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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Ethanol Fuel Demand Forecasting in Brazil Using a LSTM Recurrent Neural Network Approach

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Autor(es):
Puentes, J. A. [1] ; Ribeiro, C. O. [2] ; Ruelas, E. A. [3] ; Figueroa, V. [1]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Tecnol Nacl Mexico Celaya, Guanajuato - Mexico
[2] Univ Sao Paulo, Sao Paulo, SP - Brazil
[3] Inst Tecnol Super Irapuato, Guanajuato - Mexico
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: IEEE Latin America Transactions; v. 19, n. 4, p. 551-558, APR 2021.
Citações Web of Science: 0
Resumo

Ethanol is a biofuel widely consumed in Brazil, which functions as a substitute for gasoline since the late 1970s. Due to several fluctuations in the characteristics of the Brazilian vehicle fleet and the political-economic conditions of the country, forecasting ethanol consumption has become a difficult task to perform. Under this scenario, the aim of this paper was to forecast ethanol consumption in Brazil using an approach of Long-Short Term Memory (LSTM) Recurrent Neural Networks (RNN) and Autoregressive Integrated Moving Average (ARIMA) models. The above, taking into consideration univariate and multivariate models for each case. Likewise, single-layer and multi-layer topologies of LSTM RNN were explored in this study. The results show that LSTM models overperformed ARIMA models even working with a relatively small training dataset of just 180 instances. This, for both univariate and multivariate models. A novel approach for searching suitable LSTM Neural Network topologies is proposed in this paper. (AU)

Processo FAPESP: 15/50684-9 - Sustainable gas pathways for Brazil: from microcosm to macrocosm
Beneficiário:Reinaldo Giudici
Modalidade de apoio: Auxílio à Pesquisa - Temático
Processo FAPESP: 14/50279-4 - Brasil Research Centre for Gas Innovation
Beneficiário:Julio Romano Meneghini
Modalidade de apoio: Auxílio à Pesquisa - Programa Centros de Pesquisa em Engenharia