earticle

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.