摘要
针对风速序列非线性对预测结果的影响,提出一种基于改进互补集合经验模态分解和极限学习机的风速预测模型。首先对风速序列进行改进互补集合经验模态分解,并利用相空间重构得到若干新的时间序列,以降低风速序列的不平稳性。通过改进布谷鸟算法矫正极限学习机模型的输入参数,预测处理后的风速序列。通过实例仿真,比较改进前后不同模型的相对误差,说明该文预测模型的合理性。
A wind speed prediction model is proposed in this paper to deal with nonlinearity of wind speed series.The proposed model is based on improved complementary ensemble empirical mode decomposition(CEEMD)and extreme learning machine(ELM).Firstly,in the proposed model,the wind speed series are decomposed using improved CEEMD,and some new time series are obtained utilizing phase space reconstruction,lowering the inconsistency of wind speed series.After that,the processed wind speed series are predicted using the optimal input parameters,found using improved cuckoo search(CS)algorithm,of the ELM model.At the end of this paper,simulations are conducted and the prediction model is proved to be reasonable by comparing the relative error of different models before and after the prediction process.
作者
高桂革
原阔
曾宪文
郑炳杰
Gao Guige;Yuan Kuo;Zeng Xianwen;Zheng Bingjie(School of Electric Engineering,Shanghai Dianji University,Shanghai 201306,China;School of Electronic Information Engineering,Shanghai Dianji University,Shanghai 201306,China;Huarun New Energy(Datong)Wind Energy Ltd.,Datong 038200,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2021年第7期284-289,共6页
Acta Energiae Solaris Sinica
关键词
风速
预测分析
互补集合经验模态分解
布谷鸟算法
相空间重构
极限学习机
wind speed
predictive analytics
complementary ensemble empirical mode decomposition(CEEMD)
cuckoo search evolutionary algorithms
phase-space reconstruction
extreme learning machine(ELM)
