Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.

López Cuesta, Miguel; Aler Mur, Ricardo; Galván León, Miguel; Rodríguez Benítez, Francisco Javier; Pozo Vazquez, Antonio David

Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.

Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending ap...

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Detalles Bibliográficos
Autores:	López Cuesta, Miguel, Aler Mur, Ricardo, Galván León, Miguel, Rodríguez Benítez, Francisco Javier, Pozo Vazquez, Antonio David
Tipo de recurso:	artículo
Estado:	Versión publicada
Fecha de publicación:	2023
País:	España
Institución:	Universidad de Jaén
Repositorio:	RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
OAI Identifier:	oai:ruja.ujaen.es:10953/6680
Acceso en línea:	https://doi.org/10.3390/rs15092328 https://www.mdpi.com/2072-4292/15/9/2328 https://hdl.handle.net/10953/6680
Access Level:	acceso abierto
Palabra clave:	solar energy solar irradiance nowcasting machine learning models blending all sky imagers (ASI) MSG satellite images

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spelling	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.López Cuesta, MiguelAler Mur, RicardoGalván León, MiguelRodríguez Benítez, Francisco JavierPozo Vazquez, Antonio Davidsolar energysolar irradiance nowcastingmachine learning models blendingall sky imagers (ASI)MSG satellite imagessolar energysolar irradiance nowcastingmachine learning models blendingall sky imagers (ASI)MSG satellite imagesAccurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting.This work was financed by the Junta de Andalucía, project PROMESOLAR (Programa Operativo FEDER Andalucía 2014–2020, ref. 1260136). The authors are supported by the Junta de Andalucía (Research group TEP-220). This publication is part of the I+D+i project PID2019-107455RB-C22, funded by MCIN/AEI/10.13039/501100011033. This work was also supported by the Comunidad de Madrid Excellence ProgramMDPI202520252023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://doi.org/10.3390/rs15092328https://www.mdpi.com/2072-4292/15/9/2328https://hdl.handle.net/10953/6680reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaéninstname:Universidad de JaénInglésRemote SensingAttribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:ruja.ujaen.es:10953/66802026-06-24T12:41:07Z
dc.title.none.fl_str_mv	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.
title	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.
spellingShingle	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques. López Cuesta, Miguel solar energy solar irradiance nowcasting machine learning models blending all sky imagers (ASI) MSG satellite images solar energy solar irradiance nowcasting machine learning models blending all sky imagers (ASI) MSG satellite images
title_short	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.
title_full	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.
title_fullStr	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.
title_full_unstemmed	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.
title_sort	Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.
dc.creator.none.fl_str_mv	López Cuesta, Miguel Aler Mur, Ricardo Galván León, Miguel Rodríguez Benítez, Francisco Javier Pozo Vazquez, Antonio David
author	López Cuesta, Miguel
author_facet	López Cuesta, Miguel Aler Mur, Ricardo Galván León, Miguel Rodríguez Benítez, Francisco Javier Pozo Vazquez, Antonio David
author_role	author
author2	Aler Mur, Ricardo Galván León, Miguel Rodríguez Benítez, Francisco Javier Pozo Vazquez, Antonio David
author2_role	author author author author
dc.subject.none.fl_str_mv	solar energy solar irradiance nowcasting machine learning models blending all sky imagers (ASI) MSG satellite images solar energy solar irradiance nowcasting machine learning models blending all sky imagers (ASI) MSG satellite images
topic	solar energy solar irradiance nowcasting machine learning models blending all sky imagers (ASI) MSG satellite images solar energy solar irradiance nowcasting machine learning models blending all sky imagers (ASI) MSG satellite images
description	Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting.
publishDate	2023
dc.date.none.fl_str_mv	2023 2025 2025
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://doi.org/10.3390/rs15092328 https://www.mdpi.com/2072-4292/15/9/2328 https://hdl.handle.net/10953/6680
url	https://doi.org/10.3390/rs15092328 https://www.mdpi.com/2072-4292/15/9/2328 https://hdl.handle.net/10953/6680
dc.language.none.fl_str_mv	Inglés
language_invalid_str_mv	Inglés
dc.relation.none.fl_str_mv	Remote Sensing
dc.rights.none.fl_str_mv	Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess
rights_invalid_str_mv	Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv	openAccess
dc.format.none.fl_str_mv	application/pdf
dc.publisher.none.fl_str_mv	MDPI
publisher.none.fl_str_mv	MDPI
dc.source.none.fl_str_mv	reponame:RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén instname:Universidad de Jaén
instname_str	Universidad de Jaén
reponame_str	RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
collection	RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score	15,811543

Improving Solar Radiation Nowcasts by Blending Data-Driven, Satellite-Images-Based and All-Sky-Imagers-Based Models Using Machine Learning Techniques.

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