可再生能源2017，Vol.35Issue(4)：566-571,6.
基于提升小波-BP神经网络的光伏阵列短期功率预测
Short-term power prediction of photovoltaic array based on lifting wavelet transform-BP neural network
摘要
Abstract
The power of the PV array is a Non-stationary random process,influenced greatly by the radiation,temperature as well as uncertain external surroundings.Improving the accuracy of photovoltaic power system short-term prediction,especially the ultra-short-term forecast accuracy,has significant implications for the improving the operation and management efficiency of the photovoltaic power system.This article proposed a slip algorithm about the output of the DC side power,combining a lifting wavelet transform with BP neural network theory,to predict the ultra-shortterm power of the photovoltaic power array.The test results show that the method of ultra-short-term power forecast has good precision,and can be applied to complex weather conditions,such as the sunny,cloudy,rainy days.关键词
短期功率预测/小波变换/BP神经网络/直流侧功率Key words
short-term power prediction/wavelet analysis/BP neural network/DC side power分类
信息技术与安全科学引用本文复制引用
丁坤,丁汉祥,王越,高列,刘振飞..基于提升小波-BP神经网络的光伏阵列短期功率预测[J].可再生能源,2017,35(4):566-571,6.基金项目
江苏省自然科学基金(BK20131134) （BK20131134）
光伏科学与技术国家重点实验室开放基金课题(201400035879). （201400035879）
