Publication
Regionalización de la variación temporal del factor de turbidez linke TL en México a partir de algoritmos de machine learning
| dc.contributor.author | Salinas-González, J.D. | |
| dc.contributor.author | García-Hernández, A. | |
| dc.contributor.author | Riveros-Rosas, D. | |
| dc.contributor.author | Moreno-Chávez, G. | |
| dc.contributor.author | González-Cabrera, A.E. | |
| dc.contributor.author | Zarzalejo, L.F. | |
| dc.date.accessioned | 2020-11-26T11:37:47Z | |
| dc.date.available | 2020-11-26T11:37:47Z | |
| dc.date.issued | 2020-11 | |
| dc.description | CIES2020 - XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar | pt_PT |
| dc.description.abstract | RESUMEN: El agrupamiento de áreas geográficas, por medio de análisis de clusters, es una tarea que permite identificar regiones de acuerdo a los comportamientos de las variables geoclimáticas. En este trabajo de investigación se agrupó al TL Linke en regiones similares de la república mexicana de acuerdo con su varianza temporal anual del año 2015. Mediante algoritmos y técnicas de Machine Learning (aprendizaje automático), como son las mezclas de gaussianos mixtos y análisis de componentes principales, se han obtenido dos mapas, de 10 y 9 regiones que permiten identificar de una manera visual dichas regiones y analizar el comportamiento del TL Linke en la república mexicana. | pt_PT |
| dc.description.abstract | ABSTRACT: The clustering of geographic zones, by cluster analysis, is a task which enables to identify geographic’s regions according to the behavior of geoclimatic’s variables. In this work, the TL Linke was grouped in similar regions in the Mexico country according to the temporal annual variance of the year 2015. Using machine learning algorithms and techniques like Gaussian mixture models and principal components analysis, we obtained two maps of 10 and 9 regions which enables identify and analyze the TL Linkes behavior on Mexico country. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Salinas-González, J.D... [et.al.] - Regionalización de la variación temporal del factor de turbidez linke TL en México a partir de algoritmos de machine learning. In: CIES2020: As Energias Renováveis na Transição Energética: Livro de Comunicações do XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar. Helder Gonçalves, Manuel Romero (Ed.). Lisboa, Portugal: LNEG, 3-5 Novembro, 2020, p. 911-918 | pt_PT |
| dc.identifier.doi | 10.34637/cies2020.1.6108 | pt_PT |
| dc.identifier.isbn | 978-989-675-076-3 | |
| dc.identifier.uri | http://hdl.handle.net/10400.9/3446 | |
| dc.language.iso | spa | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | LNEG - Laboratório Nacional de Energia e Geologia | pt_PT |
| dc.relation.publisherversion | https://doi.org/10.34637/cies2020.1.6108 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Cluster | pt_PT |
| dc.subject | Analysis | pt_PT |
| dc.subject | Machine learning | pt_PT |
| dc.subject | Turbidity | pt_PT |
| dc.title | Regionalización de la variación temporal del factor de turbidez linke TL en México a partir de algoritmos de machine learning | pt_PT |
| dc.type | conference object | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Lisboa, Portugal | pt_PT |
| oaire.citation.endPage | 918 | pt_PT |
| oaire.citation.startPage | 911 | pt_PT |
| oaire.citation.title | CIES2020 - XVII Congresso Ibérico e XIII Congresso Ibero-americano de Energia Solar | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | conferenceObject | pt_PT |
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