The Journal of Korean Institute of Communications and Information Sciences (한국통신학회논문지)

The Korean Institute of Commucations and Information Sciences (한국통신학회)
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Forecasting of Short-term Wind Power Generation Based on SVR Using Characteristics of Wind Direction and Wind Speed

풍향과 풍속의 특징을 이용한 SVR기반 단기풍력발전량 예측

  • 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)
  • Received : 2017.01.31
  • Accepted : 2017.04.25
  • Published : 2017.05.31
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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.

본 논문은 풍력발전예측의 정확도 개선을 위하여 바람의 특성을 반영한 풍력발전량예측 방법을 제안한다. 제안한 방법은 크게 바람의 특성을 추출하는 부분과 발전량을 예측하는 부분으로 구성된다. 바람의 특성을 추출하는 부분은 발전량, 풍향과 풍속의 상관분석을 이용한다. 풍향과 풍속의 상관관계를 근거로 K-means 방법으로 클러스터링하여 특징 벡터를 추출한다. 예측하는 부분은 임의의 실수값을 예측 할 수 있도록 SVM을 일반화 한 SVR을 이용하여 기계학습을 한다. 기계학습은 바람의 특성을 반영한 제안한 방법과 바람의 특성을 반영하지 않은 기존방법을 비교 실험하였다. 또한, 제안한 방법의 정확도와 타당성을 검증하기 위하여 장소가 상이한 제주도 풍력발전단지 3지역에서 수집된 데이터를 사용하였다. 실험결과, 제안한 방법의 오차가 일반적인 풍력발전예측 오차보다 개선되었다.

Keywords

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