摘要
为克服光伏功率预测中的不确定性和复杂性,提出一种基于DBO优化VMD-KELM的光伏功率预测模型,该模型首先将一系列不稳定信号通过VMD分解后进行平稳化处理,接着将分解后的序列构建KELM模型,最后采用DBO优化参数,将预测数列进行整合重构。
In order to overcome the uncertainty and complexity in photovoltaic power prediction,a photovoltaic power prediction model based on DBO optimization VMD-KELM is proposed,which firstly smoothes a series of unstable signals by VMD decomposition,then constructs a KELM model for the decomposed sequences,and finally integrates and reconstructs the prediction series by using DBO optimization parameters.
作者
任少娟
王宇驰
方续东
Ren Shaojuan;Wang Yuchi;Fang Xudong(School of Electrical and Control Engineering,Liaoning Technical University,Huludao Liaoning 125105,China;School of Metallurgy and Materials Engineering,Liaoning Institute of Science and Technology,Benxi Liaoning 117004,China)
出处
《现代工业经济和信息化》
2024年第8期184-186,共3页
Modern Industrial Economy and Informationization
关键词
光伏功率预测
变分模态分解
核极限学习机
蜣螂优化算法
photovoltaic power prediction
variational modal decomposition
kernel limit learning machine
dung beetle optimisation algorithm
