A Short-Term Forecasting of Wind Power Outputs Based on Gradient Boosting Regression Tree Algorithms
- 주제(키워드) renewable energy , wind-power forecasting , machine learning , gradient-boosting machine (GBM)
- 주제(기타) Energy & Fuels
- 설명문(일반) [Park, Soyoung; Hur, Jin] Ewha Womans Univ, Dept Climate & Energy Syst Engn, Seoul 03760, South Korea; [Jung, Solyoung; Lee, Jaegul] Korea Elect Power Corp Res Inst, Daejeon 34056, South Korea
- 관리정보기술 faculty
- 등재 SCIE, SCOPUS
- 발행기관 MDPI
- 발행년도 2023
- URI http://www.dcollection.net/handler/ewha/000000204227
- 본문언어 영어
- Published As https://doi.org/10.3390/en16031132
초록/요약
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.
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