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...

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Veröffentlicht in:	Energies (Basel) Jg. 16; H. 3; S. 1132
Hauptverfasser:	Park, Soyoung, Jung, Solyoung, Lee, Jaegul, Hur, Jin
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2023
Schlagworte:	Accuracy Algorithms Alternative energy sources Artificial intelligence Buildings and facilities Data mining Decision trees Electric power production Electricity Forecasting gradient-boosting machine (GBM) Machine learning Neural networks renewable energy Renewable resources Wind farms Wind power wind-power forecasting South Korea Jeju Island
ISSN:	1996-1073, 1996-1073
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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Jung, Solyoung Lee, Jaegul Park, Soyoung Hur, Jin
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SubjectTerms	Accuracy Algorithms Alternative energy sources Artificial intelligence Buildings and facilities Data mining Decision trees Electric power production Electricity Forecasting gradient-boosting machine (GBM) Machine learning Neural networks renewable energy Renewable resources Wind farms Wind power wind-power forecasting
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Title	A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms
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