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A novel intelligent decision support tool for average wind speed clustering
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 techn...
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| container_end_page | 2014 |
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| creator | Colak, I. Kabalci, E. Yesilbudak, M. Kahraman, H. T. |
| description | 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. |
| doi_str_mv | 10.1109/ICPE.2011.5944482 |
| format | conference_proceeding |
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The achieved clustering results based on Euclidean distance metric provide the optimum inferences to expert according to other distance metrics.</description><subject>Algorithm design and analysis</subject><subject>Clustering algorithms</subject><subject>Clustering methods</subject><subject>Data mining</subject><subject>Euclidean distance</subject><subject>hierarchical clustering</subject><subject>Renewable energy</subject><subject>wind energy</subject><subject>Wind speed</subject><issn>2150-6078</issn><isbn>9781612849584</isbn><isbn>161284958X</isbn><isbn>9781612849577</isbn><isbn>1612849563</isbn><isbn>9781612849560</isbn><isbn>1612849571</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpVkNFKwzAYhSMqOGYfQLzJC7T-SZMmuRxl6nCgF7sfafJ3RGpSmm7i2ztwN56bw3dz-DiEPDCoGAPztGk_1hUHxipphBCaX5HCKM0axrUwUqnrf6zFDVlwJqFsQOk7UuT8Cec0jZHAFuRtRWM64UBDnHEYwgHjTD26kEOKNB_HMU0znVMaaJ8mak842QPS7xA9zSOip2445hmnEA_35La3Q8bi0kuye17v2tdy-_6yaVfbMhiYy5r3wC1zsnccuPBQo4NeOdt7xTvZKdRCOQnOddB5IfVZVTvjLboOtVT1kjz-zQZE3I9T-LLTz_7yRv0Lv21SKw</recordid><startdate>201105</startdate><enddate>201105</enddate><creator>Colak, I.</creator><creator>Kabalci, E.</creator><creator>Yesilbudak, M.</creator><creator>Kahraman, H. 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T.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Colak, I.</au><au>Kabalci, E.</au><au>Yesilbudak, M.</au><au>Kahraman, H. T.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A novel intelligent decision support tool for average wind speed clustering</atitle><btitle>8th International Conference on Power Electronics - ECCE Asia</btitle><stitle>ICPE</stitle><date>2011-05</date><risdate>2011</risdate><spage>2010</spage><epage>2014</epage><pages>2010-2014</pages><issn>2150-6078</issn><isbn>9781612849584</isbn><isbn>161284958X</isbn><eisbn>9781612849577</eisbn><eisbn>1612849563</eisbn><eisbn>9781612849560</eisbn><eisbn>1612849571</eisbn><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.</abstract><pub>IEEE</pub><doi>10.1109/ICPE.2011.5944482</doi><tpages>5</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 2150-6078 |
| ispartof | 8th International Conference on Power Electronics - ECCE Asia, 2011, p.2010-2014 |
| issn | 2150-6078 |
| language | eng |
| recordid | cdi_ieee_primary_5944482 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Algorithm design and analysis Clustering algorithms Clustering methods Data mining Euclidean distance hierarchical clustering Renewable energy wind energy Wind speed |
| title | A novel intelligent decision support tool for average wind speed clustering |
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