[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12王 琦12姚 阳12陈佳浩12刘 瑾12
(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 Shunxiang12Wang Qi12Yao Yang12Chen Jiahao12Liu Jin12
(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 generationcombination
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.

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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