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dc.contributor.authorGonzález Manteiga, Wenceslao
dc.contributor.authorFebrero Bande, Manuel
dc.contributor.authorPiñeiro Lamas, María
dc.date.accessioned2020-05-08T09:39:30Z
dc.date.available2020-05-08T09:39:30Z
dc.date.issued2018
dc.identifier.citationGonzález-Manteiga, W., Febrero-Bande, M. and Piñeiro-Lamas, M. Semiparametric prediction models for variables related with energy production. J.Math.Industry 8, 7 (2018). https://doi.org/10.1186/s13362-018-0049-0
dc.identifier.issn2190-5983
dc.identifier.urihttp://hdl.handle.net/10347/22129
dc.description.abstractIn this paper a review of semiparametric models developed throughout the years thanks to an extensive collaboration between the Department of Statistics and Operations Research of the University of Santiago de Compostela and a power station located in As Pontes (A Coruña, Spain) property of Endesa Generation, SA, is shown. In particular these models were used to predict the levels of sulphur dioxide in the environment of this power station with half an hour in advance. In this paper also a new multidimensional semiparametric model is considered. This model is a generalization of the previous models and takes into account the correlation structure of errors. Its behaviour is illustrated in a simulation study and with the prediction of the levels of two important pollution indicators in the environment of the power station: sulphur dioxide and nitrogen oxides.
dc.description.sponsorshipThe work by Wenceslao González-Manteiga and Manuel Febrero-Bande was partially supported by projects MTM2013-41383-P and MTM2016-76969-P from the Spanish Ministry of Science and Innovation and European Regional Development Fund and IAP network StUDyS from Belgian Science Policy.
dc.language.isoeng
dc.publisherSpringerOpen
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/MTM2016-76969-P/ES
dc.rights© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectSemiparametric prediction models
dc.subjectPollution indicators
dc.subjectCointegration
dc.titleSemiparametric prediction models for variables related with energy production
dc.typeinfo:eu-repo/semantics/article
dc.identifier.DOI10.1186/s13362-018-0049-0
dc.relation.publisherversionhttps://doi.org/10.1186/s13362-018-0049-0
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.contributor.affiliationUniversidade de Santiago de Compostela. Departamento de Estatística, Análise Matemática e Optimización
dc.description.peerreviewedSI


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© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and
indicate if changes were made
Except where otherwise noted, this item's license is described as  © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made





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