Photovoltaic Power Forecasting Using Sky Images and Sun Motion

Solar energy adoption is moving at a rapid pace. The variability in solar energy production causes grid stability issues and hinders mass adoption. To solve these issues, more accurate photovoltaic power forecasting systems are needed. In intra-hour forecasting, the most challenging issue is high ou...

Descripción completa

Detalles Bibliográficos
Autores: Berresheim, Arne, Agudo Martínez, Antonio
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/388085
Acceso en línea:http://hdl.handle.net/10261/388085
https://api.elsevier.com/content/abstract/scopus_id/105001505997
Access Level:acceso abierto
Palabra clave:Deep Learning
Photovoltaic Power Estimation
Sky Images
Sun Tracking
id ES_1dc320c8a8a59e48e27e7d696cdc38c8
oai_identifier_str oai:digital.csic.es:10261/388085
network_acronym_str ES
network_name_str España
repository_id_str
spelling Photovoltaic Power Forecasting Using Sky Images and Sun MotionBerresheim, ArneAgudo Martínez, AntonioDeep LearningPhotovoltaic Power EstimationSky ImagesSun TrackingSolar energy adoption is moving at a rapid pace. The variability in solar energy production causes grid stability issues and hinders mass adoption. To solve these issues, more accurate photovoltaic power forecasting systems are needed. In intra-hour forecasting, the most challenging issue is high output fluctuations due to cloud motion, which can occlude the sun. Using ground-based sky images, this paper proposes two convolutional neural network models for intra-hour nowcasting and forecasting that incorporate physical information on sun motion and cloud coverage by means of the sun area mean pixel intensity. Particularly, our models exploit that information instead of relying exclusively on photovoltaic output history data as it is standard in state of the art. Taking advantage of sun position and cloud coverage information, we were able to reduce the overall root mean squared error for the nowcasting task, making the model more accurate especially during cloudy days, and obtaining competitive results on forecasting. Moreover, our models are more robust against artifacts such as occlusion and noisy observations.This work has been supported by the project MoHuCo PID2020- 120049RB-I00 funded by MCIN/ AEI /10.13039/501100011033.Peer reviewedInstitute of Electrical and Electronics EngineersAgencia Estatal de Investigación (España)Ministerio de Ciencia, Innovación y Universidades (España)Agudo Martínez, Antonio [0000-0001-6845-4998]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252024info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Postprintinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttp://hdl.handle.net/10261/388085https://api.elsevier.com/content/abstract/scopus_id/105001505997reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-120049RB-I00https://doi.org/10.1109/ICASSP48485.2024.10448183Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3880852026-05-22T06:33:51Z
dc.title.none.fl_str_mv Photovoltaic Power Forecasting Using Sky Images and Sun Motion
title Photovoltaic Power Forecasting Using Sky Images and Sun Motion
spellingShingle Photovoltaic Power Forecasting Using Sky Images and Sun Motion
Berresheim, Arne
Deep Learning
Photovoltaic Power Estimation
Sky Images
Sun Tracking
title_short Photovoltaic Power Forecasting Using Sky Images and Sun Motion
title_full Photovoltaic Power Forecasting Using Sky Images and Sun Motion
title_fullStr Photovoltaic Power Forecasting Using Sky Images and Sun Motion
title_full_unstemmed Photovoltaic Power Forecasting Using Sky Images and Sun Motion
title_sort Photovoltaic Power Forecasting Using Sky Images and Sun Motion
dc.creator.none.fl_str_mv Berresheim, Arne
Agudo Martínez, Antonio
author Berresheim, Arne
author_facet Berresheim, Arne
Agudo Martínez, Antonio
author_role author
author2 Agudo Martínez, Antonio
author2_role author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación (España)
Ministerio de Ciencia, Innovación y Universidades (España)
Agudo Martínez, Antonio [0000-0001-6845-4998]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Deep Learning
Photovoltaic Power Estimation
Sky Images
Sun Tracking
topic Deep Learning
Photovoltaic Power Estimation
Sky Images
Sun Tracking
description Solar energy adoption is moving at a rapid pace. The variability in solar energy production causes grid stability issues and hinders mass adoption. To solve these issues, more accurate photovoltaic power forecasting systems are needed. In intra-hour forecasting, the most challenging issue is high output fluctuations due to cloud motion, which can occlude the sun. Using ground-based sky images, this paper proposes two convolutional neural network models for intra-hour nowcasting and forecasting that incorporate physical information on sun motion and cloud coverage by means of the sun area mean pixel intensity. Particularly, our models exploit that information instead of relying exclusively on photovoltaic output history data as it is standard in state of the art. Taking advantage of sun position and cloud coverage information, we were able to reduce the overall root mean squared error for the nowcasting task, making the model more accurate especially during cloudy days, and obtaining competitive results on forecasting. Moreover, our models are more robust against artifacts such as occlusion and noisy observations.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Postprint
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/388085
https://api.elsevier.com/content/abstract/scopus_id/105001505997
url http://hdl.handle.net/10261/388085
https://api.elsevier.com/content/abstract/scopus_id/105001505997
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-120049RB-I00
https://doi.org/10.1109/ICASSP48485.2024.10448183
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404298526326784
score 15.812429

Similares en LA Referencia