|2/2018 - 1|
View TOC | « Previous Article | Next Article »
Improved Wind Speed Prediction Using Empirical Mode DecompositionZHANG, Y. , ZHANG, C. , SUN, J. , GUO, J.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,055 KB) | Citation | Downloads: 1,654 | Views: 27,761|
renewable energy, wind speed prediction, empirical mode decomposition, radial basis function neural network, least squares support vector basis
wind(15), prediction(15), energy(10), speed(7), artificial(7), system(5), model(5), forecasting(5), time(4), term(4)
Blue keywords are present in both the references section and the paper title.
About this article
Date of Publication: 2018-05-31
Volume 18, Issue 2, Year 2018, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2018.02001
Web of Science Accession Number: 000434245000001
SCOPUS ID: 85047879257
Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD), the Radial Basis Function Neural Network (RBF) and the Least Square Support Vector Machine (SVM), an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM) is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM), the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.
|References|||||Cited By «-- Click to see who has cited this paper|
| International Energy Agency (IEA).World Energy Outlook 2017 [DB/OL].
 Global Wind Energy Council (GWEC). Global Statistics [DB/OL].
 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 147] [SCOPUS Times Cited 175]
 R. Rajesh "Forecasting supply chain resilience performance using grey prediction," Electronic Commerce Research and Applications, Vol.20, pp.42-58, sep.2016.
[CrossRef] [Web of Science Times Cited 52] [SCOPUS Times Cited 67]
 Y.G. Zhang, Y. Xu, Z. P. Wang, "GM (1, 1) grey prediction of Lorenz chaotic system," Chaos, Solitons and Fractals, vol. 42, pp. 1003-1009, Feb. 2009.
[CrossRef] [Web of Science Times Cited 28] [SCOPUS Times Cited 36]
 A. Bezuglov, G. Comert. "Short-term freeway traffic parameter prediction: Application of grey system theory models," Expert Systems with Application, vol. 62, pp.284-292, Nov.2016.
[CrossRef] [Web of Science Times Cited 133] [SCOPUS Times Cited 147]
 V. Prema, K. Uma Rao. "Development of statistical time series models for solar power prediction," Renewable Energy, vol. 83, pp.100-109, Nov.2015.
[CrossRef] [Web of Science Times Cited 63] [SCOPUS Times Cited 89]
 Y. N. Zhao, L. Ye, Z. Li, X. R. Song, Y. S. Lang, J. Su. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy. vol. 177, pp. 793-803, Mar.2016.
[CrossRef] [Web of Science Times Cited 132] [SCOPUS Times Cited 157]
 Y. G. Zhang, P. H. Wang, P. L. Cheng, S. Lei. "Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance," Advances in Electrical and Computer Engineering, vol. 17, pp.107-114, Aug. 2017.
 F. Bre, J.M. Gimenez, V.D. Fanchinotti. "Prediction of wind pressure coefficients on building surfaces using artificial neural networks," Energy and Buildings, vol.158, pp.1429-1441, Jan. 2018.
[CrossRef] [Web of Science Times Cited 84] [SCOPUS Times Cited 110]
 J. P. Jeon, C. Kim, B.D. Oh, S. J. Kim, Y.S. Kim. "Prediction of persistent hemodynamic depression after carotid angioplasty and stenting artificial neural network model," Clinical Neurology and Neurosurgery, vol. 164, pp. 127-131, Dec.2017.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 16]
 P. Ramasamy, S.S. Chandel, A.K. Yadav. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Applied Energy, vol. 80, pp.338-347, Aug.2015.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 124]
 M. Wagarachchi, A. Karunananda. "Optimization of artificial neural network architecture using neuroplasticity," International Journal of Artificial Intelligence, vol. 15, no. 1, pp. 112-125, 2017.
 L.N. Liu, Y.L. Lei. "An accurate ecological footprint analysis and prediction for Beijing based on SVM model," Ecological Informatics, vol. 17, pp.1574-9541, Jan. 2018.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 34]
 B.A. Moghram, E. Nabil, A. Badr. "Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design," Computer Methods and Programs in Biomedicine, vol. 153, pp. 161-170, Jan. 2018.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]
 D. Martin, B. Caballero, R. Haber. "Optimal tuning of a networked linear controller using a multi-objective genetic algorithm and its application to one complex electromechanical process," International Journal of Innovative Computing, Information and Control, vol. 5, no. 10 (B) pp. 3405-3414, 2009.
 R. E. Precup, S. Doboli, S. Preitl. "Stability analysis and development of a class of fuzzy control systems," Engineering Applications of Artificial Intelligence, vol. 13, no. 3, pp. 237-247, 2000.
 A. Karniel, G.F. Inbar, "Human motor control: learning to control a time-varying, nonlinear, many-to-one system," IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 30, no. 1, pp. 1-11, 2000.
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 42]
 J. Naik, P. Satapathy, P. K. Dash. "Short-term wind speed and wind power prediction using hybrid empirical mode decomposition and kernel ridge regression," Applied Soft Computing, pp.ASOC-4606, Dec.2017.
[CrossRef] [Web of Science Times Cited 102] [SCOPUS Times Cited 114]
 C. Zhang, H.K. Wei, J. S. Zhao, T. H. Liu, T. T. Zhu, K. J. Zhang. "Short-term wind speed forecasting using empirical mode decomposition and feature selection," Renewable Energy, vol.96, pp.727-737, May 2016.
[CrossRef] [Web of Science Times Cited 124] [SCOPUS Times Cited 144]
 W. Y. Duan, Y. Han, L. M. Huang, B. B. Zhao, M. H. Wang. "A hybrid EMD-SVR model for the short-term prediction of significant wave height," Ocean Engineering, vol. 124, pp. 54-73, Sep.2016.
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 77]
 H. Liu, H.Q. Tian, Y.F. Li. "An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system," Journal of Wind Engineering and Industrial Aerodynamics, vol. 141, pp. 27-38, Mar.2015.
 X. B. Kong, X. J. Liu, R. F. Shi, K. Y.Lee. "Wind speed prediction using reduced support vector machines with feature selection," Neurocomputing, vol.169, 449-456, Apr.2015.
[CrossRef] [Web of Science Times Cited 121] [SCOPUS Times Cited 137]
 A. Baghban, M. N. Kardani, S. Habibzadeh. "Prediction viscosity of ionic liquids using a hybrid LSSVM and group contribution method," Journal of Molecular liquids, vol. 236, pp.452-464, April 2017.
[CrossRef] [Web of Science Times Cited 50] [SCOPUS Times Cited 55]
 R. Langone, C. Alzate, B.D. Ketelaere, Jonas Vlasselaer, Wannes Meert. "LS-SVM based spectral clustering and regression for predicting maintenance of industrial machines," Engineering Applications of Artificial Intelligence, vol.37, pp.268-278, Jan. 2015.
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 75]
 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]
 A. Glowacz, W. Glowacz, Z. Glowacz, J. Kozik. "Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals," Measurement, vol.113, pp.1-9, Jan.2018.
[CrossRef] [Web of Science Times Cited 235] [SCOPUS Times Cited 271]
 Sotavento Galicia [DB/OL]. [2014-11-30]. http://www.sotaventogalicia.com/en
 Y. G. Zhang, J. Y. Yang, K. C. Wang, Z. P. Wang, Y. D. Wang, "Improved wind prediction based on the Lorenz system," Renewable Energy, vol. 81, pp. 219-226, Mar. 2015.
[CrossRef] [Web of Science Times Cited 18] [SCOPUS Times Cited 22]
Web of Science® Citations for all references: 1,637 TCR
SCOPUS® Citations for all references: 1,940 TCR
Web of Science® Average Citations per reference: 55 ACR
SCOPUS® Average Citations per reference: 65 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 2023-03-20 07:22 in 132 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.