摘要
为了提高光伏发电预测的准确性,提出一种结合变分模态分解(VMD)、改进的射箭算法(AA)和改进的极限学习机(ELM)的短期光伏功率预测模型。首先,将光伏数据进行变分模态分解;然后,利用混合核函数改进极限学习机;之后,利用随机反向学习策略改进射箭算法;最后,通过改进的射箭算法对混合核极限学习机中的核参数寻优并建立预测模型。通过对澳大利亚DKA太阳能中心的数据进行验证,证明该文方法的准确性。
In order to improve the accuracy of photovoltaic power generation forecast,a short-term photovoltaic power generation forecast model based on variational mode decomposition(VMD),improved archery algorithm(IAA)and improved extreme learning machine(ELM)was proposed.Firstly,decompose the photovoltaic data by variational modal decomposition algorithm Secondly,use hybrid kernel to improve extreme learning Machine,and then use the Random opposition-based learning to improve archery algorithm.Finally,the algorithm is used to optimize the kernel function parameters.The accuracy of this method is verified by the data of DKA solar energy center in Australia.
作者
陈龙
张菁
张昊立
倪建辉
高典
Chen Long;Zhang Jing;Zhang Haoli;Ni Jianhui;Gao Dian(School of Electric and Electronic Engineering,Shanghai University of Engineering Science,Shanghai 201620,China)
出处
《太阳能学报》
EI
CAS
CSCD
北大核心
2023年第10期135-141,共7页
Acta Energiae Solaris Sinica
基金
国家自然科学基金(61803255)。
关键词
光伏发电
功率预测
机器学习
极限学习机
混合核函数
射箭算法
photovoltaic power
power forecasting
machine learning
extreme learning machine
hybrid kernel
archery algorithm
