A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil
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-ma...
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| Formato: | Artículo |
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2024
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| _version_ | 1881508988341714944 |
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| author | Mesquita Lopes Cabreira, Marlon Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane Serrano Hernández, Ronald Miguel Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_browse | Canas Rodrigues, Paulo Leite Coelho da Silva, Felipe López Gonzales, Javier Linkolk Mesquita Lopes Cabreira, Marlon Serrano Hernández, Ronald Miguel da Silva Cordeiro, Josiane |
| author_facet | Mesquita Lopes Cabreira, Marlon Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane Serrano Hernández, Ronald Miguel Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_sort | Mesquita Lopes Cabreira, Marlon |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/415 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4152026-10-30T16:14:37ZA Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in BrazilMesquita Lopes Cabreira, MarlonLeite Coelho da Silva, Felipeda Silva Cordeiro, JosianeSerrano Hernández, Ronald MiguelCanas Rodrigues, PauloLópez Gonzales, Javier Linkolkhierarchical forecastingelectricity consumptiontime seriesforecasting modelsThe 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.Articlehttps://www.mdpi.com/1996-1073/17/13/3200/pdf?version=1720175683https://doi.org/10.3390/en1713320010.3390/en17133200https://hdl.handle.net/20.500.15037/415W4400200860enEnergies1996-10732024-06-292026-06-13T08:13:04Z2026-06-13T08:13:04Z |
| spellingShingle | hierarchical forecasting electricity consumption time series forecasting models Mesquita Lopes Cabreira, Marlon Leite Coelho da Silva, Felipe da Silva Cordeiro, Josiane Serrano Hernández, Ronald Miguel Canas Rodrigues, Paulo López Gonzales, Javier Linkolk A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil |
| title | A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil |
| title_full | A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil |
| title_fullStr | A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil |
| title_full_unstemmed | A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil |
| title_short | A Hybrid Approach for Hierarchical Forecasting of Industrial Electricity Consumption in Brazil |
| title_sort | hybrid approach for hierarchical forecasting of industrial electricity consumption in brazil |
| topic | hierarchical forecasting electricity consumption time series forecasting models |
| topic_facet | hierarchical forecasting electricity consumption time series forecasting models |
| url | https://www.mdpi.com/1996-1073/17/13/3200/pdf?version=1720175683 https://doi.org/10.3390/en17133200 https://hdl.handle.net/20.500.15037/415 |
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