Browse > Article
http://dx.doi.org/10.7840/kics.2017.42.5.1085

Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed  

Kim, Yeong-ju (Mokpo National University Department of Computer Engineering)
Jeong, Min-a (Mokpo National University Department of Computer Engineering)
Son, Nam-rye (Honam University Department of Information and Communication Engineering)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.42, no.5, 2017 , pp. 1085-1092 More about this Journal
Abstract
In this paper, we propose a wind forecasting method that reflects wind characteristics to improve the accuracy of wind power prediction. The proposed method consists of extracting wind characteristics and predicting power generation. The part that extracts the characteristics of the wind uses correlation analysis of power generation amount, wind direction and wind speed. Based on the correlation between the wind direction and the wind speed, the feature vector is extracted by clustering using the K-means method. In the prediction part, machine learning is performed using the SVR that generalizes the SVM so that an arbitrary real value can be predicted. Machine learning was compared with the proposed method which reflects the characteristics of wind and the conventional method which does not reflect wind characteristics. To verify the accuracy and feasibility of the proposed method, we used the data collected from three different locations of Jeju Island wind farm. Experimental results show that the error of the proposed method is better than that of general wind power generation.
Keywords
Wind Power Generation Forecasting; K-means Clustering; SVR; Extraction Feature Vector;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 M. G. Choi, H. G, Lee, and S. C. Lee, "Evil-twin detection scheme using SVM with multi-factors," J. KICS, vol. 40, no. 2, pp. 334-348, Feb. 2015.   DOI
2 A. S. Kim, H. S, Han, K. Y. Bae, and D. K. Sung, "Wind power forecasting based support vector machine for a large-scale wind farm in jeju island," in Proc. KICS Int. Conf. Commun., pp. 11-12, Kangwon, Korea, Jan. 2016.
3 A. M. Foley, P. G. Leahy, and E. J. McKeogh, "Wind power forecasting & prediction method," IEEE, 9th Int. Conf. Environ. and Electrical Eng., pp. 16-19, May 2010.
4 I. Y. Seo, B. N. Ha, S. O. Kim, W. N. Koong, D. W. Seo, and S. J. Kim, "Short term wind power prediction using wavelet transform and ARIMA," J. Energy and Power Eng., pp. 1786-1790, Jun. 2012.
5 K. Parks and Y. H. Wan, Wind energy forecasting : A collaboration of the national center for atmospheric research(NCAR) and xcel energy, NREL/SR-5500-52233, Oct. 2011.
6 Y. Y. Hong, T. H. Yu, and C. Y. Liu, "Hour-Ahead wind speed and power forecasting using empirical mode decomposition," Energies, vol. 6, no. 12, pp. 6137-6152, Jun. 2013.   DOI
7 G. Sideratos and N. Hatziargyriou, "An advanced statistical method for wind power forecasting," IEEE Trans. Power Syst., vol. 22, no. 1, pp. 258-265, Feb. 2007.   DOI
8 M. Negnevitsky and C. Potter, "Innovative short-term wind generation prediction techniques," IEEE Power Syst. Conf. and Exposition, pp. 60-65, 2006.
9 T. El-Fouly, E. El-Saadany, and M Salama, "Grey predictor for wind energy conversion systems output power prediction," IEEE Trans. Power Syst., vol. 21, no. 3, pp. 1450-1452. Aug. 2006.   DOI
10 J. Palomares-Salas, J. Rosa, J. Ramiro, J. Melgar, A. aguera, and A. Moreno, "ARIMA vs. Neural networks for wind speed forecasting," CIMSA 2009 - Int. Conf. Computational Intell. for Measurement Syst. and Appl., pp. 129-133, 2009.
11 I. Damousis, M. Alexiadis, J. Theocharis, and P. Dokopoulos, "A fuzzy model for wind speed prediction and power generation in wind parks using spatial correlation," IEEE Trans. Energy Conversion, vol. 19, no. 2, pp. 352-361, Jul. 2008.
12 K. Kim, Y. Park, J. Park, K. Ko, and J. Huh, "Feasibility study on wind power forecasting using MOS forecasting result of KMA," JKSES, vol. 30, no. 2, Feb. 2010.
13 Y. Ho. Park, K. B. Kim, S. Y. Her, Y. M. Lee, and J. C. Huh, "A study on the wind data analysis and wind speed forecasting in Jeju area," J. Korean Solar Energy Soc., vol. 30, no. 6, 2010.
14 D. H. Shin, K. K. An, S. C. Choi, and H. K. Choi, "Malicious traffic detection using K-means," J. KICS, vol. 41, no. 2, pp. 277-284, Feb. 2016.   DOI
15 Y. I. Kim, H. Y. Jo, and Y. J. Park, "A method of nu-SVR learning with a set of basis functions," KIIS, vol. 13, no. 3, pp. 316-321, Jun. 2003.
16 J. Han and M. Kamber, Data Mining Concepts and Techniques, p. 172, 2006.
17 C. G. Park, "Prediction of software development cost using support vector regression," The Korean Operations Res. and Management Sci. Soc., vol. 23, no. 2, pp. 75-91, Nov. 2006.
18 R. J. Hyndman and A. B. Koehler, "Another look at measures of forecast accuracy," Int. J. Forecasting, vol. 22, no. 4, pp. 679-688, 2006.   DOI