2026-05-12T00:27:45Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/57682024-05-14T10:10:06Zcom_10259_4393com_10259_5086com_10259_2604col_10259_4394

Granados López, Diego 264 500 0000-0002-9046-7397 Suárez García, Andrés 543 500 Diez Mediavilla, Montserrat 154 500 0000-0002-4957-9530 Alonso Tristán, Cristina 23 500 0000-0003-4733-7391 2021-05-17T11:58:37Z 2021-05-17T11:58:37Z 2021-04 0038-092X http://hdl.handle.net/10259/5768 10.1016/j.solener.2021.02.039 There are several compilations of sky classifications that refer to Meteorological Indices (MIs) (variables usually recorded at meteorological ground stations), due to the scarcity of sky scanner devices that can supply the experimental data needed to apply the CIE standard sky classification. The use of one rather than another MI is never justified, because there is no standardized criterion for their selection. In this study, forty-three MIs, traditionally used to define different sky conditions, are reviewed. Feature Selection (FS) is a key step in the design of a sky-classification algorithm using MIs as an alternative to data from sky scanners. Four procedural methods for FS -Pearson, Permutation Importance, Recursive Feature Elimination, and Boruta- are applied to an extensive data set of MIs that includes CIE standard sky classification data, which was used as a reference. The use of FS procedures significatively reduced the original set of MIs, permitting the construction of different classification trees with high performance for the sky classification. In the case of the Pearson FS method, the classification tree only used two MIs. The advantage of the Pearson FS method is that it functions independently from the machine-learning algorithm used latter for the sky classification. application/pdf eng Elsevier Solar Energy. 2021, V. 218, p. 95-107 https://doi.org/10.1016/j.solener.2021.02.039 info:eu-repo/grantAgreement/JCyL/BU021G19 info:eu-repo/grantAgreement/MICINN/RTI2018-098900-B-I00 Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess CIE standard sky classification Feature selection Meteorological indices Machine learning Electrotecnia Electrical engineering Feature selection for CIE standard sky classification info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Solar Energy 218 95 107 THUMBNAIL Granados-se_2021.pdf.jpg Granados-se_2021.pdf.jpg IM Thumbnail image/jpeg 4446 https://riubu.ubu.es/bitstream/10259/5768/4/Granados-se_2021.pdf.jpg fd40c6d5cc0e246a04f70ce103228942 MD5 4 Granados-Se_2021.pdf.jpg Granados-Se_2021.pdf.jpg IM Thumbnail image/jpeg 5091 https://riubu.ubu.es/bitstream/10259/5768/6/Granados-Se_2021.pdf.jpg 62eb9392d43463edc6248e2d887fa9b7 MD5 6 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/5768/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 805 https://riubu.ubu.es/bitstream/10259/5768/2/license_rdf 4460e5956bc1d1639be9ae6146a50347 MD5 2 ORIGINAL Granados-Se_2021.pdf Granados-Se_2021.pdf application/pdf 4833164 https://riubu.ubu.es/bitstream/10259/5768/5/Granados-Se_2021.pdf 787e9c7f5e2bcd1c69f378f595a0da49 MD5 5 10259/5768 oai:riubu.ubu.es:10259/5768 2024-05-14 12:10:06.702 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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