Time series forecasting via integrating a filtering method: an application to electricity consumption

Felipe Leite Coelho da Silva (), Josiane da Silva Cordeiro (), Kleyton da Costa (), Nemias Saboya (), Paulo Canas Rodrigues () and Javier Linkolk López-Gonzales ()
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Felipe Leite Coelho da Silva: Federal Rural University of Rio de Janeiro
Josiane da Silva Cordeiro: Federal Rural University of Rio de Janeiro
Kleyton da Costa: Pontifical Catholic University of Rio de Janeiro
Nemias Saboya: Universidad Peruana Unión
Paulo Canas Rodrigues: Federal University of Bahia
Javier Linkolk López-Gonzales: Universidad Peruana Unión

Computational Statistics, 2025, vol. 40, issue 9, No 6, 5023-5042

Abstract: Abstract Analysis and forecasting of the industrial sector electricity consumption is important for energy planning and control, in addition to being essential for the developing of a country or region. In this context, electricity consumption projections are highly relevant for the decision-making of companies operating in energy systems with the aim of optimizing such operations. This paper proposes a methodology for forecasting electricity consumption by integrating a time series filtering method with any classical forecasting model. In particular, in order to evaluate the performance of the proposed methodology it was used the Seasonal and Trend decomposition using Loess (STL) decomposition method integrated with several classical statistical models (Holt-Winters, seasonal autoregressive integrated moving average, dynamic linear model, and TBATS) and the artificial neural networks approach (NNAR - neural network autoregression, MLP - multi-layer perceptron, and LSTM - long short-term memory). The methodology was applied to the electricity consumption of Brazil’s cement industry. Based on the MAPE and RMSE precision metrics, the proposed methodology obtained the best performance for the time series under analysis via introducing the filtering method. The LSTM model integrated with the STL decomposition presented the best results among the models considered.

Keywords: Forecast model; Statistical models; Artificial neural networks; Energy demand; Industrial sector (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01595-x

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