Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector

Felipe Leite Coelho da Silva, Kleyton da Costa, Paulo Canas Rodrigues, Rodrigo Salas and Javier Linkolk López-Gonzales
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Felipe Leite Coelho da Silva: Department of Mathematics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
Kleyton da Costa: Department of Economics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
Paulo Canas Rodrigues: Departament of Statistics, Federal University of Bahia, Salvador 40170-110, Brazil
Rodrigo Salas: Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Valparaíso 2362905, Chile
Javier Linkolk López-Gonzales: Facultad de Ingeniería y Arquitectura, Universidad Peruana Unión, Lima 15, Peru

Energies, 2022, vol. 15, issue 2, 1-12

Abstract: Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.

Keywords: energy planning; forecasting; industrial electricity consumption; artificial neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (13)

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