A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms

Soyoung Park, Solyoung Jung, Jaegul Lee, Jin Hur

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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.

Original languageEnglish
Article number1132
Issue number3
StatePublished - Feb 2023

Bibliographical note

Funding Information:
This work was supported by the Korea Electric Power Corporation (No. R21XO01-1) and this work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1F1A1074397).

Publisher Copyright:
© 2023 by the authors.


  • gradient-boosting machine (GBM)
  • machine learning
  • renewable energy
  • wind-power forecasting


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