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

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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Xuehui Wang, Yongsheng Wang, Yongsheng Qi, Jiajing Gao, Fan Yang, Jiaxuan Lu
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Energies
Subjects:
wind power prediction
sliding time window
K-line
financial technology indicators
power variation feature
XGBoost
Online Access:https://www.mdpi.com/1996-1073/18/12/3069
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1996-1073

Similar Items