Short term load forecasting for power exchange between Brasil and Paraguay
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , |
Tipo de documento: | Artigo de conferência |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/SBSE.2018.8395739 http://hdl.handle.net/11449/171238 |
Resumo: | This work presents a case study of short term load forecasting to assist in the power exchange real time dispatch operation between Brazil and Paraguay at Itaipu Dam. A classical method with statistical approach, Seasonal Autoregressive Moving Average, is compared with an artificial intelligence method based on Artificial Neural Networks. The methods are tested using a time series representing the average hourly power exchange. The results were compared with the current forecast methods used to define the daily program of operation of Itaipu using the Mean Absolute Percentage Error method. The results of the analysis showed that the model based on the Seasonal Autoregressive Moving Average present a lower error index among the methods tested. |
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Short term load forecasting for power exchange between Brasil and ParaguayArtificial neural netowrksLoad forecastingOperation planningSARIMATime seriesThis work presents a case study of short term load forecasting to assist in the power exchange real time dispatch operation between Brazil and Paraguay at Itaipu Dam. A classical method with statistical approach, Seasonal Autoregressive Moving Average, is compared with an artificial intelligence method based on Artificial Neural Networks. The methods are tested using a time series representing the average hourly power exchange. The results were compared with the current forecast methods used to define the daily program of operation of Itaipu using the Mean Absolute Percentage Error method. The results of the analysis showed that the model based on the Seasonal Autoregressive Moving Average present a lower error index among the methods tested.Itaipu Binacional Foz Do IguaçuUNIOESTEUNESPUNESPItaipu Binacional Foz Do IguaçuUNIOESTEUniversidade Estadual Paulista (Unesp)Pimenta, R. G.Gaio, G.Franco, E. M.C.Muller, M. R. [UNESP]2018-12-11T16:54:31Z2018-12-11T16:54:31Z2018-06-25info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject1-6http://dx.doi.org/10.1109/SBSE.2018.8395739SBSE 2018 - 7th Brazilian Electrical Systems Symposium, p. 1-6.http://hdl.handle.net/11449/17123810.1109/SBSE.2018.83957392-s2.0-85050252216Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporSBSE 2018 - 7th Brazilian Electrical Systems Symposiuminfo:eu-repo/semantics/openAccess2021-10-23T21:44:37Zoai:repositorio.unesp.br:11449/171238Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:22:27.478610Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Short term load forecasting for power exchange between Brasil and Paraguay |
title |
Short term load forecasting for power exchange between Brasil and Paraguay |
spellingShingle |
Short term load forecasting for power exchange between Brasil and Paraguay Pimenta, R. G. Artificial neural netowrks Load forecasting Operation planning SARIMA Time series |
title_short |
Short term load forecasting for power exchange between Brasil and Paraguay |
title_full |
Short term load forecasting for power exchange between Brasil and Paraguay |
title_fullStr |
Short term load forecasting for power exchange between Brasil and Paraguay |
title_full_unstemmed |
Short term load forecasting for power exchange between Brasil and Paraguay |
title_sort |
Short term load forecasting for power exchange between Brasil and Paraguay |
author |
Pimenta, R. G. |
author_facet |
Pimenta, R. G. Gaio, G. Franco, E. M.C. Muller, M. R. [UNESP] |
author_role |
author |
author2 |
Gaio, G. Franco, E. M.C. Muller, M. R. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Itaipu Binacional Foz Do Iguaçu UNIOESTE Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Pimenta, R. G. Gaio, G. Franco, E. M.C. Muller, M. R. [UNESP] |
dc.subject.por.fl_str_mv |
Artificial neural netowrks Load forecasting Operation planning SARIMA Time series |
topic |
Artificial neural netowrks Load forecasting Operation planning SARIMA Time series |
description |
This work presents a case study of short term load forecasting to assist in the power exchange real time dispatch operation between Brazil and Paraguay at Itaipu Dam. A classical method with statistical approach, Seasonal Autoregressive Moving Average, is compared with an artificial intelligence method based on Artificial Neural Networks. The methods are tested using a time series representing the average hourly power exchange. The results were compared with the current forecast methods used to define the daily program of operation of Itaipu using the Mean Absolute Percentage Error method. The results of the analysis showed that the model based on the Seasonal Autoregressive Moving Average present a lower error index among the methods tested. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-12-11T16:54:31Z 2018-12-11T16:54:31Z 2018-06-25 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1109/SBSE.2018.8395739 SBSE 2018 - 7th Brazilian Electrical Systems Symposium, p. 1-6. http://hdl.handle.net/11449/171238 10.1109/SBSE.2018.8395739 2-s2.0-85050252216 |
url |
http://dx.doi.org/10.1109/SBSE.2018.8395739 http://hdl.handle.net/11449/171238 |
identifier_str_mv |
SBSE 2018 - 7th Brazilian Electrical Systems Symposium, p. 1-6. 10.1109/SBSE.2018.8395739 2-s2.0-85050252216 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
SBSE 2018 - 7th Brazilian Electrical Systems Symposium |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-6 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128799965970432 |