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Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector
Author(s)
Mesquita Lopes Cabreira, Marlon
Universidade Federal Rural do Rio de Janeiro
Leite Coelho da Silva, Felipe
Universidade Federal Rural do Rio de Janeiro
da Silva Cordeiro, Josiane
Universidade Federal Rural do Rio de Janeiro
Ureta Tolentino, Jeremias Macias
Universidad de Huánuco
Carbo‐Bustinza, Natalí
Canas Rodrigues, Paulo
Universidade Federal da Bahia
Date Issued
27 de noviembre de 2024
Type
Article
Volume
42
Issue
3
Series/Report No.
Applied Stochastic Models in Business and Industry
ISSN
1524-1904
1526-4025
Abstract
ABSTRACT In Brazil, the industrial sector is the largest electricity consumer. Therefore, energy planning becomes important for industrial development. Electricity consumption data in the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Center‐West, Northeast, and North) and their respective states. This work aims to evaluate the predictive capacity of the bottom‐up, top‐down, and optimal combination approaches used to obtain electricity consumption forecasting in the Brazilian industrial sector. These approaches were integrated with the predictive models of exponential smoothing, Box and Jenkins, and neural networks. The results showed that the bottom‐up approach integrated with the Long Short‐Term Memory (LSTM) model provided the best predictions and outperformed the other hierarchical forecasting approaches with an average MAPE of less than 3%.