[1]季顺祥,王 琦,姚 阳,等.基于相似日和交叉熵理论的光伏发电功率组合预测[J].南京师范大学学报(工程技术版),2018,18(02):019.[doi:10.3969/j.issn.1672-1292.2018.02.003]
　Ji Shunxiang,Wang Qi,Yao Yang,et al.Photovoltaic Power Generation Combination ForecastingBased on Similar Days and Cross Entropy Theory[J].Journal of Nanjing Normal University(Engineering and Technology),2018,18(02):019.[doi:10.3969/j.issn.1672-1292.2018.02.003]
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基于相似日和交叉熵理论的光伏发电功率组合预测
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南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

卷:
18卷
期数:
2018年02期
页码:
019
栏目:
电气与电子工程
出版日期:
2018-06-30

文章信息/Info

Title:
Photovoltaic Power Generation Combination ForecastingBased on Similar Days and Cross Entropy Theory
文章编号:
1672-1292(2018)02-0019-10
作者:
季顺祥1; 2; 王 琦1; 2; 姚 阳1; 2; 陈佳浩1; 2; 刘 瑾1; 2
(1.南京师范大学南瑞电气与自动化学院,江苏 南京 210042)(2.南京师范大学江苏省气电互联综合能源工程实验室,江苏 南京 210023)
Author(s):
Ji Shunxiang1; 2; Wang Qi1; 2; Yao Yang1; 2; Chen Jiahao1; 2; Liu Jin1; 2
(1.School of NARI Electrical and Automation,Nanjing Normal University,Nanjing 210042,China)(2.Jiangsu Key Laboratory of Gas and Electricity Interconnection Integrated Energy,Nanjing Normal University,Nanjing 210023,China)
关键词:
光伏发电; 组合预测; 相似日; 隶属度; 交叉熵
Keywords:
photovolatic(PV)power generation; combination
分类号:
TM615
DOI:
10.3969/j.issn.1672-1292.2018.02.003
文献标志码:
A
摘要:
为进一步提高光伏发电功率预测精度,提出一种基于相似日和交叉熵理论的光伏发电短期功率组合预测方法. 首先采用模糊C均值聚类方法对历史样本数据分类,并提出一种基于隶属度的指标来选取相似日. 然后采用最小二乘支持向量机、时间序列法和BP神经网络法分别预测光伏发电功率,通过交叉熵算法动态设置各预测时刻下单一方法的权重值,建立光伏发电功率的组合预测模型. 算例结果表明,所提方法能够动态识别单一预测方法包含的信息量,能确定更加合理的权重值,从而提高光伏发电功率的预测精度.
Abstract:
In order to further improve the photovoltaic(PV)power forecasting accuracy,a short-term combination forecasting model based on similar days and cross entropy theory is proposed. Firstly,the fuzzy C-means clustering method is used to classify the historical samples,and a selection index based on membership degree is proposed to select similar days. Then,the LSSVM,ARMA and BP neural network are used to predict the PV power. The weights of three single forecasting methods are dynamically set by the cross entropy algorithm,and the short-term combination forecasting model of PV power is established. The results show that this method can dynamically identify the information of single methods and obtain appropriate weights. As a result,the forecasting accuracy of PV power can be improved.

参考文献/References:

[1] 丁明,王伟胜,王秀丽,等. 大规模光伏发电对电力系统影响综述[J]. 中国电机工程学报,2014,34(1):1-14.
[DING M,WANG W S,WANG X L,et al. A review on the effect of large-scale PV generation on power systems[J]. Proceedings of the CSEE,2014,34(1):1-14.(in Chinese)
[[2]龚莺飞,鲁宗相,乔颖,等. 光伏功率预测技术[J]. 电力系统自动化,2016,40(4):140-151.
[GONG Y F,LU Z X,QIAO Y,et al. An overview of photovoltaic energy system output forecasting technology[J]. Automation of electric power systems,2016,40(4):140-151.(in Chinese)
[[3]许童羽,马艺铭,曹英丽,等. 基于主成分分析和遗传优化BP神经网络的光伏输出功率短期预测[J]. 电力系统保护与控制,2016,44(22):90-95.
[XU T Y,MA Y M,CAO Y L,et al. Short term forecasting of photovoltaic output power based on principal component analysis and genetic optimization of BP neural network[J]. Power system protection and control,2016,44(22):90-95.(in Chinese)
[[4]TENG X L,GAO Z H,ZHANG Y Y,et al. Key technologies and the implementation of wind,PV and storage co-generation monitoring system[J]. Journal of modern power systems & clearn energy,2014,2(2):104-113.
[[5]高相铭,杨世凤,潘三博. 基于EMD和ABC-SVM的光伏并网系统输出功率预测研究[J]. 电力系统保护与控制,2015,43(21):86-92.
[GAO X M,YANG S F,PAN S B. A forecasting model for output power of grid-connected photovoltaic generation system based on EMD and ABC-SVM[J]. Power system protection and control,2015,43(21):86-92.(in Chinese)
[[6]LORENZ E,HURKA J,HEINEMANN D,et al. Irradiance forecasting for the power prediction of grid-connected photovoltaic systems[J]. IEEE journal of selected topics in applied earth observations and remote sensing,2009,2(1):2-10.
[[7]袁晓玲,施俊华,徐杰彦. 计及天气类型指数的光伏发电短期出力预测[J]. 中国电机工程学报,2013,33(34):57-64.
[YUAN X L,SHI J H,XU J Y. Short-term power forecasting for photovoltaic generation considering weather type index[J]. Proceedings of the CSEE,2013,33(34):57-64.(in Chinese)
[[8]兰华,廖志民,赵阳. 基于ARMA模型的光伏电站出力预测[J]. 电测与仪表,2011,48(2):31-35.
[LAN H,LIAO Z M,ZHAO Y. ARMA model of the solar power station based on output prediction[J]. Electrical measurement & instrumentation,2011,48(2):31-35.(in Chinese)
[[9]叶林,陈政,赵永宁,等. 基于遗传算法—模糊径向基神经网络的光伏发电功率预测模型[J]. 电力系统自动化,2015,39(16):16-22.
[YE L,CHEN Z,ZHAO Y N,et al. Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis function neural network[J]. Automation of electric power systems,2015,39(16):16-22.(in Chinese)
[[10]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.
[ZHU Y Q,TIAN J. Application of least square support vector machine in photovoltaic power forecasting[J]. Power system technology,2011,35(7):54-59.(in Chinese)
[[11]王新普,周想凌,邢杰,等. 一种基于改进灰色BP神经网络组合的光伏出力预测方法[J]. 电力系统保护与控制,2016,44(18):81-87.
[WANG X P,ZHOU X L,XING J,et al. A prediction method of PV output power based on the combination of improved grey back propagation neural network[J]. Power system protection and control,2016,44(18):81-87.(in Chinese)
[[12]杨锡运,刘欢,张彬,等. 基于熵权法的光伏输出功率组合预测模型[J]. 太阳能学报,2014,35(5):744-749.
[YANG X Y,LIU H,ZHANG B,et al. A combination method for photovoltaic power forecasting based on entropy weight method[J]. Acta energiae solaris sinica,2014,35(5):744-749.(in Chinese)
[[13]陈通,孙国强,卫志农,等. 基于相似日和CAPSO-SNN的光伏发电功率预测[J]. 电力自动化设备,2017,37(3):66-71.
[CHEN T,SUN G Q,WEI Z N,et al. Photovoltaic power generation forecasting based on similar day and CAPSO-SNN[J]. Electric power automation equipment,2017,37(3):66-71.(in Chinese)
[[14]杨锡运,刘玉奇,张璜,等. 含并网光伏电站的系统可靠性评估方法[J]. 高电压技术,2016,42(9):2689-2696.YANG X Y,LIU Y Q,ZHANG H,et al. Reliability evaluation method of grid connected with photovoltaic power station[J]. High voltage engineering,2016,42(9):2689-2696.(in Chinese)
[[15]陈宁,沙倩,汤奕,等. 基于交叉熵理论的风电功率组合预测方法[J]. 中国电机工程学报,2012,32(4):29-34.
[CHEN N,SHA Q,TANG Y,et al. A combination method for wind power prediction based on cross entropy theory[J]. Proceedings of the CSEE,2012,32(4):29-34.(in Chinese)
[[16]李建红,陈国平,葛鹏江,等. 基于相似日理论的光伏发电系统输出功率预测[J]. 华东电力,2012,40(1):153-157.
[LI J H,CHEN G P,GE P J,et al. Ouput power forecasting of PV generation system based on similar day theory[J]. East China electric power,2012,40(1):153-157.(in Chinese)
[[17]陈华友. 基于相关系数的优性组合预测模型研究[J]. 系统工程学报,2006,21(4):353-360.
[CHEN H Y. Research on properties of superior combined forecasting models based on correlation coefficients[J]. Journal of systems engineering,2006,21(4):353-360.(in Chinese)

相似文献/References:

[1]张艳莉,费万民.光伏发电中高压并网多电平逆变器的控制策略[J].南京师范大学学报(工程技术版),2011,11(01):019.
　Zhang Yanli,Fei Wanmin.SHEPWM and Power Balance Control for Composite Cascade Multilevel Inverters for Medium and High Voltage Interfacing of Photovoltaic Systems[J].Journal of Nanjing Normal University(Engineering and Technology),2011,11(02):019.

备注/Memo

备注/Memo:
收稿日期:2018-01-18.
基金项目:江苏省研究生科研与实践创新计划项目(KYCX17_1078).
通讯联系人:王琦,博士,副教授,研究方向:可再生能源发电技术. E-mail:wangqi@njnu.edu.cn
更新日期/Last Update: 2018-06-30