Short-term forecasting photovoltaic solar power for home energy management systems

Detalhes bibliográficos
Autor(a) principal: Bot, Karol
Data de Publicação: 2021
Outros Autores: Ruano, Antonio, Ruano, Maria
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.1/15367
Resumo: Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R<sup>2</sup> of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.
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spelling Short-term forecasting photovoltaic solar power for home energy management systemsPhotovoltaic power forecastingMulti-objective genetic algorithmsArtificial neural networksHome energy management systemsAccurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R<sup>2</sup> of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.Programa Operacional Portugal 2020 and Operational Program CRESC Algarve 2020 grant 01/SAICT/2018. Antonio Ruano acknowledges the support of Fundação para a Ciência e Tecnologia, through IDMEC, under LAETA, grant UIDB/50022/2020.MDPISapientiaBot, KarolRuano, AntonioRuano, Maria2021-04-12T14:25:04Z2021-01-252021-03-26T14:06:06Z2021-01-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/15367engInventions 6 (1): 12 (2021)2411-513410.3390/inventions6010012info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-24T10:27:42Zoai:sapientia.ualg.pt:10400.1/15367Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:06:09.444302Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Short-term forecasting photovoltaic solar power for home energy management systems
title Short-term forecasting photovoltaic solar power for home energy management systems
spellingShingle Short-term forecasting photovoltaic solar power for home energy management systems
Bot, Karol
Photovoltaic power forecasting
Multi-objective genetic algorithms
Artificial neural networks
Home energy management systems
title_short Short-term forecasting photovoltaic solar power for home energy management systems
title_full Short-term forecasting photovoltaic solar power for home energy management systems
title_fullStr Short-term forecasting photovoltaic solar power for home energy management systems
title_full_unstemmed Short-term forecasting photovoltaic solar power for home energy management systems
title_sort Short-term forecasting photovoltaic solar power for home energy management systems
author Bot, Karol
author_facet Bot, Karol
Ruano, Antonio
Ruano, Maria
author_role author
author2 Ruano, Antonio
Ruano, Maria
author2_role author
author
dc.contributor.none.fl_str_mv Sapientia
dc.contributor.author.fl_str_mv Bot, Karol
Ruano, Antonio
Ruano, Maria
dc.subject.por.fl_str_mv Photovoltaic power forecasting
Multi-objective genetic algorithms
Artificial neural networks
Home energy management systems
topic Photovoltaic power forecasting
Multi-objective genetic algorithms
Artificial neural networks
Home energy management systems
description Accurate photovoltaic (PV) power forecasting is crucial to achieving massive PV integration in several areas, which is needed to successfully reduce or eliminate carbon dioxide from energy sources. This paper deals with short-term multi-step PV power forecasts used in model-based predictive control for home energy management systems. By employing radial basis function (RBFs) artificial neural networks (ANN), designed using a multi-objective genetic algorithm (MOGA) with data selected by an approximate convex-hull algorithm, it is shown that excellent forecasting results can be obtained. Two case studies are used: a special house located in the USA, and the other a typical residential house situated in the south of Portugal. In the latter case, one-step-ahead values for unscaled root mean square error (RMSE), mean relative error (MRE), normalized mean average error (NMAE), mean absolute percentage error (MAPE) and R<sup>2</sup> of 0.16, 1.27%, 1.22%, 8% and 0.94 were obtained, respectively. These results compare very favorably with existing alternatives found in the literature.
publishDate 2021
dc.date.none.fl_str_mv 2021-04-12T14:25:04Z
2021-01-25
2021-03-26T14:06:06Z
2021-01-25T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.1/15367
url http://hdl.handle.net/10400.1/15367
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Inventions 6 (1): 12 (2021)
2411-5134
10.3390/inventions6010012
dc.rights.driver.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 MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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