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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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Wind Power Prediction Based on LS-SVM Model with Error Correction

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., NI, T., CHENG, P., 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 (1,180 KB) | Citation | Downloads: 452 | Views: 309

Author keywords
computer errors, error correction, support vector machines, power engineering computing, wind energy generation

References keywords
wind(19), energy(15), prediction(11), speed(10), wang(9), power(7), renewable(6), jrenene(6), term(5), short(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-02-28
Volume 17, Issue 1, Year 2017, On page(s): 3 - 8
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.01001
Web of Science Accession Number: 000396335900001

Abstract
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As conventional energy sources are non-renewable, the world's major countries are investing heavily in renewable energy research. Wind power represents the development trend of future energy, but the intermittent and volatility of wind energy are the main reasons that leads to the poor accuracy of wind power prediction. However, by analyzing the error level at different time points, it can be found that the errors of adjacent time are often approximately the same, the least square support vector machine (LS-SVM) model with error correction is used to predict the wind power in this paper. According to the simulation of wind power data of two wind farms, the proposed method can effectively improve the prediction accuracy of wind power, and the error distribution is concentrated almost without deviation. The improved method proposed in this paper takes into account the error correction process of the model, which improved the prediction accuracy of the traditional model (RBF, Elman, LS-SVM). Compared with the single LS-SVM prediction model in this paper, the mean absolute error of the proposed method had decreased by 52 percent. The research work in this paper will be helpful to the reasonable arrangement of dispatching operation plan, the normal operation of the wind farm and the large-scale development as well as fully utilization of renewable energy resources.


References | Cited By  «-- Click to see who has cited this paper

[1] A. Y. Sultan, C. Yassine, A. B. Abdullah, G. Adel, "Nested ensemble NWP approach for wind energy assessment," Renewable Energy, Vol. 37, pp. 150-160, Jan. 2012.
[CrossRef] [Web of Science Times Cited 17] [SCOPUS Times Cited 21]


[2] V. Prema, K. U. 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 6] [SCOPUS Times Cited 14]


[3] H. R. Zhao, S. Guo, "An optimized gray modal for annual power load forecasting," Energy, Vol. 107, pp. 272-286, Jul. 2016.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 7]


[4] C. D. Zuluaga, M. A. Alvarez, E. Giraldo, "Short-term wind speed prediction based on robust Kalman filtering: An experimental comparison," Applied Energy, Vol. 156, pp. 321-330, Oct. 2015.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 14]


[5] H. P. Liu, E. Erdem, J. Shi, "Comprehensive evaluation of ARMA-GARCH (-M) approaches for modeling the mean and volatility of wind speed," Applied Energy, Vol. 88, pp. 724-732, Mar. 2011.
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 78]


[6] C. Fan, S. Liu, "Wind Speed Forecasting Method: Gray Related Weighted Combination with Revised Parameter," Energy Procedia, Vol. 5, pp. 550-554, Apr. 2011.
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 7]


[7] P. Ramasamy, S. S. Chandel, A. K. Yadav, "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Vol. 80, pp. 338-347, Aug. 2015.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 19]


[8] H. Chitsaz, N. Amjady, H. Zareipour, "Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm," Energy Conversion and Management, Vol. 89, pp. 588-598, Jan. 2015.
[CrossRef] [Web of Science Times Cited 29] [SCOPUS Times Cited 38]


[9] M. A. Mohandes, T. O. Halawani, S. Rehman, A. A. Hussain, "Support vector machines for wind speed prediction," Renewable Energy, Vol. 29, no. 6, pp. 939-947, May. 2004.
[CrossRef] [Web of Science Times Cited 269] [SCOPUS Times Cited 345]


[10] K. G. Sheela, S. N. Deepa, "Neural network based hybrid computing model for wind speed prediction," Neurocomputing, Vol. 122, pp. 425-429, Dec. 2013.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 26]


[11] K. Chen, J. Yu, "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Vol. 113, pp. 690-705, Jan. 2014.
[CrossRef] [Web of Science Times Cited 59] [SCOPUS Times Cited 68]


[12] 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, Sep. 2015.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[13] Y. G. Zhang, J. Y. Yang, K. C. Wang, Y. D. Wang. "Lorenz Wind Disturbance Model Based on Grey Generated Components." Energies, Vol. 7, no. 11, pp. 7178-7193, Nov. 2014.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 6]


[14] 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 11] [SCOPUS Times Cited 12]


[15] Z.Y. Su, J.Z. Wang, H.Y. Lu, G. Zhao, "A new hybrid model optimized by an intelligent optimization algorithm for wind speed forecasting," Energy Conversion and Management, Vol. 85, pp. 443-452, Sep. 2014.
[CrossRef] [Web of Science Times Cited 19] [SCOPUS Times Cited 21]


[16] M. Monfared, H. Rastegar, H. M. Kojabadi, "A new strategy for wind speed forecasting using artificial intelligent methods," Renewable Energy, Vol. 34, no.3, pp. 845-848, Mar. 2008.
[CrossRef] [Web of Science Times Cited 85] [SCOPUS Times Cited 120]


[17] C. Ren, N. An, J. Z. Wang, et al., "Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting," Knowledge-Based Systems, Vol. 56, pp. 226-239, Jan. 2014.
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 78]


[18] S. B. Ghosn, F. Drouby, H. M. Harmanani, "A Parallel Genetic Algorithm for the Open-Shop Scheduling Problem Using Deterministic and Random Moves," International Journal of Artificial Intelligence, Vol. 14, no. 1, pp. 130-144, 2016.

[19] A. Mellit, A. M. Pavan, M. Benghanem, "Least squares support vector machine for short-term prediction of meteorological time series," Theor Appl Climatol, Vol. 111, pp. 297-307, May. 2013.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 32]


[20] X. H. Yuan, C. Chen, Y. B. Yuan, Y. H. Huang, "Short-term wind power prediction based on LSSVM-GSA modal," Energy Conversion and Management, Vol. 101, pp. 393-401, Sep. 2015.
[CrossRef] [Web of Science Times Cited 26] [SCOPUS Times Cited 26]


[21] M. A. Ramírez-Ortegón, V. Märgner, E. Cuevas, R. Rojas, "An optimization for binarization methods by removing binary artifacts," Pattern Recognition Letters, Vol. 34, pp. 1299-1306,Aug. 2013.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 10]


[22] R. E. Precup, S. Preitl, "Optimization criteria in development of fuzzy controllers with dynamics," Engineering Applications of Artificial Intelligence, Vol. 17, pp. 661-674, Aug. 2004.
[CrossRef] [Web of Science Times Cited 36] [SCOPUS Times Cited 48]


[23] Z. Li, L. Ye, Y. N. Zhao, X. R. Song, et al., "Short-term wind power prediction based on extreme learning machine with error correction," Protection and Control of Modern Power Systems, Vol. 1, pp. 2-8, Jun. 2016.
[CrossRef]


[24] E. A. Bossanyi, "Wind Turbine Control for Load Reduction," Wind Energ, Vol. 6, pp. 229-244, Jun. 2003.
[CrossRef] [Web of Science Times Cited 151] [SCOPUS Times Cited 219]




References Weight

Web of Science® Citations for all references: 931 TCR
SCOPUS® Citations for all references: 1,212 TCR

Web of Science® Average Citations per reference: 37 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 2017-04-23 16:18 in 149 seconds.




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