An Ultra-Short-Term Wind Power Prediction Method Based on the Fusion of Multiple Technical Indicators and the XGBoost Algorithm

Xuehui Wang, Yongsheng Wang (), Yongsheng Qi, Jiajing Gao, Fan Yang and Jiaxuan Lu
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Xuehui Wang: College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
Yongsheng Wang: College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
Yongsheng Qi: College of Electrical Engineering, Inner Mongolia University of Technology, Hohhot 010080, China
Jiajing Gao: College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
Fan Yang: College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China
Jiaxuan Lu: College of Data Science and Application, Inner Mongolia University of Technology, Hohhot 010080, China

Energies, 2025, vol. 18, issue 12, 1-21

Abstract: Wind power, as a clean and renewable energy source, plays an increasingly important role in the global transition to low-carbon energy systems. However, its inherent volatility and unpredictability pose challenges for accurate short-term prediction. This study proposes an ultra-short-term wind power prediction framework that integrates multiple technical indicators with the extreme gradient boosting (XGBoost) algorithm. Inspired by financial time series analysis, the model incorporates K-line representations, power fluctuation features, and classical technical indicators, including the moving average convergence divergence (MACD), Bollinger bands (BOLL), and average true range (ATR), to enhance sensitivity to short-term variations. The proposed method is validated on two real-world wind power datasets from Inner Mongolia, China, and Germany, sourced from the European network of transmission system operators for electricity (ENTSO-E). The experimental results show that the model achieves strong performance on both datasets, demonstrating good generalization ability. For instance, on the Inner Mongolia dataset, the proposed model reduces the mean squared error (MSE) by approximately 11.4% compared to the long short-term memory (LSTM) model, significantly improving prediction accuracy.

Keywords: wind power prediction; sliding time window; K-line; financial technology indicators; power variation feature; XGBoost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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