http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
A Novel Intelligent Decision Support Tool for Average Wind Speed Clustering
I. Colak,E. Kabalci,M. Yesilbudak,H.T. Kahraman 전력전자학회 2011 ICPE(ISPE)논문집 Vol.2011 No.5
The utilization ratio of wind energy, which is one of the renewable energy sources, is increased around 25% since last 15 years. However, the parameters such as performance of wind turbines and climate features are not analyzed adequately. At the analysis stage of these parameters, data mining techniques are required to be used. In this study, the agglomerative hierarchical clustering method which is one of the data mining techniques is used to analyze the provinces located in the Central Anatolia Region of Turkey in terms of average wind speed. Nearest neighbor algorithm is used as the clustering algorithm. Euclidean, Manhattan and Minkowski distance metrics are used determine the optimum hierarchical clustering results in this algorithm. The achieved clustering results based on Euclidean distance metric provide the optimum inferences to expert according to other distance metrics.