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

Author(s)

Puentes, J. A. ^[1] ; Ribeiro, C. O. ^[2] ; Ruelas, E. A. ^[3] ; Figueroa, V. ^[1]

Affiliation

^[1] Tecnol Nacl Mexico Celaya, Guanajuato - Mexico

^[2] Univ Sao Paulo, Sao Paulo, SP - Brazil

^[3] Inst Tecnol Super Irapuato, Guanajuato - Mexico

• Document type: Journal article
• Source: IEEE Latin America Transactions; v. 19, n. 4, p. 551-558, APR 2021.
• Web of Science Citations: 0

Abstract

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)

• FAPESP's process: 15/50684-9 - Sustainable gas pathways for Brazil: from microcosm to macrocosm
• Grantee: Reinaldo Giudici
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 14/50279-4 - Brasil Research Centre for Gas Innovation
• Grantee: Julio Romano Meneghini
• Support Opportunities: Research Grants - Research Centers in Engineering Program