Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector
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 (S...
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2024
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| author | Mesquita Lopes Cabreira, Marlon Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane Ureta Tolentino, Jeremias Macias Carbo‐Bustinza, Natalí Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_browse | Canas Rodrigues, Paulo Carbo‐Bustinza, Natalí Leite Coelho da Silva, Felipe López Gonzales, Javier Linkolk Mesquita Lopes Cabreira, Marlon Ureta Tolentino, Jeremias Macias da Silva Cordeiro, Josiane |
| author_facet | Mesquita Lopes Cabreira, Marlon Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane Ureta Tolentino, Jeremias Macias Carbo‐Bustinza, Natalí Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_sort | Mesquita Lopes Cabreira, Marlon |
| collection | Producción científica |
| description | 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%. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/409 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
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| spelling | oai:cris.upeu.edu.pe:20.500.15037/4092026-11-02T22:00:04ZComparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial SectorMesquita Lopes Cabreira, MarlonLeite Coelho da Silva, Felipeda Silva Cordeiro, JosianeUreta Tolentino, Jeremias MaciasCarbo‐Bustinza, NatalíCanas Rodrigues, PauloLópez Gonzales, Javier LinkolkABSTRACT 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%.Articlehttps://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/asmb.2907https://doi.org/10.1002/asmb.290710.1002/asmb.2907https://hdl.handle.net/20.500.15037/409W4404772572enApplied Stochastic Models in Business and Industry1524-19041526-40252024-11-272026-06-13T08:12:56Z2026-06-13T08:12:56Z |
| spellingShingle | Mesquita Lopes Cabreira, Marlon Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane Ureta Tolentino, Jeremias Macias Carbo‐Bustinza, Natalí Canas Rodrigues, Paulo López Gonzales, Javier Linkolk Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector |
| title | Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector |
| title_full | Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector |
| title_fullStr | Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector |
| title_full_unstemmed | Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector |
| title_short | Comparison Between Hierarchical Time Series Forecasting Approaches for the Electricity Consumption in the Brazilian Industrial Sector |
| title_sort | comparison between hierarchical time series forecasting approaches for the electricity consumption in the brazilian industrial sector |
| url | https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/asmb.2907 https://doi.org/10.1002/asmb.2907 https://hdl.handle.net/20.500.15037/409 |
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