An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production

Renewable energies are the alternative that leads to a cleaner generation and a reduction in CO2 emissions. However, their dependency on weather makes them unreliable. Traditional energy operators need a highly accurate estimation of energy to ensure the appropriate control of the network, since ene...

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Autores: Etxegarai, G. (Garazi)|||/items/ecafc318-31cc-4f30-a2e6-75c4b401f3e3, López, A. (Andrés)|||/items/9f70b953-8d32-406c-b007-2fce2e8e1df5, Aginako, N. (Naiara)|||/items/d61a4042-661c-4544-8c53-5676f455ec3b, Rodríguez, F. (Fermín)|||/items/99068aae-e6c7-4106-9d20-0528327fa474
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universidad de Navarra
Repositorio:Dadun. Depósito Académico Digital de la Universidad de Navarra
Idioma:inglés
OAI Identifier:oai:dadun.unav.edu:10171/63742
Acceso en línea:https://hdl.handle.net/10171/63742
Access Level:acceso abierto
Palabra clave:Solar irradiation forecasting
Artificial neural network
Very short-term forecasting
Long short term memory
Convolutional neural network
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spelling An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy productionEtxegarai, G. (Garazi)|||/items/ecafc318-31cc-4f30-a2e6-75c4b401f3e3López, A. (Andrés)|||/items/9f70b953-8d32-406c-b007-2fce2e8e1df5Aginako, N. (Naiara)|||/items/d61a4042-661c-4544-8c53-5676f455ec3bRodríguez, F. (Fermín)|||/items/99068aae-e6c7-4106-9d20-0528327fa474Solar irradiation forecastingArtificial neural networkVery short-term forecastingLong short term memoryConvolutional neural networkRenewable energies are the alternative that leads to a cleaner generation and a reduction in CO2 emissions. However, their dependency on weather makes them unreliable. Traditional energy operators need a highly accurate estimation of energy to ensure the appropriate control of the network, since energy generation and demand must be balanced. This paper proposes a forecaster to predict solar irradiation, for very short-term, specifically, in the 10 min ahead. This study develops two tools based on artificial neural networks, namely Long-Short Term Memory neural networks and Convolutional Neural Network. The results demonstrate that the Convolutional Neural Network has a higher accuracy. The tool is tested examining the root mean square error, which was of 52.58 W/m2 for the testing step. Compared against the benchmark, it has obtained an improvement of 8.16%. Additionally, for the 82% of the tested days it has given a less than 4% error between the predicted and the actual energy generation. Results indicate that the forecaster is accurate enough to be implemented on a photovoltaic generation plan, improving their integration into the electrical grid, not only for providing power but also ancillary services.ElsevierDadun. Depósito Académico Digital Universidad de Navarra20222022-06-3020222022-01-0120222022-01-01journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10171/63742reponame:Dadun. Depósito Académico Digital de la Universidad de Navarrainstname:Universidad de NavarraInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dadun.unav.edu:10171/637422026-06-21T12:47:57Z
dc.title.none.fl_str_mv An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
title An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
spellingShingle An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
Etxegarai, G. (Garazi)|||/items/ecafc318-31cc-4f30-a2e6-75c4b401f3e3
Solar irradiation forecasting
Artificial neural network
Very short-term forecasting
Long short term memory
Convolutional neural network
title_short An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
title_full An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
title_fullStr An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
title_full_unstemmed An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
title_sort An analysis of different deep learning neural networks for intra-hour solar irradiation forecasting to compute solar photovoltaic generators' energy production
dc.creator.none.fl_str_mv Etxegarai, G. (Garazi)|||/items/ecafc318-31cc-4f30-a2e6-75c4b401f3e3
López, A. (Andrés)|||/items/9f70b953-8d32-406c-b007-2fce2e8e1df5
Aginako, N. (Naiara)|||/items/d61a4042-661c-4544-8c53-5676f455ec3b
Rodríguez, F. (Fermín)|||/items/99068aae-e6c7-4106-9d20-0528327fa474
author Etxegarai, G. (Garazi)|||/items/ecafc318-31cc-4f30-a2e6-75c4b401f3e3
author_facet Etxegarai, G. (Garazi)|||/items/ecafc318-31cc-4f30-a2e6-75c4b401f3e3
López, A. (Andrés)|||/items/9f70b953-8d32-406c-b007-2fce2e8e1df5
Aginako, N. (Naiara)|||/items/d61a4042-661c-4544-8c53-5676f455ec3b
Rodríguez, F. (Fermín)|||/items/99068aae-e6c7-4106-9d20-0528327fa474
author_role author
author2 López, A. (Andrés)|||/items/9f70b953-8d32-406c-b007-2fce2e8e1df5
Aginako, N. (Naiara)|||/items/d61a4042-661c-4544-8c53-5676f455ec3b
Rodríguez, F. (Fermín)|||/items/99068aae-e6c7-4106-9d20-0528327fa474
author2_role author
author
author
dc.contributor.none.fl_str_mv Dadun. Depósito Académico Digital Universidad de Navarra
dc.subject.none.fl_str_mv Solar irradiation forecasting
Artificial neural network
Very short-term forecasting
Long short term memory
Convolutional neural network
topic Solar irradiation forecasting
Artificial neural network
Very short-term forecasting
Long short term memory
Convolutional neural network
description Renewable energies are the alternative that leads to a cleaner generation and a reduction in CO2 emissions. However, their dependency on weather makes them unreliable. Traditional energy operators need a highly accurate estimation of energy to ensure the appropriate control of the network, since energy generation and demand must be balanced. This paper proposes a forecaster to predict solar irradiation, for very short-term, specifically, in the 10 min ahead. This study develops two tools based on artificial neural networks, namely Long-Short Term Memory neural networks and Convolutional Neural Network. The results demonstrate that the Convolutional Neural Network has a higher accuracy. The tool is tested examining the root mean square error, which was of 52.58 W/m2 for the testing step. Compared against the benchmark, it has obtained an improvement of 8.16%. Additionally, for the 82% of the tested days it has given a less than 4% error between the predicted and the actual energy generation. Results indicate that the forecaster is accurate enough to be implemented on a photovoltaic generation plan, improving their integration into the electrical grid, not only for providing power but also ancillary services.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-06-30
2022
2022-01-01
2022
2022-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10171/63742
url https://hdl.handle.net/10171/63742
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 Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Dadun. Depósito Académico Digital de la Universidad de Navarra
instname:Universidad de Navarra
instname_str Universidad de Navarra
reponame_str Dadun. Depósito Académico Digital de la Universidad de Navarra
collection Dadun. Depósito Académico Digital de la Universidad de Navarra
repository.name.fl_str_mv
repository.mail.fl_str_mv
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