Electrical engineering

Sociology

Autoregressive integrated moving average

Consumption (sociology)

Social science

Machine learning

Time series

Engineering

Autoregressive model

Econometrics

Artificial intelligence

Economics

Multilayer perceptron

Electricity

Energy consumption

Computer science

Artificial neural network

Perceptron

Autoregressive–moving-average model

Energies

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.

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