Earticle

초록

영어

Wind power energy is receiving attention in recent years. The properties of wind are very hard to predict because of its heavy nonlinear characteristics. This paper predicts the wind speed by ANFIS and FCM clustering. The data were measured in the region of islands in Jeonnam Shinan. One year and 10 minute interval makes 52,560 samples of data but use 48,240 samples instead for stable operation. For prediction of wind speed, the covariance was examined. As a result, the input domain consists of lunar date and wind direction. This input domain has so big range of wind direction and lunar date. Therefore the whole range is partitioned by clusters. For experiments, two type are chosen. one is 4 clusters and the other is 6 clusters. . The error of cluster-6 is 7.5 % lower than cluster-4. This means that the prediction of cluster-6 is more accurate than cluster-4. With four Gaussian bell membership functions, ANFIS is trained over 200 epochs by clustered data. After training, ANFIS could predict the wind speed by lunar date and wind direction. Even if heavy nonlinear system can be predicted by ANFIS and FCM clustering.

목차

Abstract
 1. Introduction
 2. Wind Data
 3. FCM Clustering
 4. ANFIS
  4.1. Cluster-4
  4.2. Cluster-6
 5. Conclusion
 References

키워드

저자

• Young Hoon Ko [ Hyupsung University, Department of Computer Engineering ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605