[1]吴江,薛金花,余涛,等.基于NARX神经网络-小波分解光伏发电功率预测[J].郑州大学学报(工学版),2020,41(06):79-84.[doi:10.13705/j.issn.1671-6833.2020.06.015]
　SHI Ruxin,WANG Deshun,YU Tao,et al.Prediction of Photovoltaic Power Generation Based on NARX Neural Network-Wavelet Decomposition[J].Journal of Zhengzhou University (Engineering Science),2020,41(06):79-84.[doi:10.13705/j.issn.1671-6833.2020.06.015]
点击复制

基于NARX神经网络-小波分解光伏发电功率预测()
分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
41
期数:
2020年06期
页码:
79-84
栏目:
出版日期:
2020-12-31

文章信息/Info

Title:
Prediction of Photovoltaic Power Generation Based on NARX Neural Network-Wavelet Decomposition
作者:
吴江; 薛金花; 余涛; 冯鑫振; 王德顺; 窦春霞; 史如新
国网江苏省电力有限公司常州供电分公司,江苏常州213000, 中国电力科学研究院有限公司南京分院,江苏南京210009, 南京邮电大学先进技术研究院,江苏南京210023

Author(s):
SHI Ruxin1;  WANG Deshun2;  YU Tao1;  XUE Jinhua2;  FENG Xinzhen2;  DOU Chunxia3
1.Changzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd.,Changzhou 213000, China; 2.Nanjing Branch,China Electric Power Research Institute,Nanjing 210009,China; 3.Institude of Advanced Technology,Nanjing University of Posts and Telecommunications, Nanjing 210009,China
关键词:
Keywords:
prediction of photovoltaic power;  wavelet decomposition;  NARX neural network;  wavelet reconstruction;  BP neural network
DOI:
10.13705/j.issn.1671-6833.2020.06.015
文献标志码:
A
摘要:
由于光伏发电功率所具有的间歇性、波动性和随机特性，对其进行更加准确的预测可以有效减少光伏发电对电力系统的不利影响，对电力系统的安全经济稳定运行具有重要意义.本文提出了基于小波分解-NARX神经网络组合预测方法，通过小波分解将历史光伏序列分解为高频和低频分量，将高、低频数据作为NARX神经网络输入、光伏输出功率作为神经网络输出进行训练得到预测输出，随后对其进行小波重构推求出光伏发电预测数据.通过仿真结果表明，新的预测算法预测误差比传统BP神经网络更小、预测精度更高，并且具有良好的适应性，并证实了基于小波分解-NARX神经网络组合预测方法的可行性和高效性.
Abstract:
Aiming to reduce large forecast error in current photovoltaic power generation forecast, based on wavelet decomposition-NARX neural network combined prediction method was proposed. The historical PV sequence was decomposed into high frequency and low frequency components by wavelet decomposition, and the high and low frequency data were used as NARX neural network input, and PV output power was used as neural network output to train and obtain the predicted output. Then wavelet reconstruction was used to derive PV power generation prediction data. The simulation results showed that prediction error of new combined prediction algorithm was 1.47% lower than that of traditional BP neural network, and new prediction algorithm could reduce running time by nearly 5 s.

参考文献/References:

[1] 胡梦月,胡志坚,仉梦林,等.基于改进AdaBoost.RT和KELM的风功率预测方法研究[J].电网技术,2017,41(2):536-542.

[2] 黄辰,吴峻青.基于人工神经网络的短期风电功率预测[J]. 华东电力, 2014, 42(7):1408-1410.
[3] 张岚,张艳霞,郭嫦敏,等.基于神经网络的光伏系统发电功率预测[J].中国电力,2010,43(9):75-78.
[4] 朱永强,田军.最小二乘支持向量机在光伏功率预测中的应用[J].电网技术,2011,35(7):54-59．
[5] 陈昌松,段善旭,蔡涛,等．基于模糊识别的光伏发电短期预测系统[J]．电工技术学报，2011,26(7):83-89．
[6] 代倩,段善旭,蔡涛,等．基于天气类型聚类识别的光伏系统短期无辐照度发电预测模型研究[J]．中国电机工程学报，2011,31(34):28-35．
[7] 刘卫亮,刘长良,林永君,等.计及雾霾影响因素的光伏发电超短期功率预测[J].中国电机工程学报,2018,38(14):4086-4095，4315.
[8] MELLIT A,PAVAN A M.Performance prediction of 20 kWP grid-connected photovoltaic plant at Trieste (Italy) using artificial neural network[J]. Energy conversion and management,2010,51(12):2431-2441.
[9] 王晓兰,王明伟.基于小波分解和最小二乘支持向量机的短期风速预测[J].电网技术，2010,34(1):179-184.
[10] 谢吉洋,闫冬,谢垚,等.基于NARX神经网络的热负荷预测中关键影响因素分析[J].计算机应用，2018,38(11):3180-3187.
[11] 米增强,刘兴杰,张艳青,等.基于混沌分析和神经网络的风速直接多步预测[J].太阳能学报,2011,32(6):901-906.
[12] PLETT G L.Adaptive inverse control of linear and nonlinear systems using dynamic neural networks[J]. IEEE transactions on neural networks, 2003, 14(2): 360-376.
[13] 周品.MATLAB神经网络设计与应用[M].北京:清华大学出版社,2013.
[14] 赵唯嘉,张宁,康重庆,等.光伏发电出力的条件预测误差概率分布估计方法[J].电力系统自动化，2015,39(16):8-15.
[15] GUO M, LAN J H, LI J J, et al．Traffic flow data recovery algorithm based on gray residual GM(1,N)model[J].Journal of transportation systems engineering and information technology,2012,12(1):42-47.
[16] CHEN J L, LI G S, WU S J．Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration[J].Energy conversion and management,2013,75:311-318．

更新日期/Last Update: 2021-02-10