FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING

Detalhes bibliográficos
Autor(a) principal: Luna,Ivette
Data de Publicação: 2017
Outros Autores: Hidalgo,Ieda G., Pedro,Paulo S.M., Barbosa,Paulo S.F., Francato,Alberto L., Correia,Paulo B.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Pesquisa operacional (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000100129
Resumo: ABSTRACT This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.
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spelling FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTINGinflow forecastinghydroelectric plantsfuzzy inference systemsABSTRACT This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.Sociedade Brasileira de Pesquisa Operacional2017-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000100129Pesquisa Operacional v.37 n.1 2017reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/0101-7438.2017.037.01.0129info:eu-repo/semantics/openAccessLuna,IvetteHidalgo,Ieda G.Pedro,Paulo S.M.Barbosa,Paulo S.F.Francato,Alberto L.Correia,Paulo B.eng2017-06-05T00:00:00Zoai:scielo:S0101-74382017000100129Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2017-06-05T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false
dc.title.none.fl_str_mv FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
title FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
spellingShingle FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
Luna,Ivette
inflow forecasting
hydroelectric plants
fuzzy inference systems
title_short FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
title_full FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
title_fullStr FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
title_full_unstemmed FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
title_sort FUZZY INFERENCE SYSTEMS FOR MULTI-STEP AHEAD DAILY INFLOW FORECASTING
author Luna,Ivette
author_facet Luna,Ivette
Hidalgo,Ieda G.
Pedro,Paulo S.M.
Barbosa,Paulo S.F.
Francato,Alberto L.
Correia,Paulo B.
author_role author
author2 Hidalgo,Ieda G.
Pedro,Paulo S.M.
Barbosa,Paulo S.F.
Francato,Alberto L.
Correia,Paulo B.
author2_role author
author
author
author
author
dc.contributor.author.fl_str_mv Luna,Ivette
Hidalgo,Ieda G.
Pedro,Paulo S.M.
Barbosa,Paulo S.F.
Francato,Alberto L.
Correia,Paulo B.
dc.subject.por.fl_str_mv inflow forecasting
hydroelectric plants
fuzzy inference systems
topic inflow forecasting
hydroelectric plants
fuzzy inference systems
description ABSTRACT This paper presents the evaluation of a daily inflow forecasting model using a tool that facilitates the analysis of mathematical models for hydroelectric plants. The model is based on a Fuzzy Inference System. An offline version of the Expectation Maximization algorithm is employed to adjust the model parameters. The tool integrates different inflow forecasting models into a single physical structure. It makes uniform and streamlines the management of data, prediction studies, and presentation of results. A case study is carried out using data from three Brazilian hydroelectric plants of the Parana basin, Tiete River, in southern Brazil. Their activities are coordinated by Operator of the National Electric System (ONS) and inspected by the National Agency for Electricity (ANEEL). The model is evaluated considering a multi-step ahead forecasting task. The graphs allow a comparison between observed and forecasted inflows. For statistical analysis, it is used the mean absolute percentage error, the root mean square error, the mean absolute error, and the mass curve coefficient. The results show an adequate performance of the model, leading to a promising alternative for daily inflow forecasting.
publishDate 2017
dc.date.none.fl_str_mv 2017-01-01
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=S0101-74382017000100129
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382017000100129
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/0101-7438.2017.037.01.0129
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 Sociedade Brasileira de Pesquisa Operacional
publisher.none.fl_str_mv Sociedade Brasileira de Pesquisa Operacional
dc.source.none.fl_str_mv Pesquisa Operacional v.37 n.1 2017
reponame:Pesquisa operacional (Online)
instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron:SOBRAPO
instname_str Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
instacron_str SOBRAPO
institution SOBRAPO
reponame_str Pesquisa operacional (Online)
collection Pesquisa operacional (Online)
repository.name.fl_str_mv Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)
repository.mail.fl_str_mv ||sobrapo@sobrapo.org.br
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