82] [SCOPUS Times Cited 97]
 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 54] [SCOPUS Times Cited 73]
 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]
 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.
 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 56] [SCOPUS Times Cited 69]
 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 60] [SCOPUS Times Cited 78]
 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]
 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 19] [SCOPUS Times Cited 20]
 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 312] [SCOPUS Times Cited 346]
 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 13]
 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]
Web of Science® Citations for all references: 2,100 TCR
SCOPUS® Citations for all references: 2,422 TCR
Web of Science® Average Citations per reference: 84 ACR
SCOPUS® Average Citations per reference: 97 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 2022-11-28 13:56 in 138 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.