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


Vol. 42,  No. 5, pp. 1085-1092, May  2017


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  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.

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  Cite this article

[IEEE Style]

Y. Kim, M. Jeong, N. Son, "Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 5, pp. 1085-1092, 2017. DOI: .

[ACM Style]

Yeong-ju Kim, Min-a Jeong, and Nam-rye Son. 2017. Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed. The Journal of Korean Institute of Communications and Information Sciences, 42, 5, (2017), 1085-1092. DOI: .

[KICS Style]

Yeong-ju Kim, Min-a Jeong, Nam-rye Son, "Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed," The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 5, pp. 1085-1092, 5. 2017.

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pISSN : 1226 - 4717
eISSN : 2287 - 3880