A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting
A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact. The paper presents a comparative analysis of recurrent neural network algorithms and gradient bo...
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Electrical Engineering Institute Nikola Tesla
2024-01-01
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| Series: | Zbornik Radova: Elektrotehnički Institut "Nikola Tesla" |
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| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/0350-8528/2024/0350-85282434015I.pdf |
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| author | Ivanović Luka Milić Saša D. Sokolović Živko Rakić Aleksandar |
| author_facet | Ivanović Luka Milić Saša D. Sokolović Živko Rakić Aleksandar |
| author_sort | Ivanović Luka |
| collection | DOAJ |
| description | A vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact. The paper presents a comparative analysis of recurrent neural network algorithms and gradient boosting machines applied to time series data for the regression issue of estimating the active power generated by a wind farm. Gradient boosting algorithms combine the advantages of a few machine learning models (decision trees, random forests, etc.) to produce a powerful prediction model. In addition to conventional recurrent neural networks, the article deals with long short-term memory and gated recurrent unit as cutting-edge models for time series analysis and predictions. A comprehensive analysis was carried out on a large wind power generation data set. |
| format | Article |
| id | doaj-art-331f8a2e35a34bda8bbca9956b4dd85f |
| institution | Kabale University |
| issn | 0350-8528 2406-1212 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Electrical Engineering Institute Nikola Tesla |
| record_format | Article |
| series | Zbornik Radova: Elektrotehnički Institut "Nikola Tesla" |
| spelling | doaj-art-331f8a2e35a34bda8bbca9956b4dd85f2025-01-08T16:53:52ZengElectrical Engineering Institute Nikola TeslaZbornik Radova: Elektrotehnički Institut "Nikola Tesla"0350-85282406-12122024-01-01202434153610.5937/zeint34-512580350-85282434015IA comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecastingIvanović Luka0https://orcid.org/0000-0003-4345-4523Milić Saša D.1https://orcid.org/0000-0001-5757-3430Sokolović Živko2https://orcid.org/0009-0009-0747-6320Rakić Aleksandar3https://orcid.org/0000-0002-3682-1754University of Belgrade, Electrical Engineering Institute Nikola Tesla, Belgrade, SerbiaUniversity of Belgrade, Electrical Engineering Institute Nikola Tesla, Belgrade, SerbiaUniversity of Belgrade, Electrical Engineering Institute Nikola Tesla, Belgrade, SerbiaUniversity of Belgrade, School of Electrical Engineering, Belgrade, SerbiaA vital step toward a sustainable future is the power grid's incorporation of renewable energy sources. Wind energy is significant because of its broad availability and minimal environmental impact. The paper presents a comparative analysis of recurrent neural network algorithms and gradient boosting machines applied to time series data for the regression issue of estimating the active power generated by a wind farm. Gradient boosting algorithms combine the advantages of a few machine learning models (decision trees, random forests, etc.) to produce a powerful prediction model. In addition to conventional recurrent neural networks, the article deals with long short-term memory and gated recurrent unit as cutting-edge models for time series analysis and predictions. A comprehensive analysis was carried out on a large wind power generation data set.https://scindeks-clanci.ceon.rs/data/pdf/0350-8528/2024/0350-85282434015I.pdfmachine learningrecurrent neural networklong short-term memorygated recurrent unitgradient boosting machinesxgboostwind farmpower generation |
| spellingShingle | Ivanović Luka Milić Saša D. Sokolović Živko Rakić Aleksandar A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting Zbornik Radova: Elektrotehnički Institut "Nikola Tesla" machine learning recurrent neural network long short-term memory gated recurrent unit gradient boosting machines xgboost wind farm power generation |
| title | A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting |
| title_full | A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting |
| title_fullStr | A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting |
| title_full_unstemmed | A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting |
| title_short | A comparative analysis of Deep Neural Networks and Gradient Boosting Algorithms in long-term wind power forecasting |
| title_sort | comparative analysis of deep neural networks and gradient boosting algorithms in long term wind power forecasting |
| topic | machine learning recurrent neural network long short-term memory gated recurrent unit gradient boosting machines xgboost wind farm power generation |
| url | https://scindeks-clanci.ceon.rs/data/pdf/0350-8528/2024/0350-85282434015I.pdf |
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