International Journal of Fuzzy Logic and Intelligent Systems

  • 제11권4호
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  • Pages.247-253
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  • 2011
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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A Short-Term Wind Speed Forecasting Through Support Vector Regression Regularized by Particle Swarm Optimization

  • 투고 : 2011.09.04
  • 심사 : 2011.11.07
  • 발행 : 2011.12.25
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초록

A sustainability of electricity supply has emerged as a critical issue for low carbon green growth in South Korea. Wind power is the fastest growing source of renewable energy. However, due to its own intermittency and volatility, the power supply generated from wind energy has variability in nature. Hence, accurate forecasting of wind speed and power plays a key role in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. This paper presents a short-term wind speed prediction method based on support vector regression. Moreover, particle swarm optimization is adopted to find an optimum setting of hyper-parameters in support vector regression. An illustration is given by real-world data and the effect of model regularization by particle swarm optimization is discussed as well.

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참고문헌

  1. J. Catalao, H. Pousinho and V. M. F. Mendes, "Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal," IEEE Transactions on Sustainable Energy, vol. 2, pp. 50-59, 2011.
  2. J. Zhou, J. Shi and G. Li, "Fine tuning support vector machines for short-term wind speed forecasting," Energy Conversion and Management, vol. 52, pp. 1990-1998, 2011. https://doi.org/10.1016/j.enconman.2010.11.007
  3. H. Liu, H. Q. Tian, C. Chen and Y. F. Li, "A hybrid statistical method to predict wind speed and wind power," Renewable Energy, vol. 35, pp. 1857-1861, 2010. https://doi.org/10.1016/j.renene.2009.12.011
  4. M. C. Mabel and E. Fernandez, "Analysis of wind power generation and prediction using ANN: A case study," Renewable Energy, vol. 33, pp. 986-992, 2008. https://doi.org/10.1016/j.renene.2007.06.013
  5. M. Monfared, H. Rastegar and H. M. Kojabadi, "A new strategy for wind speed forecasting using artificial intelligent methods," Renewable Energy, vol. 34, pp. 845-848, 2009. https://doi.org/10.1016/j.renene.2008.04.017
  6. M. A. Mohandes, T. O. Halawani, S. Rehman and A. A. Hussain, "Support vector machines for wind speed prediction," Renewable Energy, vol. 29, pp. 939-947, 2004. https://doi.org/10.1016/j.renene.2003.11.009
  7. S. Salcedo-Sanz, E. G. Ortiz-Garcia, A. M. Perez-Bellido, A. Portilla-Figueras, and L. Prieto, "Short term wind speed prediction based on evolutionary support vector regression algorithms," Expert Systems with Applications, vol. 38, pp. 4052-4057, 2011. https://doi.org/10.1016/j.eswa.2010.09.067
  8. D. S. Moon and S. H. Kim, "A study on wind speed estimation and maximum power point tracking scheme for wind turbine system," Journal of Korean Institute of Intelligent Systems, vol. 20, pp. 852-857, 2010. https://doi.org/10.5391/JKIIS.2010.20.6.852
  9. H. Song, R. D. Dosano, and B. Lee, "Power system voltage stability classification using interior point method based support vector machine," International Journal of Fuzzy Logic and Intelligent Systems, vol. 9, pp. 238-243, 2009. https://doi.org/10.5391/IJFIS.2009.9.3.238
  10. J. Kennedy and R. Eberhart, "Particle swarm optimization," Proceedings of IEEE Conference on Neural Networks, vol. 4, pp. 1942-1948, 1995.
  11. V. N. Vapnik, Statistical Learning Theory, Wiley, New York, 1990.
  12. F. J. Martinez-de-Pison, C. Barreto, A. Pernia and F. Alba, "Modelling of an elastomer profile extrusion process using support vector machines," Journal of Materials Processing Technology, vol. 197, pp. 161-169, 2008. https://doi.org/10.1016/j.jmatprotec.2007.06.025
  13. C. C. Chang, and C. J. Lin, "LIBSVM: a library for support vector machines," ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 1-27, 2011. Software available at http://www.csie.ntu.edu.tw/-cjlin/libsvm