摘要
针对风电场短期风速预测准确度不高的问题,提出一种基于互补集成经验模态分解和Stacking融合的短期风速组合预测模型。首先,为突出短期风速的局部特征并降低建模难度,通过互补集成经验模态分解算法将短期风速分解为若干个稳定分量。然后,利用信息熵和近似熵来判定各分量的复杂度,高复杂度分量选择最小二乘支持向量机、低复杂度分量选择随机配置网络作为对应的预测模型。利用Stacking算法对每个模型的预测值进行融合,使预测精度得到提升。最后,通过一组实际的短期风速数据作为研究对象,将提出的预测模型应用于其预测。对比结果表明,所提预测模型可提高短期风速的预测精度。
A short-term wind speed combination prediction model based on complementary ensemble empirical mode decomposition and Stacking fusion is proposed to address the issue of low accuracy in short-term wind speed prediction in wind farms.Firstly,in order to highlight the local characteristics of short-term wind speed and reduce modeling difficulty,the short-term wind speed is decomposed into several stable components using complementary ensemble empirical mode decomposition algorithm.Then,information entropy and approximate entropy are used to determine the complexity of each component.The high complexity component selects the least squares support vector machine,and the low complexity component selects the stochastic configuration networks as the corresponding prediction model.By using the Stacking algorithm to fuse the predicted values of each model,the prediction accuracy is improved.Finally,using a group of actual short-term wind speed data set as the research object,the proposed prediction model is applied to its prediction.The comparison results indicate that the proposed prediction model improves the accuracy of short-term wind speed prediction.
作者
唐非
Tang Fei(School of Artificial Intelligence,Shenyang University of Technology,Shenyang 110870,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2024年第7期735-744,共10页
Acta Energiae Solaris Sinica
基金
辽宁省教育厅科学研究项目面上项目(LJKZ0145)。
关键词
风能
短期风速
组合预测
互补集成经验模态分解
多模型
Stacking融合
wind energy
short-term wind speed
combination prediction
complementary ensemble empirical mode decomposition
multiple model
Stacking fusion
