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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Vydané v:Energies (Basel) Ročník 16; číslo 3; s. 1132
Hlavní autori: Park, Soyoung, Jung, Solyoung, Lee, Jaegul, Hur, Jin
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.01.2023
Predmet:
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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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en16031132