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

Author

Listed:
  • Soyoung Park

    (Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul 03760, Republic of Korea)

  • Solyoung Jung

    (Korea Electric Power Corporation Research Institute, Daejeon 34056, Republic of Korea)

  • Jaegul Lee

    (Korea Electric Power Corporation Research Institute, Daejeon 34056, Republic of Korea)

  • Jin Hur

    (Department of Climate and Energy Systems Engineering, Ewha Womans University, Seoul 03760, Republic of Korea)

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.

Suggested Citation

  • Soyoung Park & Solyoung Jung & Jaegul Lee & Jin Hur, 2023. "A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms," Energies, MDPI, vol. 16(3), pages 1-12, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1132-:d:1041655
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    References listed on IDEAS

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    1. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
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    Cited by:

    1. Chao-Ming Huang & Shin-Ju Chen & Sung-Pei Yang & Hsin-Jen Chen, 2023. "One-Day-Ahead Hourly Wind Power Forecasting Using Optimized Ensemble Prediction Methods," Energies, MDPI, vol. 16(6), pages 1-22, March.
    2. Wen-Chang Tsai & Chih-Ming Hong & Chia-Sheng Tu & Whei-Min Lin & Chiung-Hsing Chen, 2023. "A Review of Modern Wind Power Generation Forecasting Technologies," Sustainability, MDPI, vol. 15(14), pages 1-40, July.

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