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

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.

Original languageEnglish
Article number1132
JournalEnergies
Volume16
Issue number3
DOIs
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.

Keywords

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

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