A computational intelligence approach for solar photovoltaic power generation forecasting

This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed pre...

Descripción completa

Detalles Bibliográficos
Autores: Nesmachnow, Sergio, Risso, Claudio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:Uruguay
Institución:Universidad de la República
Repositorio:COLIBRI
Idioma:inglés
OAI Identifier:oai:colibri.udelar.edu.uy:20.500.12008/52917
Acceso en línea:https://journals.sagepub.com/doi/10.1177/27533735241237990
https://hdl.handle.net/20.500.12008/52917
Access Level:acceso abierto
Palabra clave:Solar photovoltaic
Forecasting
Neural networks
Computational intelligence
id UY_7494b4fca2da648eb6f57e4fc60a9fb2
oai_identifier_str oai:colibri.udelar.edu.uy:20.500.12008/52917
network_acronym_str UY
network_name_str Uruguay
repository_id_str
dc.title.none.fl_str_mv A computational intelligence approach for solar photovoltaic power generation forecasting
title A computational intelligence approach for solar photovoltaic power generation forecasting
spellingShingle A computational intelligence approach for solar photovoltaic power generation forecasting
Nesmachnow, Sergio
Solar photovoltaic
Forecasting
Neural networks
Computational intelligence
title_short A computational intelligence approach for solar photovoltaic power generation forecasting
title_full A computational intelligence approach for solar photovoltaic power generation forecasting
title_fullStr A computational intelligence approach for solar photovoltaic power generation forecasting
title_full_unstemmed A computational intelligence approach for solar photovoltaic power generation forecasting
title_sort A computational intelligence approach for solar photovoltaic power generation forecasting
dc.creator.none.fl_str_mv Nesmachnow, Sergio
Risso, Claudio
author Nesmachnow, Sergio
author_facet Nesmachnow, Sergio
Risso, Claudio
author_role author
author2 Risso, Claudio
author2_role author
dc.contributor.filiacion.none.fl_str_mv Nesmachnow Sergio, Universidad de la República (Uruguay). Facultad de Ingeniería.
Risso Claudio, Universidad de la República (Uruguay). Facultad de Ingeniería.
dc.subject.es.fl_str_mv Solar photovoltaic
Forecasting
Neural networks
Computational intelligence
topic Solar photovoltaic
Forecasting
Neural networks
Computational intelligence
description This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed predictions enable the implementation of efficient planning, management, and distribution strategies for the generated power, ultimately enhancing the performance and efficiency of the system. The study analyzes and compares artificial neural network approaches for a specific case study using real solar photovoltaic power generation data from Uruguay in the period 2018 to 2022. Several artificial neural network architectures are evaluated for forecasting. The main results indicate that the approach applying a combination of Encoder-Decoder and Long Short Term Memory artificial neural networks is the most effective method for the addressed forecasting problem. The approach yielded promising results, with an average mean error value of 0.09, improving over the other artificial neural network architectures. Even better results were obtained for sunny days. The generated forecasts hold significant value for its application in planning and scheduling processes, aiming to enhance the overall quality of service of the electricity grid.
publishDate 2024
dc.date.issued.none.fl_str_mv 2024
dc.date.accessioned.none.fl_str_mv 2025-12-09T12:40:26Z
dc.date.available.none.fl_str_mv 2025-12-09T12:40:26Z
dc.type.es.fl_str_mv Artículo
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.version.none.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.citation.es.fl_str_mv Nesmachnow, S. y Risso, C. "A computational intelligence approach for solar photovoltaic power generation forecasting". Renewable Energies. [en línea]. 2024, vol. 2, no. 1, pp. 1-18. DOI: 10.1177/27533735241237990.
dc.identifier.issn.none.fl_str_mv 2753-3735
dc.identifier.uri.none.fl_str_mv https://journals.sagepub.com/doi/10.1177/27533735241237990
https://hdl.handle.net/20.500.12008/52917
dc.identifier.doi.none.fl_str_mv 10.1177/27533735241237990
identifier_str_mv Nesmachnow, S. y Risso, C. "A computational intelligence approach for solar photovoltaic power generation forecasting". Renewable Energies. [en línea]. 2024, vol. 2, no. 1, pp. 1-18. DOI: 10.1177/27533735241237990.
2753-3735
10.1177/27533735241237990
url https://journals.sagepub.com/doi/10.1177/27533735241237990
https://hdl.handle.net/20.500.12008/52917
dc.language.iso.none.fl_str_mv en
eng
language_invalid_str_mv en
language eng
dc.relation.none.fl_str_mv Renewable Energies, vol. 2, no. 1, jan. 2024, pp. 1-18, DOI: 10.1177/27533735241237990.
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.license.none.fl_str_mv Licencia Creative Commons Atribución - No Comercial (CC - By-NC 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv Licencia Creative Commons Atribución - No Comercial (CC - By-NC 4.0)
dc.format.extent.es.fl_str_mv 18 p.
dc.format.mimetype.es.fl_str_mv application/pdf
dc.publisher.es.fl_str_mv Sage
dc.source.none.fl_str_mv reponame:COLIBRI
instname:Universidad de la República
instacron:Universidad de la República
instname_str Universidad de la República
instacron_str Universidad de la República
institution Universidad de la República
reponame_str COLIBRI
collection COLIBRI
bitstream.url.fl_str_mv http://localhost:8080/xmlui/bitstream/20.500.12008/52917/5/license.txt
http://localhost:8080/xmlui/bitstream/20.500.12008/52917/2/license_url
http://localhost:8080/xmlui/bitstream/20.500.12008/52917/3/license_text
http://localhost:8080/xmlui/bitstream/20.500.12008/52917/4/license_rdf
http://localhost:8080/xmlui/bitstream/20.500.12008/52917/1/NR24.pdf
bitstream.checksum.fl_str_mv 6429389a7df7277b72b7924fdc7d47a9
966d4a1cc97b2c4389b5142dd97d3c7f
33ea1719120050a89f37d1e091115085
e9507f17d292a045f834ee111e4d098b
31f5c9b68a0c6755ad83784b6d7ff8b1
bitstream.checksumAlgorithm.fl_str_mv MD5
MD5
MD5
MD5
MD5
repository.name.fl_str_mv COLIBRI - Universidad de la República
repository.mail.fl_str_mv karina.camps@seciu.edu.uy
_version_ 1871461110077456384
spelling Nesmachnow Sergio, Universidad de la República (Uruguay). Facultad de Ingeniería.Risso Claudio, Universidad de la República (Uruguay). Facultad de Ingeniería.2025-12-09T12:40:26Z2025-12-09T12:40:26Z2024Nesmachnow, S. y Risso, C. "A computational intelligence approach for solar photovoltaic power generation forecasting". Renewable Energies. [en línea]. 2024, vol. 2, no. 1, pp. 1-18. DOI: 10.1177/27533735241237990.2753-3735https://journals.sagepub.com/doi/10.1177/27533735241237990https://hdl.handle.net/20.500.12008/5291710.1177/27533735241237990This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed predictions enable the implementation of efficient planning, management, and distribution strategies for the generated power, ultimately enhancing the performance and efficiency of the system. The study analyzes and compares artificial neural network approaches for a specific case study using real solar photovoltaic power generation data from Uruguay in the period 2018 to 2022. Several artificial neural network architectures are evaluated for forecasting. The main results indicate that the approach applying a combination of Encoder-Decoder and Long Short Term Memory artificial neural networks is the most effective method for the addressed forecasting problem. The approach yielded promising results, with an average mean error value of 0.09, improving over the other artificial neural network architectures. Even better results were obtained for sunny days. The generated forecasts hold significant value for its application in planning and scheduling processes, aiming to enhance the overall quality of service of the electricity grid.Submitted by Ribeiro Jorge (jribeiro@fing.edu.uy) on 2025-12-04T01:19:34Z No. of bitstreams: 2 license_rdf: 26648 bytes, checksum: e9507f17d292a045f834ee111e4d098b (MD5) NR24.pdf: 5003819 bytes, checksum: 31f5c9b68a0c6755ad83784b6d7ff8b1 (MD5)Approved for entry into archive by Machado Jimena (jmachado@fing.edu.uy) on 2025-12-08T17:03:07Z (GMT) No. of bitstreams: 2 license_rdf: 26648 bytes, checksum: e9507f17d292a045f834ee111e4d098b (MD5) NR24.pdf: 5003819 bytes, checksum: 31f5c9b68a0c6755ad83784b6d7ff8b1 (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2025-12-09T12:40:26Z (GMT). No. of bitstreams: 2 license_rdf: 26648 bytes, checksum: e9507f17d292a045f834ee111e4d098b (MD5) NR24.pdf: 5003819 bytes, checksum: 31f5c9b68a0c6755ad83784b6d7ff8b1 (MD5) Previous issue date: 2024La investigación presentada en este artículo se desarrolló como parte de un proyecto conjunto entre la UTE y la Universidad de la República, Uruguay.18 p.application/pdfenengSageRenewable Energies, vol. 2, no. 1, jan. 2024, pp. 1-18, DOI: 10.1177/27533735241237990.Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial (CC - By-NC 4.0)Solar photovoltaicForecastingNeural networksComputational intelligenceA computational intelligence approach for solar photovoltaic power generation forecastingArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaNesmachnow, SergioRisso, ClaudioLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/52917/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-847http://localhost:8080/xmlui/bitstream/20.500.12008/52917/2/license_url966d4a1cc97b2c4389b5142dd97d3c7fMD52license_textlicense_texttext/html; charset=utf-828383http://localhost:8080/xmlui/bitstream/20.500.12008/52917/3/license_text33ea1719120050a89f37d1e091115085MD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-826648http://localhost:8080/xmlui/bitstream/20.500.12008/52917/4/license_rdfe9507f17d292a045f834ee111e4d098bMD54ORIGINALNR24.pdfNR24.pdfapplication/pdf5003819http://localhost:8080/xmlui/bitstream/20.500.12008/52917/1/NR24.pdf31f5c9b68a0c6755ad83784b6d7ff8b1MD5120.500.12008/529172025-12-09 09:40:26.376oai:colibri.udelar.edu.uy:20.500.12008/52917VGVybWlub3MgeSBjb25kaWNpb25lcyByZWxhdGl2YXMgYWwgZGVwb3NpdG8gZGUgb2JyYXMKCgpMYXMgb2JyYXMgZGVwb3NpdGFkYXMgZW4gZWwgUmVwb3NpdG9yaW8gc2UgcmlnZW4gcG9yIGxhIE9yZGVuYW56YSBkZSBsb3MgRGVyZWNob3MgZGUgbGEgUHJvcGllZGFkIEludGVsZWN0dWFsICBkZSBsYSBVbml2ZXJzaWRhZCBEZSBMYSBSZXDDumJsaWNhLiAoUmVzLiBOwrogOTEgZGUgQy5ELkMuIGRlIDgvSUlJLzE5OTQg4oCTIEQuTy4gNy9JVi8xOTk0KSB5ICBwb3IgbGEgT3JkZW5hbnphIGRlbCBSZXBvc2l0b3JpbyBBYmllcnRvIGRlIGxhIFVuaXZlcnNpZGFkIGRlIGxhIFJlcMO6YmxpY2EgKFJlcy4gTsK6IDE2IGRlIEMuRC5DLiBkZSAwNy8xMC8yMDE0KS4gCgpBY2VwdGFuZG8gZWwgYXV0b3IgZXN0b3MgdMOpcm1pbm9zIHkgY29uZGljaW9uZXMgZGUgZGVww7NzaXRvIGVuIENPTElCUkksIGxhIFVuaXZlcnNpZGFkIGRlIFJlcMO6YmxpY2EgcHJvY2VkZXLDoSBhOiAgCgphKSBhcmNoaXZhciBtw6FzIGRlIHVuYSBjb3BpYSBkZSBsYSBvYnJhIGVuIGxvcyBzZXJ2aWRvcmVzIGRlIGxhIFVuaXZlcnNpZGFkIGEgbG9zIGVmZWN0b3MgZGUgZ2FyYW50aXphciBhY2Nlc28sIHNlZ3VyaWRhZCB5IHByZXNlcnZhY2nDs24KYikgY29udmVydGlyIGxhIG9icmEgYSBvdHJvcyBmb3JtYXRvcyBzaSBmdWVyYSBuZWNlc2FyaW8gIHBhcmEgZmFjaWxpdGFyIHN1IHByZXNlcnZhY2nDs24geSBhY2Nlc2liaWxpZGFkIHNpbiBhbHRlcmFyIHN1IGNvbnRlbmlkby4KYykgcmVhbGl6YXIgbGEgY29tdW5pY2FjacOzbiBww7pibGljYSB5IGRpc3BvbmVyIGVsIGFjY2VzbyBsaWJyZSB5IGdyYXR1aXRvIGEgdHJhdsOpcyBkZSBJbnRlcm5ldCBtZWRpYW50ZSBsYSBwdWJsaWNhY2nDs24gZGUgbGEgb2JyYSBiYWpvIGxhIGxpY2VuY2lhIENyZWF0aXZlIENvbW1vbnMgc2VsZWNjaW9uYWRhIHBvciBlbCBwcm9waW8gYXV0b3IuCgoKRW4gY2FzbyBxdWUgZWwgYXV0b3IgaGF5YSBkaWZ1bmRpZG8geSBkYWRvIGEgcHVibGljaWRhZCBhIGxhIG9icmEgZW4gZm9ybWEgcHJldmlhLCAgcG9kcsOhIHNvbGljaXRhciB1biBwZXLDrW9kbyBkZSBlbWJhcmdvIHNvYnJlIGxhIGRpc3BvbmliaWxpZGFkIHDDumJsaWNhIGRlIGxhIG1pc21hLCBlbCBjdWFsIGNvbWVuemFyw6EgYSBwYXJ0aXIgZGUgbGEgYWNlcHRhY2nDs24gZGUgZXN0ZSBkb2N1bWVudG8geSBoYXN0YSBsYSBmZWNoYSBxdWUgaW5kaXF1ZSAuCgpFbCBhdXRvciBhc2VndXJhIHF1ZSBsYSBvYnJhIG5vIGluZnJpZ2UgbmluZ8O6biBkZXJlY2hvIHNvYnJlIHRlcmNlcm9zLCB5YSBzZWEgZGUgcHJvcGllZGFkIGludGVsZWN0dWFsIG8gY3VhbHF1aWVyIG90cm8uCgpFbCBhdXRvciBnYXJhbnRpemEgcXVlIHNpIGVsIGRvY3VtZW50byBjb250aWVuZSBtYXRlcmlhbGVzIGRlIGxvcyBjdWFsZXMgbm8gdGllbmUgbG9zIGRlcmVjaG9zIGRlIGF1dG9yLCAgaGEgb2J0ZW5pZG8gZWwgcGVybWlzbyBkZWwgcHJvcGlldGFyaW8gZGUgbG9zIGRlcmVjaG9zIGRlIGF1dG9yLCB5IHF1ZSBlc2UgbWF0ZXJpYWwgY3V5b3MgZGVyZWNob3Mgc29uIGRlIHRlcmNlcm9zIGVzdMOhIGNsYXJhbWVudGUgaWRlbnRpZmljYWRvIHkgcmVjb25vY2lkbyBlbiBlbCB0ZXh0byBvIGNvbnRlbmlkbyBkZWwgZG9jdW1lbnRvIGRlcG9zaXRhZG8gZW4gZWwgUmVwb3NpdG9yaW8uCgpFbiBvYnJhcyBkZSBhdXRvcsOtYSBtw7psdGlwbGUgL3NlIHByZXN1bWUvIHF1ZSBlbCBhdXRvciBkZXBvc2l0YW50ZSBkZWNsYXJhIHF1ZSBoYSByZWNhYmFkbyBlbCBjb25zZW50aW1pZW50byBkZSB0b2RvcyBsb3MgYXV0b3JlcyBwYXJhIHB1YmxpY2FybGEgZW4gZWwgUmVwb3NpdG9yaW8sIHNpZW5kbyDDqXN0ZSBlbCDDum5pY28gcmVzcG9uc2FibGUgZnJlbnRlIGEgY3VhbHF1aWVyIHRpcG8gZGUgcmVjbGFtYWNpw7NuIGRlIGxvcyBvdHJvcyBjb2F1dG9yZXMuCgpFbCBhdXRvciBzZXLDoSByZXNwb25zYWJsZSBkZWwgY29udGVuaWRvIGRlIGxvcyBkb2N1bWVudG9zIHF1ZSBkZXBvc2l0YS4gTGEgVURFTEFSIG5vIHNlcsOhIHJlc3BvbnNhYmxlIHBvciBsYXMgZXZlbnR1YWxlcyB2aW9sYWNpb25lcyBhbCBkZXJlY2hvIGRlIHByb3BpZWRhZCBpbnRlbGVjdHVhbCBlbiBxdWUgcHVlZGEgaW5jdXJyaXIgZWwgYXV0b3IuCgpBbnRlIGN1YWxxdWllciBkZW51bmNpYSBkZSB2aW9sYWNpw7NuIGRlIGRlcmVjaG9zIGRlIHByb3BpZWRhZCBpbnRlbGVjdHVhbCwgbGEgVURFTEFSICBhZG9wdGFyw6EgdG9kYXMgbGFzIG1lZGlkYXMgbmVjZXNhcmlhcyBwYXJhIGV2aXRhciBsYSBjb250aW51YWNpw7NuIGRlIGRpY2hhIGluZnJhY2Npw7NuLCBsYXMgcXVlIHBvZHLDoW4gaW5jbHVpciBlbCByZXRpcm8gZGVsIGFjY2VzbyBhIGxvcyBjb250ZW5pZG9zIHkvbyBtZXRhZGF0b3MgZGVsIGRvY3VtZW50byByZXNwZWN0aXZvLgoKTGEgb2JyYSBzZSBwb25kcsOhIGEgZGlzcG9zaWNpw7NuIGRlbCBww7pibGljbyBhIHRyYXbDqXMgZGUgbGFzIGxpY2VuY2lhcyBDcmVhdGl2ZSBDb21tb25zLCBlbCBhdXRvciBwb2Ryw6Egc2VsZWNjaW9uYXIgdW5hIGRlIGxhcyA2IGxpY2VuY2lhcyBkaXNwb25pYmxlczoKCgpBdHJpYnVjacOzbiAoQ0MgLSBCeSk6IFBlcm1pdGUgdXNhciBsYSBvYnJhIHkgZ2VuZXJhciBvYnJhcyBkZXJpdmFkYXMsIGluY2x1c28gY29uIGZpbmVzIGNvbWVyY2lhbGVzLCBzaWVtcHJlIHF1ZSBzZSByZWNvbm96Y2EgYWwgYXV0b3IuCgpBdHJpYnVjacOzbiDigJMgQ29tcGFydGlyIElndWFsIChDQyAtIEJ5LVNBKTogUGVybWl0ZSB1c2FyIGxhIG9icmEgeSBnZW5lcmFyIG9icmFzIGRlcml2YWRhcywgaW5jbHVzbyBjb24gZmluZXMgY29tZXJjaWFsZXMsIHBlcm8gbGEgZGlzdHJpYnVjacOzbiBkZSBsYXMgb2JyYXMgZGVyaXZhZGFzIGRlYmUgaGFjZXJzZSBtZWRpYW50ZSB1bmEgbGljZW5jaWEgaWTDqW50aWNhIGEgbGEgZGUgbGEgb2JyYSBvcmlnaW5hbCwgcmVjb25vY2llbmRvIGEgbG9zIGF1dG9yZXMuCgpBdHJpYnVjacOzbiDigJMgTm8gQ29tZXJjaWFsIChDQyAtIEJ5LU5DKTogUGVybWl0ZSB1c2FyIGxhIG9icmEgeSBnZW5lcmFyIG9icmFzIGRlcml2YWRhcywgc2llbXByZSB5IGN1YW5kbyBlc29zIHVzb3Mgbm8gdGVuZ2FuIGZpbmVzIGNvbWVyY2lhbGVzLCByZWNvbm9jaWVuZG8gYWwgYXV0b3IuCgpBdHJpYnVjacOzbiDigJMgU2luIERlcml2YWRhcyAoQ0MgLSBCeS1ORCk6IFBlcm1pdGUgZWwgdXNvIGRlIGxhIG9icmEsIGluY2x1c28gY29uIGZpbmVzIGNvbWVyY2lhbGVzLCBwZXJvIG5vIHNlIHBlcm1pdGUgZ2VuZXJhciBvYnJhcyBkZXJpdmFkYXMsIGRlYmllbmRvIHJlY29ub2NlciBhbCBhdXRvci4KCkF0cmlidWNpw7NuIOKAkyBObyBDb21lcmNpYWwg4oCTIENvbXBhcnRpciBJZ3VhbCAoQ0Mg4oCTIEJ5LU5DLVNBKTogUGVybWl0ZSB1c2FyIGxhIG9icmEgeSBnZW5lcmFyIG9icmFzIGRlcml2YWRhcywgc2llbXByZSB5IGN1YW5kbyBlc29zIHVzb3Mgbm8gdGVuZ2FuIGZpbmVzIGNvbWVyY2lhbGVzIHkgbGEgZGlzdHJpYnVjacOzbiBkZSBsYXMgb2JyYXMgZGVyaXZhZGFzIHNlIGhhZ2EgbWVkaWFudGUgbGljZW5jaWEgaWTDqW50aWNhIGEgbGEgZGUgbGEgb2JyYSBvcmlnaW5hbCwgcmVjb25vY2llbmRvIGEgbG9zIGF1dG9yZXMuCgpBdHJpYnVjacOzbiDigJMgTm8gQ29tZXJjaWFsIOKAkyBTaW4gRGVyaXZhZGFzIChDQyAtIEJ5LU5DLU5EKTogUGVybWl0ZSB1c2FyIGxhIG9icmEsIHBlcm8gbm8gc2UgcGVybWl0ZSBnZW5lcmFyIG9icmFzIGRlcml2YWRhcyB5IG5vIHNlIHBlcm1pdGUgdXNvIGNvbiBmaW5lcyBjb21lcmNpYWxlcywgZGViaWVuZG8gcmVjb25vY2VyIGFsIGF1dG9yLgoKTG9zIHVzb3MgcHJldmlzdG9zIGVuIGxhcyBsaWNlbmNpYXMgaW5jbHV5ZW4gbGEgZW5hamVuYWNpw7NuLCByZXByb2R1Y2Npw7NuLCBjb211bmljYWNpw7NuLCBwdWJsaWNhY2nDs24sIGRpc3RyaWJ1Y2nDs24geSBwdWVzdGEgYSBkaXNwb3NpY2nDs24gZGVsIHDDumJsaWNvLiBMYSBjcmVhY2nDs24gZGUgb2JyYXMgZGVyaXZhZGFzIGluY2x1eWUgbGEgYWRhcHRhY2nDs24sIHRyYWR1Y2Npw7NuIHkgZWwgcmVtaXguCgpDdWFuZG8gc2Ugc2VsZWNjaW9uZSB1bmEgbGljZW5jaWEgcXVlIGhhYmlsaXRlIHVzb3MgY29tZXJjaWFsZXMsIGVsIGRlcMOzc2l0byBkZWJlcsOhIHNlciBhY29tcGHDsWFkbyBkZWwgYXZhbCBkZWwgamVyYXJjYSBtw6F4aW1vIGRlbCBTZXJ2aWNpbyBjb3JyZXNwb25kaWVudGUuCg==Institucionalhttps://www.colibri.udelar.edu.uyUniversidad públicahttps://udelar.edu.uy/https://www.colibri.udelar.edu.uy/oai/requestkarina.camps@seciu.edu.uyUruguayopendoar:47712025-12-09T12:40:26COLIBRI - Universidad de la Repúblicafalse
score 14.965156

Similares en LA Referencia