USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS
Autor(a) principal: | |
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Data de Publicação: | 2016 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Cerne (Online) |
Texto Completo: | https://cerne.ufla.br/site/index.php/CERNE/article/view/902 |
Resumo: | Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state. |
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oai:cerne.ufla.br:article/902 |
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Cerne (Online) |
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spelling |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAISForest economicstime seriesprediction.Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state. CERNECERNE2016-04-05info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://cerne.ufla.br/site/index.php/CERNE/article/view/902CERNE; Vol. 19 No. 2 (2013); 281-288CERNE; v. 19 n. 2 (2013); 281-2882317-63420104-7760reponame:Cerne (Online)instname:Universidade Federal de Lavras (UFLA)instacron:UFLAenghttps://cerne.ufla.br/site/index.php/CERNE/article/view/902/679Copyright (c) 2016 CERNEinfo:eu-repo/semantics/openAccessCoelho Junior, Luiz MoreiraRezende, José Luiz Pereira deBatista, André Luiz FrançaMendonça, Adriano Ribeiro deLacerda, Wilian Soares2016-04-06T09:35:22Zoai:cerne.ufla.br:article/902Revistahttps://cerne.ufla.br/site/index.php/CERNEPUBhttps://cerne.ufla.br/site/index.php/CERNE/oaicerne@dcf.ufla.br||cerne@dcf.ufla.br2317-63420104-7760opendoar:2024-05-21T19:54:10.873051Cerne (Online) - Universidade Federal de Lavras (UFLA)true |
dc.title.none.fl_str_mv |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS |
title |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS |
spellingShingle |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS Coelho Junior, Luiz Moreira Forest economics time series prediction. |
title_short |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS |
title_full |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS |
title_fullStr |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS |
title_full_unstemmed |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS |
title_sort |
USE OF ARTIFICIAL NEURAL NETWORKS FOR PROGNOSIS OF CHARCOAL PRICES IN MINAS GERAIS |
author |
Coelho Junior, Luiz Moreira |
author_facet |
Coelho Junior, Luiz Moreira Rezende, José Luiz Pereira de Batista, André Luiz França Mendonça, Adriano Ribeiro de Lacerda, Wilian Soares |
author_role |
author |
author2 |
Rezende, José Luiz Pereira de Batista, André Luiz França Mendonça, Adriano Ribeiro de Lacerda, Wilian Soares |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Coelho Junior, Luiz Moreira Rezende, José Luiz Pereira de Batista, André Luiz França Mendonça, Adriano Ribeiro de Lacerda, Wilian Soares |
dc.subject.por.fl_str_mv |
Forest economics time series prediction. |
topic |
Forest economics time series prediction. |
description |
Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-04-05 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/902 |
url |
https://cerne.ufla.br/site/index.php/CERNE/article/view/902 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://cerne.ufla.br/site/index.php/CERNE/article/view/902/679 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2016 CERNE info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2016 CERNE |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
CERNE CERNE |
publisher.none.fl_str_mv |
CERNE CERNE |
dc.source.none.fl_str_mv |
CERNE; Vol. 19 No. 2 (2013); 281-288 CERNE; v. 19 n. 2 (2013); 281-288 2317-6342 0104-7760 reponame:Cerne (Online) instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Cerne (Online) |
collection |
Cerne (Online) |
repository.name.fl_str_mv |
Cerne (Online) - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
cerne@dcf.ufla.br||cerne@dcf.ufla.br |
_version_ |
1799874941966876672 |