Forecasting electricity generation from renewable sources during a pandemic

Detalhes bibliográficos
Autor(a) principal: Reichert,Bianca
Data de Publicação: 2022
Outros Autores: Souza,Adriano Mendonça, Mezzomo,Meiri
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Gestão & Produção
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100206
Resumo: Abstract Renewable sources are responsible for more than half of Brazilian electric generation, which basically correspond to hydropower, biomass and wind sources. This research aimed to verify if the Autoregressive Integrated Moving Average (ARIMA) models present good performance in predicting electricity generation from biomass, hydropower and wind power for the first months of COVID-19 pandemic in Brazil. The best forecasting models adjusted for biomass, hydropower and wind generation was the SARIMA, since this model was able to identify seasonal effects of climatic instability, such as periods of drought. Based on the seasonality of the largest generating sources, renewable generation needs to be offset by other sources, as non-renewable, and more efforts are needed to make Brazilian electric matrix more sustainable.
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spelling Forecasting electricity generation from renewable sources during a pandemicARIMA modelsRenewable sourcesTime seriesCOVID-19Abstract Renewable sources are responsible for more than half of Brazilian electric generation, which basically correspond to hydropower, biomass and wind sources. This research aimed to verify if the Autoregressive Integrated Moving Average (ARIMA) models present good performance in predicting electricity generation from biomass, hydropower and wind power for the first months of COVID-19 pandemic in Brazil. The best forecasting models adjusted for biomass, hydropower and wind generation was the SARIMA, since this model was able to identify seasonal effects of climatic instability, such as periods of drought. Based on the seasonality of the largest generating sources, renewable generation needs to be offset by other sources, as non-renewable, and more efforts are needed to make Brazilian electric matrix more sustainable.Universidade Federal de São Carlos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100206Gestão & Produção v.29 2022reponame:Gestão & Produçãoinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCAR10.1590/1806-9649-2022v29e024info:eu-repo/semantics/openAccessReichert,BiancaSouza,Adriano MendonçaMezzomo,Meirieng2022-03-08T00:00:00Zoai:scielo:S0104-530X2022000100206Revistahttps://www.gestaoeproducao.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpgp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br1806-96490104-530Xopendoar:2022-03-08T00:00Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)false
dc.title.none.fl_str_mv Forecasting electricity generation from renewable sources during a pandemic
title Forecasting electricity generation from renewable sources during a pandemic
spellingShingle Forecasting electricity generation from renewable sources during a pandemic
Reichert,Bianca
ARIMA models
Renewable sources
Time series
COVID-19
title_short Forecasting electricity generation from renewable sources during a pandemic
title_full Forecasting electricity generation from renewable sources during a pandemic
title_fullStr Forecasting electricity generation from renewable sources during a pandemic
title_full_unstemmed Forecasting electricity generation from renewable sources during a pandemic
title_sort Forecasting electricity generation from renewable sources during a pandemic
author Reichert,Bianca
author_facet Reichert,Bianca
Souza,Adriano Mendonça
Mezzomo,Meiri
author_role author
author2 Souza,Adriano Mendonça
Mezzomo,Meiri
author2_role author
author
dc.contributor.author.fl_str_mv Reichert,Bianca
Souza,Adriano Mendonça
Mezzomo,Meiri
dc.subject.por.fl_str_mv ARIMA models
Renewable sources
Time series
COVID-19
topic ARIMA models
Renewable sources
Time series
COVID-19
description Abstract Renewable sources are responsible for more than half of Brazilian electric generation, which basically correspond to hydropower, biomass and wind sources. This research aimed to verify if the Autoregressive Integrated Moving Average (ARIMA) models present good performance in predicting electricity generation from biomass, hydropower and wind power for the first months of COVID-19 pandemic in Brazil. The best forecasting models adjusted for biomass, hydropower and wind generation was the SARIMA, since this model was able to identify seasonal effects of climatic instability, such as periods of drought. Based on the seasonality of the largest generating sources, renewable generation needs to be offset by other sources, as non-renewable, and more efforts are needed to make Brazilian electric matrix more sustainable.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100206
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100206
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/1806-9649-2022v29e024
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Universidade Federal de São Carlos
publisher.none.fl_str_mv Universidade Federal de São Carlos
dc.source.none.fl_str_mv Gestão & Produção v.29 2022
reponame:Gestão & Produção
instname:Universidade Federal de São Carlos (UFSCAR)
instacron:UFSCAR
instname_str Universidade Federal de São Carlos (UFSCAR)
instacron_str UFSCAR
institution UFSCAR
reponame_str Gestão & Produção
collection Gestão & Produção
repository.name.fl_str_mv Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)
repository.mail.fl_str_mv gp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br
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