基于结合混沌纵横交叉的PSO-DBN的短期光伏功率预测

TM615; 为了提高短期光伏发电预测的准确性,文中采用深度置信网络(DBN)建立了各模型函数的预测模型.通过分析各模型函数的特征,建立了光伏发电模型的功率预测.传统的基于神经网络的功率预测难以训练多层网络,影响其预测精度.DBN采用无监督贪婪逐层训练算法构建了一个在回归预测分析中具有优异性能的多隐层网络结构,已成为深度学习领域的研究热点.DBN连接权重采用结合混沌纵横交叉的粒子群优化算法(CC-PSO)优化,避免出现由随机初始化导致的局部最优解现象,从而提高了DBN网络预测性能.最后,案例测试显示了所提出模型的有效性....

Full description

Saved in:
Bibliographic Details
Published in电测与仪表 Vol. 57; no. 6; pp. 67 - 72
Main Authors 孙辉, 冷建伟
Format Journal Article
LanguageChinese
Published 天津理工大学电气电子工程学院,天津,300384 25.03.2020
Subjects
粒子群算法
预测精度
混沌横纵交叉
深度信念网络
光伏功率预测
Online AccessGet full text
ISSN1001-1390
DOI10.19753/j.issn1001-1390.2020.06.011

Cover

Abstract TM615; 为了提高短期光伏发电预测的准确性,文中采用深度置信网络(DBN)建立了各模型函数的预测模型.通过分析各模型函数的特征,建立了光伏发电模型的功率预测.传统的基于神经网络的功率预测难以训练多层网络,影响其预测精度.DBN采用无监督贪婪逐层训练算法构建了一个在回归预测分析中具有优异性能的多隐层网络结构,已成为深度学习领域的研究热点.DBN连接权重采用结合混沌纵横交叉的粒子群优化算法(CC-PSO)优化,避免出现由随机初始化导致的局部最优解现象,从而提高了DBN网络预测性能.最后,案例测试显示了所提出模型的有效性.
AbstractList TM615; 为了提高短期光伏发电预测的准确性,文中采用深度置信网络(DBN)建立了各模型函数的预测模型.通过分析各模型函数的特征,建立了光伏发电模型的功率预测.传统的基于神经网络的功率预测难以训练多层网络,影响其预测精度.DBN采用无监督贪婪逐层训练算法构建了一个在回归预测分析中具有优异性能的多隐层网络结构,已成为深度学习领域的研究热点.DBN连接权重采用结合混沌纵横交叉的粒子群优化算法(CC-PSO)优化,避免出现由随机初始化导致的局部最优解现象,从而提高了DBN网络预测性能.最后,案例测试显示了所提出模型的有效性.
Author 冷建伟
孙辉
AuthorAffiliation 天津理工大学电气电子工程学院,天津,300384
AuthorAffiliation_xml – name: 天津理工大学电气电子工程学院,天津,300384
Author_FL Leng Jianwei
Sun Hui
Author_FL_xml – sequence: 1
  fullname: Sun Hui
– sequence: 2
  fullname: Leng Jianwei
Author_xml – sequence: 1
  fullname: 孙辉
– sequence: 2
  fullname: 冷建伟
BookMark eNo9j81Kw0AcxPdQwVr7FuIt8b-72U1y1PgJxQrquWw2u9IiWzCI9F7RVmkFbyIExIMIXoRSiD5OPvAtjCieZpjD_GaWUM30jUJoBYONfZfRtZ7djWODAbCFqQ82AQI2cBswrqH6f76ImnHcDYFh6jocSB0FeZJm6aT8uM_vrov5vHi_LdNZ8fKapc_5dFQ-DA8O29bmxn7lyuSteEzyy1H2Oc3HSTm5-noaFrObZbSgxWmsmn_aQMfbW0fBrtVq7-wF6y0rxtUOK2KOYDyU1Q7HFeBwThSREHFPcYF9Qh0lQXrKjxSLlJZSMgxu5BMtNPW0Qxto9bf3QhgtzEmn1z8_MxWxE8nBIPz5DLwi0W_PS2Uh
ClassificationCodes TM615
ContentType Journal Article
Copyright Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
Copyright_xml – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
DBID 2B.
4A8
92I
93N
PSX
TCJ
DOI 10.19753/j.issn1001-1390.2020.06.011
DatabaseName Wanfang Data Journals - Hong Kong
WANFANG Data Centre
Wanfang Data Journals
万方数据期刊 - 香港版
China Online Journals (COJ)
China Online Journals (COJ)
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
DocumentTitle_FL Short-term PV power prediction based on particle swarm optimization combined with chaos crossover for deep belief networks
EndPage 72
ExternalDocumentID dcyyb202006011
GroupedDBID -03
2B.
4A8
5XA
5XD
92H
92I
93N
ABJNI
ACGFS
ADMLS
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CEKLB
CW9
GROUPED_DOAJ
PSX
TCJ
TGT
U1G
U5M
ID FETCH-LOGICAL-s1011-d54a56bc39047a04662e2c0d68e6a19234ec0c8e9de5defccc5107d92faf38f43
ISSN 1001-1390
IngestDate Thu May 29 04:07:58 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 6
Keywords 粒子群算法
预测精度
混沌横纵交叉
深度信念网络
光伏功率预测
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-s1011-d54a56bc39047a04662e2c0d68e6a19234ec0c8e9de5defccc5107d92faf38f43
PageCount 6
ParticipantIDs wanfang_journals_dcyyb202006011
PublicationCentury 2000
PublicationDate 2020-03-25
PublicationDateYYYYMMDD 2020-03-25
PublicationDate_xml – month: 03
  year: 2020
  text: 2020-03-25
  day: 25
PublicationDecade 2020
PublicationTitle 电测与仪表
PublicationTitle_FL Electrical Measurement & Instrumentation
PublicationYear 2020
Publisher 天津理工大学电气电子工程学院,天津,300384
Publisher_xml – name: 天津理工大学电气电子工程学院,天津,300384
SSID ssib051374602
ssj0039791
ssib001129792
Score 2.2535605
Snippet TM615;...
SourceID wanfang
SourceType Aggregation Database
StartPage 67
Title 基于结合混沌纵横交叉的PSO-DBN的短期光伏功率预测
URI https://d.wanfangdata.com.cn/periodical/dcyyb202006011
Volume 57
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LaxRBEG6SCKIH8Ylvc0gfJ86jn8eZyS5BMAomsLcwMz1jTiuYzSE5RzQqieBNhAXxIIIXIQRWf84-8F9Y1TO7O8HgC5ahtrqr-uuumanq7uluQhZSBW61SBPHl17mMM9IJzVe4Rhfi5TnaZLZAbf7K2J5jd1r8dbMbKv21dJWJ13Mdk5cV_I_VgUe2BVXyf6DZSdKgQE02BeuYGG4_pWNaYNT3aRRSBsMr6pBG5JGEdWBTXKpUrQhaCTxh4RPVWzzhDTiyAkVDcfiIUMp1aRKYx4NCtnDRw-cpWhl8t8STRouobCOkUYZbmVAS4zyyAltkkRMCsrWNPStuMCCVVQPiq1OVgGyqVaVsvVhWB-ECEA9gDu-Q7AQQKE1pkRQiK6nKFtpIEC4ah7EWh_jgA6tGzjlemh7V1qNjIbawgCCW2AuKkNN0nLKPLIqPhTHwbtITzklRLcmLrHFsYITcY11gMbx4xMA-HGAs6qs5jvw6zQIqN26cyl3364eorqnKA8hqWKO8vSiX7wZLnq27gz1T9QvYgvZTWcrN3V8v3CTbW-nmAV32vFmySlfQlRZG2ywgTKEeXI6n869QDLhTjZew8leO1QxLvM0WRgDuvsbOHblW7tI2o9rQdrqeXKu6l3Nh-WjcoHM7GxcJGdre25eIvGg2-v39kff3g7evBgeHQ2_vh71DoefPvd7HwcHe6N3u9UtD9So-2X4vjt4ttf_fjB42R3tP__xYXd4-OoyWWs2VuNlpzpJxNn0cA7AcJZwkWYAl8nEZUL4uZ-5RqhcJNjHYXnmZirXJucmL7IsA1cljfaLpAhUwYIrZK79pJ1fJfOB4FoYDRlAyvgshaZKJPCUgZcyV9fInaoJ1qs3xeb6cZNc_2OOG-TM9Cm4SeY6T7fyWxD7dtLb1ow_Ab--gyQ
linkProvider Directory of Open Access Journals
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8E%E7%BB%93%E5%90%88%E6%B7%B7%E6%B2%8C%E7%BA%B5%E6%A8%AA%E4%BA%A4%E5%8F%89%E7%9A%84PSO-DBN%E7%9A%84%E7%9F%AD%E6%9C%9F%E5%85%89%E4%BC%8F%E5%8A%9F%E7%8E%87%E9%A2%84%E6%B5%8B&rft.jtitle=%E7%94%B5%E6%B5%8B%E4%B8%8E%E4%BB%AA%E8%A1%A8&rft.au=%E5%AD%99%E8%BE%89&rft.au=%E5%86%B7%E5%BB%BA%E4%BC%9F&rft.date=2020-03-25&rft.pub=%E5%A4%A9%E6%B4%A5%E7%90%86%E5%B7%A5%E5%A4%A7%E5%AD%A6%E7%94%B5%E6%B0%94%E7%94%B5%E5%AD%90%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E5%A4%A9%E6%B4%A5%2C300384&rft.issn=1001-1390&rft.volume=57&rft.issue=6&rft.spage=67&rft.epage=72&rft_id=info:doi/10.19753%2Fj.issn1001-1390.2020.06.011&rft.externalDocID=dcyyb202006011
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fdcyyb%2Fdcyyb.jpg