Photovoltaic generation model as a function of weather variables using artificial intelligence techniques

Autores
Sánchez Reinoso, Carlos Roberto; Cutrera, M.; Battioni, M.; Milone, Diego Humberto; Buitrago, R. H.
Año de publicación
2012
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The optimisation of photovoltaic systems of electricity generation involve the necesity of real data of the different variables as well as determination of their relationships. In the field of photovoltaic solar energy there is interest to predict the energy generation in terms of solar radiation and climatic parameters. For this purpose, it is needed a good sensing and measurement of these parameters. In this paper, we propose a method based on artificial intelligence techniques for obtaining the generated energy under climatic conditions during a year. In addition, we propose a model that relates short-circuit current with radiation, considering the true nonlinear behavior of the relationship between variables. The results of the proposed method using real data show its validity and usefulness in predicting the generated energy by photovoltaic modules and the search for alternative methods of measuring global radiation at low cost and reasonable error.
Fil: Sánchez Reinoso, Carlos Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Cutrera, M.. Universidad Nacional de Catamarca; Argentina
Fil: Battioni, M.. Universidad Nacional de Catamarca; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Buitrago, R. H.. Universidad Nacional de Catamarca; Argentina
Materia
ARTIFICIAL INTELLIGENCE
GENERATION PREDICTION
MEASUREMENTS
PHOTOVOLTAIC ENERGY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/196334
Acceder
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Photovoltaic generation model as a function of weather variables using artificial intelligence techniquesSánchez Reinoso, Carlos RobertoCutrera, M.Battioni, M.Milone, Diego HumbertoBuitrago, R. H.ARTIFICIAL INTELLIGENCEGENERATION PREDICTIONMEASUREMENTSPHOTOVOLTAIC ENERGYhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The optimisation of photovoltaic systems of electricity generation involve the necesity of real data of the different variables as well as determination of their relationships. In the field of photovoltaic solar energy there is interest to predict the energy generation in terms of solar radiation and climatic parameters. For this purpose, it is needed a good sensing and measurement of these parameters. In this paper, we propose a method based on artificial intelligence techniques for obtaining the generated energy under climatic conditions during a year. In addition, we propose a model that relates short-circuit current with radiation, considering the true nonlinear behavior of the relationship between variables. The results of the proposed method using real data show its validity and usefulness in predicting the generated energy by photovoltaic modules and the search for alternative methods of measuring global radiation at low cost and reasonable error.Fil: Sánchez Reinoso, Carlos Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Cutrera, M.. Universidad Nacional de Catamarca; ArgentinaFil: Battioni, M.. Universidad Nacional de Catamarca; ArgentinaFil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Buitrago, R. H.. Universidad Nacional de Catamarca; ArgentinaPergamon-Elsevier Science Ltd2012-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/196334Sánchez Reinoso, Carlos Roberto; Cutrera, M.; Battioni, M.; Milone, Diego Humberto; Buitrago, R. H.; Photovoltaic generation model as a function of weather variables using artificial intelligence techniques; Pergamon-Elsevier Science Ltd; International Journal of Hydrogen Energy; 37; 19; 10-2012; 14781-147850360-3199CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0360319911027741info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijhydene.2011.12.081info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:45:24Zoai:ri.conicet.gov.ar:11336/196334instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:45:25.278CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
title Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
spellingShingle Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
Sánchez Reinoso, Carlos Roberto
ARTIFICIAL INTELLIGENCE
GENERATION PREDICTION
MEASUREMENTS
PHOTOVOLTAIC ENERGY
title_short Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
title_full Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
title_fullStr Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
title_full_unstemmed Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
title_sort Photovoltaic generation model as a function of weather variables using artificial intelligence techniques
dc.creator.none.fl_str_mv Sánchez Reinoso, Carlos Roberto
Cutrera, M.
Battioni, M.
Milone, Diego Humberto
Buitrago, R. H.
author Sánchez Reinoso, Carlos Roberto
author_facet Sánchez Reinoso, Carlos Roberto
Cutrera, M.
Battioni, M.
Milone, Diego Humberto
Buitrago, R. H.
author_role author
author2 Cutrera, M.
Battioni, M.
Milone, Diego Humberto
Buitrago, R. H.
author2_role author
author
author
author
dc.subject.none.fl_str_mv ARTIFICIAL INTELLIGENCE
GENERATION PREDICTION
MEASUREMENTS
PHOTOVOLTAIC ENERGY
topic ARTIFICIAL INTELLIGENCE
GENERATION PREDICTION
MEASUREMENTS
PHOTOVOLTAIC ENERGY
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The optimisation of photovoltaic systems of electricity generation involve the necesity of real data of the different variables as well as determination of their relationships. In the field of photovoltaic solar energy there is interest to predict the energy generation in terms of solar radiation and climatic parameters. For this purpose, it is needed a good sensing and measurement of these parameters. In this paper, we propose a method based on artificial intelligence techniques for obtaining the generated energy under climatic conditions during a year. In addition, we propose a model that relates short-circuit current with radiation, considering the true nonlinear behavior of the relationship between variables. The results of the proposed method using real data show its validity and usefulness in predicting the generated energy by photovoltaic modules and the search for alternative methods of measuring global radiation at low cost and reasonable error.
Fil: Sánchez Reinoso, Carlos Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Cutrera, M.. Universidad Nacional de Catamarca; Argentina
Fil: Battioni, M.. Universidad Nacional de Catamarca; Argentina
Fil: Milone, Diego Humberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; Argentina
Fil: Buitrago, R. H.. Universidad Nacional de Catamarca; Argentina
description The optimisation of photovoltaic systems of electricity generation involve the necesity of real data of the different variables as well as determination of their relationships. In the field of photovoltaic solar energy there is interest to predict the energy generation in terms of solar radiation and climatic parameters. For this purpose, it is needed a good sensing and measurement of these parameters. In this paper, we propose a method based on artificial intelligence techniques for obtaining the generated energy under climatic conditions during a year. In addition, we propose a model that relates short-circuit current with radiation, considering the true nonlinear behavior of the relationship between variables. The results of the proposed method using real data show its validity and usefulness in predicting the generated energy by photovoltaic modules and the search for alternative methods of measuring global radiation at low cost and reasonable error.
publishDate 2012
dc.date.none.fl_str_mv 2012-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/196334
Sánchez Reinoso, Carlos Roberto; Cutrera, M.; Battioni, M.; Milone, Diego Humberto; Buitrago, R. H.; Photovoltaic generation model as a function of weather variables using artificial intelligence techniques; Pergamon-Elsevier Science Ltd; International Journal of Hydrogen Energy; 37; 19; 10-2012; 14781-14785
0360-3199
CONICET Digital
CONICET
url http://hdl.handle.net/11336/196334
identifier_str_mv Sánchez Reinoso, Carlos Roberto; Cutrera, M.; Battioni, M.; Milone, Diego Humberto; Buitrago, R. H.; Photovoltaic generation model as a function of weather variables using artificial intelligence techniques; Pergamon-Elsevier Science Ltd; International Journal of Hydrogen Energy; 37; 19; 10-2012; 14781-14785
0360-3199
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0360319911027741
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.ijhydene.2011.12.081
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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