Machine Learning Nowcasting of PV Energy Using Satellite Data

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at...

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Detalhes bibliográficos
Autores: Catalina Feliu, Alejandro, Torres Barrán, Alberto, Alaiz Gudín, Carlos María, Dorronsoro Ibero, José Ramón
Formato: artículo
Fecha de publicación:2019
País:España
Recursos:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/702746
Acesso em linha:http://hdl.handle.net/10486/702746
https://dx.doi.org/10.1007/s11063-018-09969-1
Access Level:acceso abierto
Palavra-chave:Clear sky models
Lasso
Nowcasting
Photovoltaic energy
Support vector regression
EUMETSAT
Informática
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spelling Machine Learning Nowcasting of PV Energy Using Satellite DataCatalina Feliu, AlejandroTorres Barrán, AlbertoAlaiz Gudín, Carlos MaríaDorronsoro Ibero, José RamónClear sky modelsLassoNowcastingPhotovoltaic energySupport vector regressionEUMETSATInformáticaThis version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11063-018-09969-1Satellite-measured radiances are obviously of great interest for photovoltaic (PV) energy prediction. In this work we will use them together with clear sky irradiance estimates for the nowcasting of PV energy productions over peninsular Spain. We will feed them directly into two linear Machine Learning models, Lasso and linear Support Vector Regression (SVR), and two highly non-linear ones, Deep Neural Networks (in particular, Multilayer Perceptrons, MLPs) and Gaussian SVRs. We shall also use a simple clear sky-based persistence model for benchmarking purposes. We consider prediction horizons of up to 6 h, with Gaussian SVR being statistically better than the other models at each horizon, since its errors increase slowly with time (with an average of 1.92% for the first three horizons and of 2.89% for the last three). MLPs performance is close to that of the Gaussian SVR for the longer horizons (with an average of 3.1%) but less so at the initial ones (average of 2.26%), being nevertheless significantly better than the linear models. As it could be expected, linear models give weaker results (in the initial horizons, Lasso and linear SVR have already an error of 3.21% and 3.46%, respectively), but we will take advantage of the spatial sparsity provided by Lasso to try to identify the concrete areas with a larger influence on PV energy nowcastsWith partial support from Spain’s Grants TIN2013-42351-P, TIN2016-76406-P, TIN2015-70308-REDT and S2013/ICE-2845 CASI-CAM-CM. Work supported also by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016, and the UAM–ADIC Chair for Data Science and Machine Learning. The second author was also supported by the FPU–MEC Grant AP-2012-5163. We thank Red Eléctrica de España for useful discussions and making available PV energy data and gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAMSpringer NatureDepartamento de Ingeniería InformáticaEscuela Politécnica SuperiorAprendizaje Automático (ING EPS-001)20192019-01-05research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/702746https://dx.doi.org/10.1007/s11063-018-09969-1reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/7027462026-06-23T12:46:27Z
dc.title.none.fl_str_mv Machine Learning Nowcasting of PV Energy Using Satellite Data
title Machine Learning Nowcasting of PV Energy Using Satellite Data
spellingShingle Machine Learning Nowcasting of PV Energy Using Satellite Data
Catalina Feliu, Alejandro
Clear sky models
Lasso
Nowcasting
Photovoltaic energy
Support vector regression
EUMETSAT
Informática
title_short Machine Learning Nowcasting of PV Energy Using Satellite Data
title_full Machine Learning Nowcasting of PV Energy Using Satellite Data
title_fullStr Machine Learning Nowcasting of PV Energy Using Satellite Data
title_full_unstemmed Machine Learning Nowcasting of PV Energy Using Satellite Data
title_sort Machine Learning Nowcasting of PV Energy Using Satellite Data
dc.creator.none.fl_str_mv Catalina Feliu, Alejandro
Torres Barrán, Alberto
Alaiz Gudín, Carlos María
Dorronsoro Ibero, José Ramón
author Catalina Feliu, Alejandro
author_facet Catalina Feliu, Alejandro
Torres Barrán, Alberto
Alaiz Gudín, Carlos María
Dorronsoro Ibero, José Ramón
author_role author
author2 Torres Barrán, Alberto
Alaiz Gudín, Carlos María
Dorronsoro Ibero, José Ramón
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
Aprendizaje Automático (ING EPS-001)
dc.subject.none.fl_str_mv Clear sky models
Lasso
Nowcasting
Photovoltaic energy
Support vector regression
EUMETSAT
Informática
topic Clear sky models
Lasso
Nowcasting
Photovoltaic energy
Support vector regression
EUMETSAT
Informática
description This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11063-018-09969-1
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-01-05
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/702746
https://dx.doi.org/10.1007/s11063-018-09969-1
url http://hdl.handle.net/10486/702746
https://dx.doi.org/10.1007/s11063-018-09969-1
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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