Forecasting the Brazilian demand for biodiesel using artificial neural networks
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Data de Publicação: | 2021 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/13381 |
Resumo: | Biodiesel is a renewable fuel used as an alternative to totally or partially replace petroleum diesel. The mandatory percentage of this biofuel added to fossil diesel in Brazil has constantly been increasing. Predicting the amount of biodiesel that will be demanded in the future is essential to maintain the national surplus balance and assist in the sector decision-making. Artificial neural networks (ANNs) help forecast different types of demands. Therefore, this study uses artificial neural networks to forecast the Brazilian demand for biodiesel. The ANN proposed in this work encompassed data obtained from a non-parametric demand forecasting model based on time series. The non-parametric model considered the trends and seasonality of the data to forecast the demand for biodiesel. One hundred multilayer perceptron networks were modeled with error propagation for two scenarios of Brazilian biodiesel (use of 15% (B15) or 20% (B20) of biodiesel to diesel). All values of R2 greater than 0.99 for the simulated networks and RMSE <2% prove that the RNA model developed has high precision in predicting the demand for biodiesel. The best network for each scenario was determined by RMSE heuristic analysis. The best-simulated RNA results showed growth in biodiesel demand from 2019 to 2050 of 150.63% for B15. And 229.73% for B20. Both demand growth scenarios are justified by the gradual increase in the mandatory percentage of biodiesel to diesel. Thus, the growing results of biodiesel demand prove the country search for a non-toxic, biodegradable, and renewable fuel in its energy matrix. |
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Forecasting the Brazilian demand for biodiesel using artificial neural networks Pronóstico de la demanda brasileña de biodiésel mediante redes neuronales artificiales Previsão da demanda brasileira de biodiesel utilizando redes neurais artificiais BiodieselBrasilRedes neurais artificiaisSazonalidadePrevisão de demanda.BiodieselBrazilArtificial neural networksSeasonalityDemand forecast.BiodiéselBrasilRedes neuronales artificialesEstacionalidadPrevisión de demanda.Biodiesel is a renewable fuel used as an alternative to totally or partially replace petroleum diesel. The mandatory percentage of this biofuel added to fossil diesel in Brazil has constantly been increasing. Predicting the amount of biodiesel that will be demanded in the future is essential to maintain the national surplus balance and assist in the sector decision-making. Artificial neural networks (ANNs) help forecast different types of demands. Therefore, this study uses artificial neural networks to forecast the Brazilian demand for biodiesel. The ANN proposed in this work encompassed data obtained from a non-parametric demand forecasting model based on time series. The non-parametric model considered the trends and seasonality of the data to forecast the demand for biodiesel. One hundred multilayer perceptron networks were modeled with error propagation for two scenarios of Brazilian biodiesel (use of 15% (B15) or 20% (B20) of biodiesel to diesel). All values of R2 greater than 0.99 for the simulated networks and RMSE <2% prove that the RNA model developed has high precision in predicting the demand for biodiesel. The best network for each scenario was determined by RMSE heuristic analysis. The best-simulated RNA results showed growth in biodiesel demand from 2019 to 2050 of 150.63% for B15. And 229.73% for B20. Both demand growth scenarios are justified by the gradual increase in the mandatory percentage of biodiesel to diesel. Thus, the growing results of biodiesel demand prove the country search for a non-toxic, biodegradable, and renewable fuel in its energy matrix.El biodiésel es un combustible renovable que se utiliza como alternativa para sustituir total o parcialmente el diésel de petróleo. El porcentaje obligatorio de este biocombustible agregado al diesel fósil en Brasil ha aumentado constantemente. Predecir la cantidad de biodiésel que se demandará en el futuro es fundamental para mantener el saldo excedentario nacional y ayudar en la toma de decisiones del sector. Las redes neuronales artificiales (ANN) son útiles para pronosticar diferentes tipos de demandas. Por lo tanto, este estudio utiliza redes neuronales artificiales para pronosticar la demanda brasileña de biodiesel. La ANN propuesta en este trabajo abarcó datos obtenidos de un modelo de pronóstico de demanda no paramétrico basado en series de tiempo. El modelo no paramétrico consideró las tendencias y la estacionalidad de los datos para pronosticar la demanda de biodiesel. Se modelaron 100 redes de perceptrones multicapa con propagación de errores para dos escenarios de biodiesel brasileño (uso de 15% (B15) o 20% (B20) de biodiesel a diesel). Todos los valores de R2 superiores a 0,99 para las redes simuladas y RMSE <2% demuestran que el modelo de ARN desarrollado tiene una alta precisión en la predicción de la demanda de biodiésel. La mejor red para cada escenario se determinó mediante análisis heurístico de RMSE. Los resultados de los mejores ARN simulados mostraron un crecimiento en la demanda de biodiésel de 2019 a 2050 de 150,63% para B15 y 229,73% para B20. Ambos escenarios de crecimiento de la demanda se justifican por el aumento gradual del porcentaje obligatorio de biodiésel a diésel. Así, los crecientes resultados de la demanda de biodiesel evidencian la búsqueda del país de un combustible no tóxico, biodegradable y renovable en su matriz energética.O biodiesel é um combustível renovável utilizado como uma alternativa para substituir de modo total ou parcial o diesel de petróleo. A porcentagem obrigatória desse biocombustível adicionado ao diesel fóssil no Brasil tem sido elevada constantemente. Prever a quantidade de biodiesel que será demandada futuramente é essencial para manter o balanço nacional superavitário e auxiliar nas tomadas de decisões do setor. As redes neurais artificiais (RNAs) são úteis para previsão de diferentes tipos de demandas. Assim sendo, esse estudo utiliza redes neurais artificiais na previsão da demanda brasileira de biodiesel. A RNA proposta neste trabalho englobou dados obtidos de um modelo não-paramétrico de previsão de demanda baseado em séries temporais. O modelo não-paramétrico considerou as tendências e sazonalidade dos dados para previsão da demanda de biodiesel. Foram modeladas 100 redes do tipo perceptron multicamadas com retropropagação do erro para dois cenários do biodiesel brasileiro (uso de 15% (B15) ou 20% (B20) de biodiesel ao diesel). Todos os valores de R2 maiores que 0,99 para as redes simuladas e RMSE<2% comprovam que o modelo de RNA desenvolvido possui alta precisão em prever a demanda de biodiesel. A melhor rede para cada cenário foi determinada por análise heurística do RMSE. Os resultados das melhores RNA’s simuladas mostraram um crescimento da demanda de biodiesel de 2019 a 2050 de 150,63% para o B15, e de 229,73% para o B20. Ambos cenários de aumento de demanda são justificados pela elevação gradual da porcentagem obrigatória do biodiesel ao diesel. Dessa forma, os resultados crescentes da demanda de biodiesel comprovam a busca do país por um combustível não-tóxico, biodegradável e renovável na sua matriz energética.Research, Society and Development2021-05-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1338110.33448/rsd-v10i5.13381Research, Society and Development; Vol. 10 No. 5; e17410513381Research, Society and Development; Vol. 10 Núm. 5; e17410513381Research, Society and Development; v. 10 n. 5; e174105133812525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIporhttps://rsdjournal.org/index.php/rsd/article/view/13381/13283Copyright (c) 2021 Kaique Vitor Louzada Caires; George Simonellihttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessCaires, Kaique Vitor Louzada Simonelli, George 2021-05-17T18:20:49Zoai:ojs.pkp.sfu.ca:article/13381Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:34:44.275998Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Forecasting the Brazilian demand for biodiesel using artificial neural networks Pronóstico de la demanda brasileña de biodiésel mediante redes neuronales artificiales Previsão da demanda brasileira de biodiesel utilizando redes neurais artificiais |
title |
Forecasting the Brazilian demand for biodiesel using artificial neural networks |
spellingShingle |
Forecasting the Brazilian demand for biodiesel using artificial neural networks Caires, Kaique Vitor Louzada Biodiesel Brasil Redes neurais artificiais Sazonalidade Previsão de demanda. Biodiesel Brazil Artificial neural networks Seasonality Demand forecast. Biodiésel Brasil Redes neuronales artificiales Estacionalidad Previsión de demanda. |
title_short |
Forecasting the Brazilian demand for biodiesel using artificial neural networks |
title_full |
Forecasting the Brazilian demand for biodiesel using artificial neural networks |
title_fullStr |
Forecasting the Brazilian demand for biodiesel using artificial neural networks |
title_full_unstemmed |
Forecasting the Brazilian demand for biodiesel using artificial neural networks |
title_sort |
Forecasting the Brazilian demand for biodiesel using artificial neural networks |
author |
Caires, Kaique Vitor Louzada |
author_facet |
Caires, Kaique Vitor Louzada Simonelli, George |
author_role |
author |
author2 |
Simonelli, George |
author2_role |
author |
dc.contributor.author.fl_str_mv |
Caires, Kaique Vitor Louzada Simonelli, George |
dc.subject.por.fl_str_mv |
Biodiesel Brasil Redes neurais artificiais Sazonalidade Previsão de demanda. Biodiesel Brazil Artificial neural networks Seasonality Demand forecast. Biodiésel Brasil Redes neuronales artificiales Estacionalidad Previsión de demanda. |
topic |
Biodiesel Brasil Redes neurais artificiais Sazonalidade Previsão de demanda. Biodiesel Brazil Artificial neural networks Seasonality Demand forecast. Biodiésel Brasil Redes neuronales artificiales Estacionalidad Previsión de demanda. |
description |
Biodiesel is a renewable fuel used as an alternative to totally or partially replace petroleum diesel. The mandatory percentage of this biofuel added to fossil diesel in Brazil has constantly been increasing. Predicting the amount of biodiesel that will be demanded in the future is essential to maintain the national surplus balance and assist in the sector decision-making. Artificial neural networks (ANNs) help forecast different types of demands. Therefore, this study uses artificial neural networks to forecast the Brazilian demand for biodiesel. The ANN proposed in this work encompassed data obtained from a non-parametric demand forecasting model based on time series. The non-parametric model considered the trends and seasonality of the data to forecast the demand for biodiesel. One hundred multilayer perceptron networks were modeled with error propagation for two scenarios of Brazilian biodiesel (use of 15% (B15) or 20% (B20) of biodiesel to diesel). All values of R2 greater than 0.99 for the simulated networks and RMSE <2% prove that the RNA model developed has high precision in predicting the demand for biodiesel. The best network for each scenario was determined by RMSE heuristic analysis. The best-simulated RNA results showed growth in biodiesel demand from 2019 to 2050 of 150.63% for B15. And 229.73% for B20. Both demand growth scenarios are justified by the gradual increase in the mandatory percentage of biodiesel to diesel. Thus, the growing results of biodiesel demand prove the country search for a non-toxic, biodegradable, and renewable fuel in its energy matrix. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-05-02 |
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://rsdjournal.org/index.php/rsd/article/view/13381 10.33448/rsd-v10i5.13381 |
url |
https://rsdjournal.org/index.php/rsd/article/view/13381 |
identifier_str_mv |
10.33448/rsd-v10i5.13381 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/13381/13283 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2021 Kaique Vitor Louzada Caires; George Simonelli https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2021 Kaique Vitor Louzada Caires; George Simonelli https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 5; e17410513381 Research, Society and Development; Vol. 10 Núm. 5; e17410513381 Research, Society and Development; v. 10 n. 5; e17410513381 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
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1797052672390463488 |