Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm

Seyoung Kim; Jeongmin Kim; RYU， KWANG RYEL

Journal of The Korea Society of Computer and Information

Abbr : JKSCI
2015, 20(3), pp.19-27
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science

Seyoung Kim ¹, Jeongmin Kim ², RYU， KWANG RYEL ³

¹부산대학교
²부산대학교
³부산대학교

ABSTRACT

Technologies of wind power generation for development of alternative energy technology have beenaccumulated over the past 20 years. Wind power generation is environmentally friendly and economicalbecause it uses the wind blowing in nature as energy resource. In order to operate wind power generationefficiently, it is necessary to accurately predict wind speed changing every moment in nature. It isimportant 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 planbecause the difference between predicting power and real power causes economic loss. In this paper, wepropose a method of wind speed prediction using numeric prediction algorithm-based wind speed forecastmodel made to analyze the wind speed forecast given by the Meteorological Administration and patternvalue for considering seasonal property of wind speed as well as changing trend of past wind speed. Thewind speed forecast given by the Meteorological Administration is the forecast in respect to comparativelywide area including wind generation farm. But it contributes considerably to make accuracy of wind speedprediction high. Also, the experimental results demonstrate that as the rate of wind is analyzed in moredetail, the greater accuracy will be obtained.
KEYWORDS

wind speed prediction, numeric prediction algorithm, ensemble of model trees
REFERENCES (13)

[confproc] Y. K. Wu / 2007 / A literature review of wind forecasting technology in the world / Proceedings of the IEEE conference on Power Tech : 504~509

[journal] W. Y. Chang / 2014 / A literature Review of Wind Forecasting Methods / Journal of Power and Energy Engineering 2(4) : 161~168

[journal] E. Erdem / 2011 / ARMA based approaches for forecasting the tuple of wind speed and direction / Applied Energy 88(4) : 1405~1414

[report] M. Milligan / 2003 / Statistical Wind Power Forecasting models: results for U.S. Wind Farms / National Renewable Energy Laboratory

[journal] M. Monfared / 2009 / A new strategy for wind speed forecasting using artificial intelligent methods / Renewably Energy 34(3) : 845~848

[journal] Z. Guo / 2011 / A case study on a hybrid wind speed forecasting method using BP neural network / Knowledge-based systems 24(7) : 1048~1056

[journal] M. Ross / 2011 / Energy storage system scheduling for an isolated microgrid / IET Renew. Power Gener 5(2) : 117~123

[book] S. Russell / 2010 / Artificial Intelligence : A Modern Approach Third Edition / Pearson Education : 737~738

[book] C. G. Atkeson / 1997 / Lazy learning / Springer : 75~113

[confproc] J. R. Quinlan / 1992 / Learning with continuous classes / 5th Australian Joint Conference on Artificial Intelligence : 343~348

[report] Y. Wang / 1996 / Induction of model trees for predicting continuous classes / University of Waikato, Department of Computer Science

[journal] R. Kohavi / 1997 / Wrappers for feature subset selection / Artificial Intelligence 97(1) : 273~324

[journal] I. Guyon / 2003 / An Introduction to Variable and Feature Selection / The Journal of Machine Learning Research 3 : 1157~1182
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