A novel intelligent decision support tool for average wind speed clustering

Colak I., Kabalci E., Yesilbudak M., KAHRAMAN H. T.

8th International Conference on Power Electronics - ECCE Asia: "Green World with Power Electronics", ICPE 2011-ECCE Asia, Jeju, South Korea, 30 May - 03 June 2011, pp.2010-2014 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icpe.2011.5944482
  • City: Jeju
  • Country: South Korea
  • Page Numbers: pp.2010-2014
  • Keywords: hierarchical clustering, Renewable energy, wind energy, wind speed
  • Karadeniz Technical University Affiliated: Yes

Abstract

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. © 2011 IEEE.