A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching

This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).

Detalles Bibliográficos
Autores: Omoyele, Olalekan, Hoffmann, Maximilian, Weinand, Jann Michael, Larrañeta, Miguel, Linßen, Jochen, Stolten, Detlef
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2026
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/179275
Acceso en línea:https://hdl.handle.net/11441/179275
https://doi.org/10.1016/j.renene.2025.124551
Access Level:acceso abierto
Palabra clave:Solar irradiance
Downscaling
Clearness index
Variability index
Temporal resolution
Energy system modeling
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spelling A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter MatchingOmoyele, OlalekanHoffmann, MaximilianWeinand, Jann MichaelLarrañeta, MiguelLinßen, JochenStolten, DetlefSolar irradianceDownscalingClearness indexVariability indexTemporal resolutionEnergy system modelingThis is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).The limited intra-hour variability of globally available hourly renewable energy system data leads to inaccuracies in the modeling of renewable energy systems. While sub-hourly data can improve model accuracy, such data are not globally available. The existing approaches to increase the temporal resolution of solar irradiance often rely on site specific measurements or complex models, limiting global scalability. This work, therefore, presents a methodology to increase the temporal resolution of the global horizontal irradiance from 1 h to 1 min using non-dimensional irradiance and parameters matching based on daily irradiance characteristics for arbitrary locations. The methodology is validated using statistical methods and energy system optimization. The hourly annual normalized root mean square error and Kolmogorov-Smirnov Integral range from 5 to 7 % and 0.1–0.7, respectively, for different locations consisting of varying weather conditions. The energy system optimization results of the synthetic data demonstrate superiority in terms of cost and feasibility relative to the average hourly resolution data. The use of synthetic minute resolution data significantly improves the design accuracy of dynamic components such as inverters and storage systems. The globally applicable method, based on Köppen-Geiger classification coverage, will enable more reliable energy systems modeling in the future.ElsevierIngeniería EnergéticaMinisterio de Ciencia e Innovación (MICIN). España2026info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/179275https://doi.org/10.1016/j.renene.2025.124551reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésRenewable Energy, 256, 124551.RYC2021-032300-Ihttps://www.sciencedirect.com/science/article/pii/S0960148125022153info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1792752026-06-17T12:51:07Z
dc.title.none.fl_str_mv A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
title A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
spellingShingle A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
Omoyele, Olalekan
Solar irradiance
Downscaling
Clearness index
Variability index
Temporal resolution
Energy system modeling
title_short A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
title_full A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
title_fullStr A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
title_full_unstemmed A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
title_sort A High-Resolution Downscaling Approach for Solar Irradiance Using Statistical Parameter Matching
dc.creator.none.fl_str_mv Omoyele, Olalekan
Hoffmann, Maximilian
Weinand, Jann Michael
Larrañeta, Miguel
Linßen, Jochen
Stolten, Detlef
author Omoyele, Olalekan
author_facet Omoyele, Olalekan
Hoffmann, Maximilian
Weinand, Jann Michael
Larrañeta, Miguel
Linßen, Jochen
Stolten, Detlef
author_role author
author2 Hoffmann, Maximilian
Weinand, Jann Michael
Larrañeta, Miguel
Linßen, Jochen
Stolten, Detlef
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Ingeniería Energética
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Solar irradiance
Downscaling
Clearness index
Variability index
Temporal resolution
Energy system modeling
topic Solar irradiance
Downscaling
Clearness index
Variability index
Temporal resolution
Energy system modeling
description This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).
publishDate 2026
dc.date.none.fl_str_mv 2026
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/179275
https://doi.org/10.1016/j.renene.2025.124551
url https://hdl.handle.net/11441/179275
https://doi.org/10.1016/j.renene.2025.124551
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Renewable Energy, 256, 124551.
RYC2021-032300-I
https://www.sciencedirect.com/science/article/pii/S0960148125022153
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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