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JRM Vol.28 No.5 pp. 681-686
doi: 10.20965/jrm.2016.p0681
(2016)

Paper:

Method Evaluation for Short-Term Wind Speed Prediction Considering Multi Regions in Japan

Ikki Tanaka and Hiromitsu Ohmori

School of Integrated Design Engineering, Graduate School of Science and Technology, Keio University
3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

Received:
February 20, 2016
Accepted:
May 13, 2016
Published:
October 20, 2016
Keywords:
wind speed prediction, ARMA, GARCH, neural network, support vector regression
Abstract

Method Evaluation for Short-Term Wind Speed Prediction Considering Multi Regions in Japan

Prediction errors at observation points

Wind energy use is being developed worldwide. Improving wind speed forecasting techniques has become important due to their economic impact on power system operation with increasing wind power penetration. Wind speed prediction is generally difficult due to wind’s intermittent nature, so many approaches have been proposed by researchers. The viability of these techniques has been verified, however, in only a certain few areas, rather than being evaluated quantitatively in many different locations. We use data from different parts of Japan for one-step-ahead prediction and applied different approaches at each point, which was then evaluated such as mean absolute error. We used the persistent model, the ARMA-GARCH model, the nonlinear autoregressive network with external input (NARX), the recurrent neural network (RNN), and support vector regression (SVR). Our results suggest that it is difficult to create the same model which minimizes error in all areas, confirming the need for individual predictors for individual regions.

Cite this article as:
I. Tanaka and H. Ohmori, “Method Evaluation for Short-Term Wind Speed Prediction Considering Multi Regions in Japan,” J. Robot. Mechatron., Vol.28, No.5, pp. 681-686, 2016.
Data files:
References
  1. [1] N. Cheggaga, “Improvements in wind speed forecasting using an online learning,” 2014 5th Int. Renewable Energy Congress (IREC), pp. 1-6, 2014.
  2. [2] D. Sharma and T. T. Lie, “Wind speed forecasting using hybrid ANN-Kalman filter techniques,” 2012 Int. Power and Energy Conf. (IPEC), pp. 644-648, 2012.
  3. [3] H. Babazadeh et al., “An hour ahead wind speed prediction by Kalman filter,” 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA), pp. 1-6, 2012.
  4. [4] B. P. Sangita and M. S. R. Deshmukh, “Use of support vector machine for wind speed prediction,” 2011 Int. Conf. on Power and Energy Systems (ICPS), pp. 1-8, 2011.
  5. [5] R. G. Kavasseri and K. Seetharaman, “Day-ahead wind speed forecasting using f-ARIMA models,” Renewable Energy, Vol.34, No.5, pp. 1388-1393, 2009.
  6. [6] Y.-K. Wu and J.-S. Hong, “A literature review of wind forecasting technology in the world,” 2007 IEEE Lausanne Power Tech, pp. 504-509, 2007.
  7. [7] M. Lei et al., “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, Vol.13, No.4, pp. 915-920, 2009.
  8. [8] H. Chen et al., “GARCH in mean type models for wind power forecasting,” 2013 IEEE Power & Energy Society General Meeting (PES), pp. 1-5, 2013.
  9. [9] H. B. Azad, S. Mekhilef, and V. G. Ganapathy, “Long-term wind speed forecasting and general pattern recognition using neural networks,” IEEE Trans. on Sustainable Energy, Vol.5, No.2, pp. 546-553, 2014.
  10. [10] R. L. Welch, S. M. Ruffing, and G. K. Venayagamoorthy, “Comparison of feedforward and feedback neural network architectures for short term wind speed prediction,” 2009 Int. Joint Conf. on Neural Networks, pp. 3335-3340, 2009.
  11. [11] H. Zhonghe, Z. Xiaoxun, and Y. Xiaojing, “Training Method of support vector regression based on multi-dimensional feature and research on forecast model of vibration time series,” 2010 Int. Conf. on Intelligent Computation Technology and Automation, Vol.3, pp. 1087-1090, 2010.
  12. [12] M. Sugimoto and K. Kurashige, “A Study of Effective Prediction Methods of the State-action Pair for Robot Control using Online SVR,” J. of Robotics and Mechatronics, Vol.27, No.5, pp. 469-479, 2015.
  13. [13] Y. Wang et al., “Short-term wind speed prediction using support vector regression,” 2010 IEEE Power & Energy Society General Meeting, pp. 1-6, 2010.
  14. [14] S. Oke et al., “Any-place forecasting method of nationwide time-series wind speed using classified forecast models based on wind conditions,” IEEJ Trans. on Power & Energy, Vol.129, pp. 598-604, 2009.
  15. [15] J. D. Hamilton, “Time series analysis,” Vol.2, Princeton, Princeton University Press, 1994.
  16. [16] A. Lojowska et al., “Advantages of ARMA-GARCH wind speed time series modeling,” 2010 IEEE 11th Int. Conf. on Probabilistic Methods Applied to Power Systems, pp. 83-88, 2010.
  17. [17] V. Vapnik, S. E. Golowich, and A. Smola, “Support vector method for function approximation, regression estimation, and signal processing,” Advances in Neural Information Processing Systems, Vol.9, 1996.
  18. [18] V. Vapnik, “The nature of statistical learning theory,” Springer Science & Business Media, 2013.
  19. [19] C. M. Bishop, “Pattern Recognition,” Machine Learning, 2006.
  20. [20] A. G. Abo-Khalil and D.-C. Lee, “MPPT control of wind generation systems based on estimated wind speed using SVR,” IEEE Trans. on Industrial Electronics, Vol.55, No.3, pp. 1489-1490, 2008.
  21. [21] S. C. Johnson, “Hierarchical clustering schemes,” Psychometrika, Vol.32, No.3, pp. 241-254, 1967.

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Last updated on Nov. 12, 2018