Time series forecasting via integrating a filtering method: an application to electricity consumption
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 compan...
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| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2025
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| Acceso en línea: | Enlace permanente (DOI o Handle) |
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| _version_ | 1881508987675869184 |
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| author | Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane da Costa, Kleyton Saboya Rios, Nemias Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_browse | Canas Rodrigues, Paulo Leite Coelho da Silva, Felipe López Gonzales, Javier Linkolk Saboya Rios, Nemias da Costa, Kleyton da Silva Cordeiro, Josiane |
| author_facet | Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane da Costa, Kleyton Saboya Rios, Nemias Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_sort | Leite Coelho da Silva, Felipe |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/405 |
| institution | UPeU |
| language | English |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4052026-10-30T16:14:36ZTime series forecasting via integrating a filtering method: an application to electricity consumptionLeite Coelho da Silva, Felipeda Silva Cordeiro, Josianeda Costa, KleytonSaboya Rios, NemiasCanas Rodrigues, PauloLópez Gonzales, Javier LinkolkStatistics, generalProbability and Statistics in Computer ScienceProbability Theory and Stochastic ProcessesEconomic Theory/Quantitative Economics/Mathematical MethodsAnalysis 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.Article10.1007/s00180-024-01595-xhttps://hdl.handle.net/20.500.15037/405W4406346661enComputational Statistics0943-40621613-96582025-01-132026-06-13T08:12:51Z2026-06-13T08:12:51Z |
| spellingShingle | Statistics, general Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory/Quantitative Economics/Mathematical Methods Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane da Costa, Kleyton Saboya Rios, Nemias Canas Rodrigues, Paulo López Gonzales, Javier Linkolk Time series forecasting via integrating a filtering method: an application to electricity consumption |
| title | Time series forecasting via integrating a filtering method: an application to electricity consumption |
| title_full | Time series forecasting via integrating a filtering method: an application to electricity consumption |
| title_fullStr | Time series forecasting via integrating a filtering method: an application to electricity consumption |
| title_full_unstemmed | Time series forecasting via integrating a filtering method: an application to electricity consumption |
| title_short | Time series forecasting via integrating a filtering method: an application to electricity consumption |
| title_sort | time series forecasting via integrating a filtering method an application to electricity consumption |
| topic | Statistics, general Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory/Quantitative Economics/Mathematical Methods |
| topic_facet | Statistics, general Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory/Quantitative Economics/Mathematical Methods |
| url | https://hdl.handle.net/20.500.15037/405 |
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