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Improved Wind Speed Prediction Using Empirical Mode DecompositionZHANG, Y. , ZHANG, C. , SUN, J. , GUO, J.
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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)
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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 60] [SCOPUS Times Cited 70]
 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 24] [SCOPUS Times Cited 28]
 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 21] [SCOPUS Times Cited 28]
 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 76] [SCOPUS Times Cited 85]
 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 36] [SCOPUS Times Cited 52]
 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 58] [SCOPUS Times Cited 70]
 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 22] [SCOPUS Times Cited 26]
 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 3] [SCOPUS Times Cited 4]
 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 65] [SCOPUS Times Cited 81]
 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 7] [SCOPUS Times Cited 9]
 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 3] [SCOPUS Times Cited 4]
 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 36] [SCOPUS Times Cited 40]
 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 31] [SCOPUS Times Cited 36]
 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 70] [SCOPUS Times Cited 77]
 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 27] [SCOPUS Times Cited 30]
 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 74] [SCOPUS Times Cited 82]
 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 31] [SCOPUS Times Cited 39]
 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 37] [SCOPUS Times Cited 53]
 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 25] [SCOPUS Times Cited 25]
 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 119] [SCOPUS Times Cited 129]
 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 15] [SCOPUS Times Cited 16]
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