A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms
With growing interest in sustainability and net-zero emissions, there has been a global trend to integrate wind power into energy grids. However, challenges such as the intermittency of wind energy remain, which leads to a significant need for accurate wind-power forecasting. Therefore, this study f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/3/1132 |
| _version_ | 1827760223765397504 |
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| author | Soyoung Park Solyoung Jung Jaegul Lee Jin Hur |
| author_facet | Soyoung Park Solyoung Jung Jaegul Lee Jin Hur |
| author_sort | Soyoung Park |
| collection | DOAJ |
| description | With growing interest in sustainability and net-zero emissions, there has been a global trend to integrate wind power into energy grids. However, challenges such as the intermittency of wind energy remain, which leads to a significant need for accurate wind-power forecasting. Therefore, this study focuses on creating a wind-power generation-forecasting model using a machine-learning algorithm. In this study, we used the gradient-boosting machine (GBM) algorithm to build a wind-power forecasting model. Time-series data with a 15 min interval from Jeju’s wind farms were applied to the model as input data. The short-term forecasting model trained by the same month with the test set turns out to have the best performance, with an NMAE value of 5.15%. Furthermore, the forecasting results were applied to Jeju’s power system to carry out a grid-security analysis. The improved accuracy of wind-power forecasting and its impact on the security of electrical grids in this study potentially contributes to greater integration of wind energy. |
| first_indexed | 2024-03-11T09:47:19Z |
| format | Article |
| id | doaj.art-db15470c9bfe45f1a2611a9768af8d62 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-11T09:47:19Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-db15470c9bfe45f1a2611a9768af8d622023-11-16T16:33:06ZengMDPI AGEnergies1996-10732023-01-01163113210.3390/en16031132A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree AlgorithmsSoyoung Park0Solyoung Jung1Jaegul Lee2Jin Hur3Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul 03760, Republic of KoreaKorea Electric Power Corporation Research Institute, Daejeon 34056, Republic of KoreaKorea Electric Power Corporation Research Institute, Daejeon 34056, Republic of KoreaDepartment of Climate and Energy Systems Engineering, Ewha Womans University, Seoul 03760, Republic of KoreaWith growing interest in sustainability and net-zero emissions, there has been a global trend to integrate wind power into energy grids. However, challenges such as the intermittency of wind energy remain, which leads to a significant need for accurate wind-power forecasting. Therefore, this study focuses on creating a wind-power generation-forecasting model using a machine-learning algorithm. In this study, we used the gradient-boosting machine (GBM) algorithm to build a wind-power forecasting model. Time-series data with a 15 min interval from Jeju’s wind farms were applied to the model as input data. The short-term forecasting model trained by the same month with the test set turns out to have the best performance, with an NMAE value of 5.15%. Furthermore, the forecasting results were applied to Jeju’s power system to carry out a grid-security analysis. The improved accuracy of wind-power forecasting and its impact on the security of electrical grids in this study potentially contributes to greater integration of wind energy.https://www.mdpi.com/1996-1073/16/3/1132renewable energywind-power forecastingmachine learninggradient-boosting machine (GBM) |
| spellingShingle | Soyoung Park Solyoung Jung Jaegul Lee Jin Hur A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms Energies renewable energy wind-power forecasting machine learning gradient-boosting machine (GBM) |
| title | A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms |
| title_full | A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms |
| title_fullStr | A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms |
| title_full_unstemmed | A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms |
| title_short | A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms |
| title_sort | short term forecasting of wind power outputs based on gradient boosting regression tree algorithms |
| topic | renewable energy wind-power forecasting machine learning gradient-boosting machine (GBM) |
| url | https://www.mdpi.com/1996-1073/16/3/1132 |
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