An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience
The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/21/7444 |
| _version_ | 1797631919610920960 |
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| author | Giancarlo Aquila Lucas Barros Scianni Morais Victor Augusto Durães de Faria José Wanderley Marangon Lima Luana Medeiros Marangon Lima Anderson Rodrigo de Queiroz |
| author_facet | Giancarlo Aquila Lucas Barros Scianni Morais Victor Augusto Durães de Faria José Wanderley Marangon Lima Luana Medeiros Marangon Lima Anderson Rodrigo de Queiroz |
| author_sort | Giancarlo Aquila |
| collection | DOAJ |
| description | The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience. |
| first_indexed | 2024-03-11T11:30:30Z |
| format | Article |
| id | doaj.art-311a5ca7fd0d436087bb0993f4506d47 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T11:30:30Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-311a5ca7fd0d436087bb0993f4506d472023-11-10T15:02:34ZengMDPI AGEnergies1996-10732023-11-011621744410.3390/en16217444An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian ExperienceGiancarlo Aquila0Lucas Barros Scianni Morais1Victor Augusto Durães de Faria2José Wanderley Marangon Lima3Luana Medeiros Marangon Lima4Anderson Rodrigo de Queiroz5Institute of Production Engineering and Management, Federal University of Itajubá, Itajubá 37500-903, MG, BrazilInstitute of Electrical and Energy Systems, Federal University of Itajubá, Itajubá 37500-903, MG, BrazilGraduate Program on Operations Research, NC State University, Raleigh, NC 27606, USAInstitute of Electrical and Energy Systems, Federal University of Itajubá, Itajubá 37500-903, MG, BrazilNicholas School of Environment, Duke University, Durham, NC 27708, USAGraduate Program on Operations Research, NC State University, Raleigh, NC 27606, USAThe advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience.https://www.mdpi.com/1996-1073/16/21/7444short-term load forecastingday-ahead operational planningtime series forecastingmachine learning methodselectricity power systems |
| spellingShingle | Giancarlo Aquila Lucas Barros Scianni Morais Victor Augusto Durães de Faria José Wanderley Marangon Lima Luana Medeiros Marangon Lima Anderson Rodrigo de Queiroz An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience Energies short-term load forecasting day-ahead operational planning time series forecasting machine learning methods electricity power systems |
| title | An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience |
| title_full | An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience |
| title_fullStr | An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience |
| title_full_unstemmed | An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience |
| title_short | An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience |
| title_sort | overview of short term load forecasting for electricity systems operational planning machine learning methods and the brazilian experience |
| topic | short-term load forecasting day-ahead operational planning time series forecasting machine learning methods electricity power systems |
| url | https://www.mdpi.com/1996-1073/16/21/7444 |
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