摘要
由于天气状况、机组控制策略等外部因素的复杂性,未来预测数据常常偏离训练数据分布,从而导致超短期风电功率预测模型精度显著下降。为解决该问题,提出了基于不变学习的超短期风电功率预测方法,该方法通过联合优化环境推理模块与不变特征学习模块,学习不变特征与功率间映射关系,实现鲁棒性建模。结果表明:与2个基准模型相比,所提方法的归一化均方根误差和归一化平均绝对误差分别降低1.19~1.30百分点和0.41~0.68百分点。
Due to the complexity of external factors such as weather conditions and wind turbine control strategies,future forecasting data often deviates from the training data distribution,leading to a significant decline in the accuracy of ultra-short-term wind power prediction models.To address this issue,an ultra-short-term wind power forecasting method based on invariant learning was proposed.The method learned the mapping relationship between invariant features and power by jointly optimizeing the environment inference module and the invariant feature learning module,achieving robust modeling.The results show that compared with two benchmark models,the proposed method reduces forecasting error of the normalized root mean square error and normalized mean absolute error by an average of 1.19-1.30 and 0.41-0.68 percentage points respectively.
作者
李玉浩
阎洁
王函
韩爽
刘永前
LI Yuhao;YAN Jie;WANG Han;HAN Shuang;LIU Yongqian(School of New Energy,North China Electric Power University,Beijing 102206,China)
出处
《动力工程学报》
北大核心
2025年第10期1696-1703,共8页
Journal of Chinese Society of Power Engineering
基金
国家自然科学基金资助项目(52277099)
北京市科技新星资助项目(20230484355)。
关键词
超短期
风电功率预测
分布外
不变学习
ultra-short-term
wind power forecasting
out-of-distribution
invariant learning
