Forecasting Hierarchical Time Series in Power Generation
Academic attention is being paid to the study of hierarchical time series. Especially in the electrical sector, there are several applications in which information can be organized into a hierarchical structure. The present study analyzed hourly power generation in Brazil (2018–2020), grouped accord...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/14/3722 |
| _version_ | 1797561977756712960 |
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| author | Tiago Silveira Gontijo Marcelo Azevedo Costa |
| author_facet | Tiago Silveira Gontijo Marcelo Azevedo Costa |
| author_sort | Tiago Silveira Gontijo |
| collection | DOAJ |
| description | Academic attention is being paid to the study of hierarchical time series. Especially in the electrical sector, there are several applications in which information can be organized into a hierarchical structure. The present study analyzed hourly power generation in Brazil (2018–2020), grouped according to each of the electrical subsystems and their respective sources of generating energy. The objective was to calculate the accuracy of the main measures of aggregating and disaggregating the forecasts of the Autoregressive Integrated Moving Average (ARIMA) and Error, Trend, Seasonal (ETS) models. Specifically, the following hierarchical approaches were analyzed: (i) bottom-up (BU), (ii) top-down (TD), and (iii) optimal reconciliation. The optimal reconciliation models showed the best mean performance, considering the primary predictive windows. It was also found that energy forecasts in the South subsystem presented greater inaccuracy compared to the others, which signals the need for individualized models for this subsystem. |
| first_indexed | 2024-03-10T18:21:27Z |
| format | Article |
| id | doaj.art-d06d045f195b4e259644d49503781ca2 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T18:21:27Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d06d045f195b4e259644d49503781ca22023-11-20T07:18:04ZengMDPI AGEnergies1996-10732020-07-011314372210.3390/en13143722Forecasting Hierarchical Time Series in Power GenerationTiago Silveira Gontijo0Marcelo Azevedo Costa1Graduate Program in Industrial Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte 31270-901, MG, BrazilGraduate Program in Industrial Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte 31270-901, MG, BrazilAcademic attention is being paid to the study of hierarchical time series. Especially in the electrical sector, there are several applications in which information can be organized into a hierarchical structure. The present study analyzed hourly power generation in Brazil (2018–2020), grouped according to each of the electrical subsystems and their respective sources of generating energy. The objective was to calculate the accuracy of the main measures of aggregating and disaggregating the forecasts of the Autoregressive Integrated Moving Average (ARIMA) and Error, Trend, Seasonal (ETS) models. Specifically, the following hierarchical approaches were analyzed: (i) bottom-up (BU), (ii) top-down (TD), and (iii) optimal reconciliation. The optimal reconciliation models showed the best mean performance, considering the primary predictive windows. It was also found that energy forecasts in the South subsystem presented greater inaccuracy compared to the others, which signals the need for individualized models for this subsystem.https://www.mdpi.com/1996-1073/13/14/3722power generationelectrical subsystemstime series |
| spellingShingle | Tiago Silveira Gontijo Marcelo Azevedo Costa Forecasting Hierarchical Time Series in Power Generation Energies power generation electrical subsystems time series |
| title | Forecasting Hierarchical Time Series in Power Generation |
| title_full | Forecasting Hierarchical Time Series in Power Generation |
| title_fullStr | Forecasting Hierarchical Time Series in Power Generation |
| title_full_unstemmed | Forecasting Hierarchical Time Series in Power Generation |
| title_short | Forecasting Hierarchical Time Series in Power Generation |
| title_sort | forecasting hierarchical time series in power generation |
| topic | power generation electrical subsystems time series |
| url | https://www.mdpi.com/1996-1073/13/14/3722 |
| work_keys_str_mv | AT tiagosilveiragontijo forecastinghierarchicaltimeseriesinpowergeneration AT marceloazevedocosta forecastinghierarchicaltimeseriesinpowergeneration |