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IT Revolutions. Third International ICST Conference, Córdoba, Spain, March 23-25, 2011, Revised Selected Papers

Research Article

Intelligent Techniques for Identification of Zones with Similar Wind Patterns

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  • @INPROCEEDINGS{10.1007/978-3-642-32304-1_2,
        author={Jos\^{e} Palomares-Salas and Agust\^{\i}n Ag\'{y}era-P\^{e}rez and Juan Gonz\^{a}lez de la Rosa},
        title={Intelligent Techniques for Identification of Zones with Similar Wind Patterns},
        proceedings={IT Revolutions. Third International ICST Conference, C\^{o}rdoba, Spain, March 23-25, 2011, Revised Selected Papers},
        proceedings_a={IT REVOLUTIONS},
        year={2012},
        month={10},
        keywords={Cluster Analysis Clustering Applications Data Mining Self-Organizing Feature Map},
        doi={10.1007/978-3-642-32304-1_2}
    }
    
  • José Palomares-Salas
    Agustín Agüera-Pérez
    Juan González de la Rosa
    Year: 2012
    Intelligent Techniques for Identification of Zones with Similar Wind Patterns
    IT REVOLUTIONS
    Springer
    DOI: 10.1007/978-3-642-32304-1_2
José Palomares-Salas,*, Agustín Agüera-Pérez,*, Juan González de la Rosa,*
    *Contact email: josecarlos.palomares@uca.es, agustin.aguera@uca.es, juanjose.delarosa@uca.es

    Abstract

    Two process to demarcate areas with analogous wind conditions have been developed in this analysis. The used techniques are based on clustering algorithms that will show us the wind directions relations for all stations placed in the studied zone. These relations will be used to build two matrixes, one for each method, allowing us working simultaneously with all relations. By permutation of elements on these matrixes it is possible to group related stations. These grouped distributions matrixes will be compared among themselves and with the wind directions correlation matrix to select the best algorithm of them.

    Keywords
    Cluster Analysis Clustering Applications Data Mining Self-Organizing Feature Map
    Published
    2012-10-08
    http://dx.doi.org/10.1007/978-3-642-32304-1_2
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