Intelligent Systems for Power Load Forecasting: A Study Review
The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/22/6105 |
| _version_ | 1827701738789928960 |
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| author | Ibrahim Salem Jahan Vaclav Snasel Stanislav Misak |
| author_facet | Ibrahim Salem Jahan Vaclav Snasel Stanislav Misak |
| author_sort | Ibrahim Salem Jahan |
| collection | DOAJ |
| description | The study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study. |
| first_indexed | 2024-03-10T14:39:37Z |
| format | Article |
| id | doaj.art-1704432006a141e0bdb89632d05fed9b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T14:39:37Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1704432006a141e0bdb89632d05fed9b2023-11-20T21:50:36ZengMDPI AGEnergies1996-10732020-11-011322610510.3390/en13226105Intelligent Systems for Power Load Forecasting: A Study ReviewIbrahim Salem Jahan0Vaclav Snasel1Stanislav Misak2ENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech RepublicComputer Science Department, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech RepublicENET Centre, VSB—Technical University of Ostrava, 708 00 Ostrava, Czech RepublicThe study of power load forecasting is gaining greater significance nowadays, particularly with the use and integration of renewable power sources and external power stations. Power forecasting is an important task in the planning, control, and operation of utility power systems. In addition, load forecasting (LF) aims to estimate the power or energy needed to meet the required power or energy to supply the specific load. In this article, we introduce, review and compare different power load forecasting techniques. Our goal is to help in the process of explaining the problem of power load forecasting via brief descriptions of the proposed methods applied in the last decade. The study reviews previous research that deals with the design of intelligent systems for power forecasting using various methods. The methods are organized into five groups—Artificial Neural Network (ANN), Support Vector Regression, Decision Tree (DT), Linear Regression (LR), and Fuzzy Sets (FS). This way, the review provides a clear concept of power load forecasting for the purposes of future research and study.https://www.mdpi.com/1996-1073/13/22/6105renewable energy sourcesload forecastingsmart systemweather dataoff-grid system |
| spellingShingle | Ibrahim Salem Jahan Vaclav Snasel Stanislav Misak Intelligent Systems for Power Load Forecasting: A Study Review Energies renewable energy sources load forecasting smart system weather data off-grid system |
| title | Intelligent Systems for Power Load Forecasting: A Study Review |
| title_full | Intelligent Systems for Power Load Forecasting: A Study Review |
| title_fullStr | Intelligent Systems for Power Load Forecasting: A Study Review |
| title_full_unstemmed | Intelligent Systems for Power Load Forecasting: A Study Review |
| title_short | Intelligent Systems for Power Load Forecasting: A Study Review |
| title_sort | intelligent systems for power load forecasting a study review |
| topic | renewable energy sources load forecasting smart system weather data off-grid system |
| url | https://www.mdpi.com/1996-1073/13/22/6105 |
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