The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Evolutionary Nonlinear Compensation and Support Vector Machine Based Prediction of Windstorm Advisory

진화적 비선형 보정 및 SVM 분류에 의한 강풍 특보 예측 기법

  • Seo, Kisung (Department of Electronics Engineering, Seokyeong University)
  • Received : 2017.11.13
  • Accepted : 2017.11.16
  • Published : 2017.12.01
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Abstract

This paper introduces the prediction methods of windstorm advisory using GP nonlinear compensation and SVM. The existing special report prediction is not specialized for strong wind, such as windstorm, because it is based on the wide range of predicted values for wind speed from low to high. In order to improve the performance of strong wind reporting prediction, a method that can efficiently classify boundaries of strong wind is necessary. First, evolutionary nonlinear regression based compensation technique is applied to obtain more accurate values of prediction for wind speed using UM data. Based on the prediction wind speed, the windstorm advisory is determined. Second, SVM method is applied to classify directly using the data of UM predictors and windstorm advisory. Above two methods are compared to evaluate of the performances for the windstorm data in Jeju Island in South Korea. The data of 2007-2009, 2011 year is used for training, and 2012 year is used for test.

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References

  1. Korean Meteorological Society, Introduction to Atmospheric Science, Sigma Press, 2009.
  2. Korea Meteorological Administration, http://www.kma.go.kr.
  3. C. P. Sweeney, P. Lynch, and P. Nolan, "Reducing errors of wind speed forecast by an optimal combination of post-processing methods", Meteorological Applications, Vol. 20, Iss. 1, pp. 32-40. 2013 https://doi.org/10.1002/met.294
  4. W. Zhang, J. Wang, J. Wang, Z. Zhao, M. Tian, "Shortterm wind speed forecasting based on a hybrid model", Applied Soft Computing, Vol. 13, Iss. 7, pp. 3225-3233. 2013 https://doi.org/10.1016/j.asoc.2013.02.016
  5. H. Wang, G. Wang, G. Li, J. Peng, Y. Liu, "Deep belief network based deterministic and probabilistic wind speed forecasting approach", Applied Energy Vol. 182, pp. 80-93, 2016 https://doi.org/10.1016/j.apenergy.2016.08.108
  6. Koza, J. R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, The MIT Press, 1992.
  7. B. Hyeon, K. Seo, Y. Lee, "Evolutionary Nonlinear Regression Based Compensation Technique for Shortrange Prediction of Wind Speed using Automatic Weather Station", The Transactions of the Korean Institute of Electrical Engineers, Vol. 64, No. 1, pp. 107-112, 2015 https://doi.org/10.5370/KIEE.2015.64.1.107
  8. D. Kim, and K. Seo, "Comparison of Linear and Nonlinear Regressions and Elements Analysis for Wind Speed Prediction", Journal of Korean Institute of Intelligent Systems, Vol. 25, No. 5, pp. 477-482, 2015 https://doi.org/10.5391/JKIIS.2015.25.5.477
  9. V. Vapnik, Statistical Learning Theory, Wiley, New York, USA, 1998
  10. United Kingdom Met Office's website : http://www.metoffice.gov.uk