untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2016-07-04T13:43:57Z</responseDate>
<request identifier=oai:HAL:meteo-01304572v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:meteo-01304572v1</identifier>
<datestamp>2016-05-03</datestamp>
<setSpec>type:ART</setSpec>
<setSpec>subject:phys</setSpec>
<setSpec>collection:METEO</setSpec>
<setSpec>collection:CNRS</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:UNIV-POITIERS</setSpec>
<setSpec>collection:UNILIM</setSpec>
<setSpec>collection:IRD</setSpec>
<setSpec>collection:AGROPOLIS</setSpec>
<setSpec>collection:XLIM</setSpec>
<setSpec>collection:XLIM-SRI</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Identifying statistical properties of solar radiation models by using information criteria</title>
<creator>Linguet, Laurent</creator>
<creator>Pousset, Yannis</creator>
<creator>Olivier, Christian</creator>
<contributor>Espace pour le Développement (UMR ESPACE-DEV) ; Institut de Recherche pour le Développement (IRD) - Université des Antilles et de la Guyane (UAG) - Université de la Réunion - Université de Montpellier (UM)</contributor>
<contributor>Université de Guyane</contributor>
<contributor>Systèmes et Réseaux Intelligents (XLIM-SRI) ; XLIM (XLIM) ; Université de Limoges (UNILIM) - Centre National de la Recherche Scientifique (CNRS) - Université de Limoges (UNILIM) - Centre National de la Recherche Scientifique (CNRS)</contributor>
<contributor>Université de Poitiers</contributor>
<contributor>FEDER Europe</contributor>
<description>International audience</description>
<source>ISSN: 0038-092X</source>
<source>Solar Energy</source>
<publisher>Elsevier</publisher>
<identifier>meteo-01304572</identifier>
<identifier>https://hal-meteofrance.archives-ouvertes.fr/meteo-01304572</identifier>
<identifier>https://hal-meteofrance.archives-ouvertes.fr/meteo-01304572/document</identifier>
<identifier>https://hal-meteofrance.archives-ouvertes.fr/meteo-01304572/file/Manuscript_Hal.pdf</identifier>
<source>https://hal-meteofrance.archives-ouvertes.fr/meteo-01304572</source>
<source>Solar Energy, Elsevier, 2016, 132, pp.236-246. <http://www.sciencedirect.com/science/journal/0038092X/132/supp/C>. <10.1016/j.solener.2016.02.038 ></source>
<identifier>DOI : 10.1016/j.solener.2016.02.038 </identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.solener.2016.02.038 </relation>
<source>http://www.sciencedirect.com/science/journal/0038092X/132/supp/C</source>
<language>en</language>
<subject lang=en>model selection </subject>
<subject lang=en>solar radiation model</subject>
<subject lang=en>solar radiation</subject>
<subject lang=en>information criteria</subject>
<subject>[PHYS] Physics [physics]</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>The purpose of this article is to improve modeling of solar irradiance through the analysis of measurement data on the ground in the intertropical zone. For this, we identify, using information criteria, the probabilistic distributions introduced in two models of synthetic solar irradiance generation. We then validate the results by using the KL divergence and KSI parameter as comparison criteria between distributions arising from real and synthesized data. Our study confirms, for example, that the Gaussian classical distribution is not suitable for modeling solar irradiance, and we propose other more suitable statistical laws instead. The value of the identification procedure of the distribution laws presented in this article is that it ensures the production of solar irradiance data comparable in their statistical content to the measured data. Another advantage is that this procedure contributes to highlighting the time invariance of distribution laws representing the random terms. We conclude that this new information-criteria-based method permits the identification of the probability laws that best describe the statistical distributions introduced in models of synthetic solar irradiance generation.</description>
<date>2016</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
