Browse > Article
http://dx.doi.org/10.9708/jksci.2015.20.3.019

Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm  

Kim, Se-Young (Dept. of Computer Engineering, Pusan National University)
Kim, Jeong-Min (Dept. of Computer Engineering, Pusan National University)
Ryu, Kwang-Ryel (Dept. of Computer Engineering, Pusan National University)
Publication Information
Journal of the Korea Society of Computer and Information / v.20, no.3, 2015 , pp. 19-27 More about this Journal
Abstract
Technologies of wind power generation for development of alternative energy technology have been accumulated over the past 20 years. Wind power generation is environmentally friendly and economical because it uses the wind blowing in nature as energy resource. In order to operate wind power generation efficiently, it is necessary to accurately predict wind speed changing every moment in nature. It is important not only averagely how well to predict wind speed but also to minimize the largest absolute error between real value and prediction value of wind speed. In terms of generation operating plan, minimizing the largest absolute error plays an important role for building flexible generation operating plan because the difference between predicting power and real power causes economic loss. In this paper, we propose a method of wind speed prediction using numeric prediction algorithm-based wind speed forecast model made to analyze the wind speed forecast given by the Meteorological Administration and pattern value for considering seasonal property of wind speed as well as changing trend of past wind speed. The wind speed forecast given by the Meteorological Administration is the forecast in respect to comparatively wide area including wind generation farm. But it contributes considerably to make accuracy of wind speed prediction high. Also, the experimental results demonstrate that as the rate of wind is analyzed in more detail, the greater accuracy will be obtained.
Keywords
wind speed prediction; numeric prediction algorithm; ensemble of model trees;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Y. K. Wu, and J. S. Hong, "A literature review of wind forecasting technology in the world," Proceedings of the IEEE conference on Power Tech, Lausanne, pp. 504-509, July. 2007.
2 W. Y. Chang, "A literature Review of Wind Forecasting Methods," Journal of Power and Energy Engineering, vol. 2, no. 4, pp. 161-168, April. 2014.   DOI
3 E. Erdem, and J. Shi, "ARMA based approaches for forecasting the tuple of wind speed and direction," Applied Energy, vol. 88, no. 4, pp. 1405-1414, April. 2011.   DOI   ScienceOn
4 M. Milligan, M. Schwartz, and Y. Wan, "Statistical Wind Power Forecasting models: results for U.S. Wind Farms", National Renewable Energy Laboratory, Golden, Colorado. May. 2003, NREL/CP-500-33956 (Preprint)
5 M. Monfared, H. Rastegar, and H. M. Kojabadi, "A new strategy for wind speed forecasting using artificial intelligent methods," Renewably Energy, vol. 34, no. 3, pp. 845-848, Mar. 2009   DOI   ScienceOn
6 Z. Guo, J. Wu, H. Lu, and J. Wang, "A case study on a hybrid wind speed forecasting method using BP neural network," Knowledge-based systems, vol. 24, no. 7, pp. 1048-1056, Oct. 2011.
7 M. Ross, R. Hidalgo, C. Abbey, and G. Joos, "Energy storage system scheduling for an isolated microgrid," IET Renew. Power Gener., vol. 5, no. 2, pp. 117-123, Mar. 2011   DOI   ScienceOn
8 S. Russell, and P. Norvig, "Artificial Intelligence : A Modern Approach Third Edition," Pearson Education, pp737-738, 2010.
9 C. G. Atkeson, A. W. Moore, and S. Schaal, "Locally weighted learning for control," Lazy learning. Springer Netherlands, pp. 75-113, 1997.
10 J. R. Quinlan, "Learning with continuous classes," 5th Australian Joint Conference on Artificial Intelligence, pp. 343-348, 1992.
11 Y. Wang, and I. H. Witten, "Induction of model trees for predicting continuous classes," Working paper 96/23, Hamilton, New Zealand: University of Waikato, Department of Computer Science, 1996.
12 R. Kohavi, and G. H. John, "Wrappers for feature subset selection," Artificial Intelligence, vol. 97, no. 1-2, pp. 273-324, Dec. 1997.   DOI   ScienceOn
13 I. Guyon, and A. Elisseeff, "An Introduction to Variable and Feature Selection," The Journal of Machine Learning Research vol. 3, pp. 1157-1182, Jan. 2003