广西师范大学学报（自然科学版）2026，Vol.44Issue(3)：36-46,11.DOI:10.16088/j.issn.1001-6600.2025081901
基于异质集成学习的低温寒潮天气下短期风电功率预测
Short-term Wind Power Forecasting Based on Heterogeneous Ensemble Learning Under Low Temperature and Cold Wave Weather
摘要
Abstract
Unplanned icing-induced outages of wind turbines during low temperature and cold wave weather lead to severe fluctuations in wind farm power generation,posing significant challenges to the prediction accuracy of traditional models under such extreme operating conditions.To address this issue,this paper proposes a short-term power forecasting method based on heterogeneous ensemble learning.Firstly,it constructs a wavelet transform-driven deep feature extraction network,in which wavelet transform decouples meteorological data into detail components and trend components.These components are processed separately,where Convolutional Neural Networks enhance the capture of spatial local features,while Long Short-Term Memory networks model temporal dependencies,followed by adaptive feature fusion via a cross-attention mechanism.Subsequently,a heterogeneous ensemble strategy builds a Stacking framework combined with Light Gradient Boosting Machine,Extreme Gradient Boosting,and Support Vector Regression serve as diverse Base Learners to fully exploit feature heterogeneity,while linear regression(LR)acts as the meta-learner to optimize prediction accuracy and robustness.Based on the real-world data from a wind farm in Jiangxi province during winter,the proposed method achieves a 4-hour-ahead prediction with mean absolute error,mean squared error,and coefficient of determination of 0.028,0.119,and 0.618,respectively.The experimental results demonstrate that the proposed model significantly enhances forecasting accuracy under low temperature and cold wave weather conditions compared with both single models and conventional ensemble approaches.关键词
短期风电功率预测/低温寒潮天气/覆冰/异质集成学习/stacking集成Key words
short-term wind power forecasting/low temperature and cold wave weather/ice coating/heterogeneous ensemble learning/stacking ensemble分类
信息技术与安全科学引用本文复制引用
何昊,吴康,兰鑫,桂小智,董优丽..基于异质集成学习的低温寒潮天气下短期风电功率预测[J].广西师范大学学报（自然科学版）,2026,44(3):36-46,11.基金项目
国家自然科学基金(52077089) （52077089）
江西省电网公司科技项目(KC2411) （KC2411）
