[1]齐琦,陈芳芳,徐天奇,等.基于经验模态分解改进神经网络光伏出力预测[J].应用科技,2020,47(3):41-45.[doi:10.11991/yykj.201911018]
 QI Qi,CHEN Fangfang,XU Tianqi,et al.Improved neural network for output prediction of photovoltaic generation based on empirical modal decomposition[J].Applied science and technology,2020,47(3):41-45.[doi:10.11991/yykj.201911018]
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基于经验模态分解改进神经网络光伏出力预测(/HTML)
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《应用科技》[ISSN:1009-671X/CN:23-1191/U]

卷:
第47卷
期数:
2020年3期
页码:
41-45
栏目:
现代电子技术
出版日期:
2020-07-05

文章信息/Info

Title:
Improved neural network for output prediction of photovoltaic generation based on empirical modal decomposition
作者:
齐琦 陈芳芳 徐天奇 孙祥晟
云南民族大学 电气信息工程学院,云南 昆明 650504
Author(s):
QI Qi CHEN Fangfang XU Tianqi SUN Xiangsheng
School of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China
关键词:
清洁能源光伏发电出力预测经验模态分解GA遗传优化算法BP神经网络组合预测模型
Keywords:
clean energyPV generationoutput forecastingempirical modal decompositionGA genetic optimization algorithmBP neural networkcombined forecast model
分类号:
TM615
DOI:
10.11991/yykj.201911018
文献标志码:
A
摘要:
光伏发电存在光伏出力不稳定性与波动性等问题。本文提出一种基于经验模态分解(EMD)的遗传算法(GA)优化BP神经网络的短期发电功率预测模型,优化了BP神经网络迭代次数多、收敛时间长等缺陷。从某小型光伏电站获得发电数据,建立EMD-GA-BP预测模型,与单一的BP神经网络预测模型和GA-BP神经网络预测模型作对比,证实本文提出预测模型稳定性好且误差较小,具有一定的研究价值。
Abstract:
Photovoltaics power generation is a hot spot of research nowadays, and in the mean time, there are also shortcomings, such as instability and volatility of photovoltaic output. In this paper, a short-term power generation forecast model of genetic algorithm(GA)-BP neural network based on empirical modal decomposition(EMD) is proposed, which optimizes the defects of the BP neural network, such as too many times of iteration, long convergence time, etc. An EMD-GA-BP prediction model was established by obtaining power generation data from a small photovoltaic power station, and then compared with the single BP neural network prediction model and the GA-BP neural network prediction model, confirming that both the stability and error of the prediction model are small. This study has a certain research value.

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备注/Memo

备注/Memo:
收稿日期:2019-11-15。
基金项目:国家自然科学基金项目(61761049,61461055);云南省教育厅科学研究基金项目(2019Y0169)
作者简介:齐琦,女,硕士研究生;陈芳芳,女,副教授
通讯作者:陈芳芳,E-mail:cff2009h@126.com
更新日期/Last Update: 2020-08-05