摘要
为提高超短期风功率预测精度,提出一种基于SGMD-SE与优化的TCN-BiLSTM/BiGRU组合预测模型。首先,采用最大互信息系数(MIC)选取出风功率强相关变量,作为预测模型的输入特征。其次,利用能抑制模态混叠、无须设置分解参数的辛几何模态分解(SGMD),将原始风功率信号分解成若干个较平稳的初始辛几何分量(SGC)。然后,使用样本熵(SE)完成初始分量重构并将重构后分量划分为复杂度高、低两类,根据两类分量不同特点,分别搭建TCN-BiLSTM模型、TCN-BiGRU模型进行预测。为改善BiLSTM、BiGRU预测性能,采用时间卷积网络(TCN)提取两类分量特征,并提出一种基于Tent混沌映射和柯西变异的改进鱼鹰优化算法(IOOA)优化其关键参量。最后,叠加各分量预测值得到最终的预测结果。结果表明:所提出的组合预测模型可有效提升超短期风功率预测的准确率,具有较强的实用价值。
To enhance the precision of ultra-short-term wind power prediction,a model is proposed that combines SGMD-SE with an optimized TCN-BiLSTM/BiGRU framework.Firstly,highly correlated variables are selected using maximum information coefficient(MIC)as input features.Secondly,symplectic geometry modal decomposition(SGMD)is utilized to decompose the original signal into initial symplectic geometry components(SGC),effectively suppressing mode mixing and eliminating the need for decomposition parameters.Then,sample entropy(SE)is applied to reconstruct the initial components,which are classified into high and low complexity categories.Based on the different characteristics of these categories,separate TCN-BiLSTM and TCN-BiGRU models are built for prediction.To improve the predictive performance of BiLSTM and BiGRU,time convolutional networks(TCN)are utilized to extract features from the two component categories.Additionally,an improved optimization algorithm called IOOA,based on Tent chaotic mapping and Cauchy mutation,is proposed to optimize their key parameters.Finally,the final prediction result is superimposed by combining the predicted values of each component.The findings suggest that the proposed hybrid prediction model significantly enhances the accuracy of ultra-short-term wind power forecasting and holds substantial practical utility.
作者
宋江涛
崔双喜
樊小朝
孙玉峰
Song Jiangtao;Cui Shuangxi;Fan Xiaochao;Sun Yufeng(Renewable Energy Generation and Grid Integration Control Center,Xinjiang,University Urumqi 830047,China;Department of New Energy Science and Engineering,Xinjiang Institute of Engineering,Urumqi 830023,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2024年第10期588-596,共9页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(52067020)
新疆维吾尔自治区重点研发项目(2022B01019-1)。
关键词
风功率预测
分解
长短期记忆网络
时间卷积网络
鱼鹰优化算法
wind power prediction
decomposition
long short-term memory
time convolutional network
osprey optimization algorithm
