A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil

Marlon Mesquita Lopes Cabreira, Felipe Leite Coelho da Silva, Josiane da Silva Cordeiro, Ronald Miguel Serrano Hernández, Paulo Canas Rodrigues and Javier Linkolk López-Gonzales ()
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Marlon Mesquita Lopes Cabreira: Department of Mathematics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
Felipe Leite Coelho da Silva: Department of Mathematics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
Josiane da Silva Cordeiro: Department of Mathematics, Federal Rural University of Rio de Janeiro, Seropédica 23890-000, Brazil
Ronald Miguel Serrano Hernández: Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru
Paulo Canas Rodrigues: Department of Statistics, Federal University of Bahia, Salvador 40170-115, Brazil
Javier Linkolk López-Gonzales: Escuela de Posgrado, Universidad Peruana Unión, Lima 15468, Peru

Energies, 2024, vol. 17, issue 13, 1-15

Abstract: The Brazilian industrial sector is the largest electricity consumer in the power system. Energy planning in this sector is important mainly due to its economic, social, and environmental impact. In this context, electricity consumption analysis and projections are highly relevant for the decision-making of the industrial sectorand organizations operating in the energy system. The electricity consumption data from the Brazilian industrial sector can be organized into a hierarchical structure composed of each geographic region (South, Southeast, Midwest, Northeast, and North) and their respective states. This work proposes a hybrid approach that incorporates the projections obtained by the exponential smoothing and Box–Jenkins models to obtain the hierarchical forecasting of electricity consumption in the Brazilian industrial sector. The proposed approach was compared with the bottom-up, top-down, and optimal combination approaches, which are widely used for time series hierarchical forecasting. The performance of the models was evaluated using the mean absolute percentage error (MAPE) and root mean squared error (RMSE) precision measures. The results indicate that the proposed hybrid approach can contribute to the projection and analysis of industrial sector electricity consumption in Brazil.

Keywords: hierarchical forecasting; electricity consumption; time series; forecasting models (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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