Article(id=1222493246706671748, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222493244286558340, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202212235, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1669910400000, receivedDateStr=2022-12-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769394702840, onlineDateStr=2026-01-26, pubDate=1692892800000, pubDateStr=2023-08-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769394702840, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769394702840, creator=13701087609, updateTime=1769394702840, updator=13701087609, issue=Issue{id=1222493244286558340, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='8', pageStart='1', pageEnd='196', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769394702264, creator=13701087609, updateTime=1769394819736, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222493737050169898, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222493244286558340, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222493737050169899, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222493244286558340, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=162, endPage=171, ext={EN=ArticleExt(id=1222493246983495814, articleId=1222493246706671748, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Distributed photovoltaic ultra-short-term power prediction method based on combined neural network, columnId=1211002409397129992, journalTitle=Thermal Power Generation, columnName=Power generation technology forum, runingTitle=null, highlight=null, articleAbstract=

The penetration rate of distributed photovoltaic power stations in the power system is increasing year by year, to ensure the safe and stable operation of the power grid, a distributed photovoltaic ultra-short-term power prediction method based on combined neural networks is proposed. Firstly, a 1DCNN&1DCNN-LSTM combined neural network model is constructed by using 1D convolutional neural network (1DCNN) and long short-term memory (LSTM) neural networks, to obtain multi location numerical weather prediction (NWP) information and historical power information, using combined neural network model for spatially correlated photovoltaic power prediction and time series prediction; and a fully connected neural network (FCNN) is added to the combined neural network model, which is used to learn and assign weights to the two prediction results, achieving ultra-short-term prediction of distributed photovoltaic power generation. The validation was conducted using measured data from a photovoltaic power station in Hebei, and the results showed that this method can effectively improve the accuracy of distributed photovoltaic prediction and has certain practical value.

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分布式光伏电站在电力系统中的渗透率逐年升高,为保障电网安全稳定运行,提出一种基于组合神经网络的分布式光伏超短期功率预测方法。首先利用一维卷积神经网络(1DCNN)与长短时记忆(LSTM)神经网络构建1DCNN&1DCNN-LSTM组合神经网络模型,获取多位置数值天气预报(NWP)信息与历史功率信息;然后利用组合神经网络模型进行空间相关性光伏功率预测与时间序列预测,并在组合神经网络模型中加入全连接神经网络(FCNN),利用全连接神经网络对2种预测结果进行学习与权重分配,实现了分布式光伏发电功率的超短期预测。采用河北某光伏电站实测数据进行验证,验证结果表明,该方法能够有效提高分布式光伏预测精度,具有一定的实用价值。

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马文兵(1997),男,硕士研究生,主要研究方向为新能源发电与机器学习方面,
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杨锡运(1973),女,教授,博士生导师,主要研究方向为新能源发电控制,

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杨锡运(1973),女,教授,博士生导师,主要研究方向为新能源发电控制,

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基于组合神经网络的分布式光伏超短期功率预测方法
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杨锡运 1 , 马文兵 1 , 彭琰 2 , 孟令卓超 1 , 王晨旭 2 , 马骏超 2
热力发电 | 发电技术论坛 2023,52(8): 162-171
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热力发电 | 发电技术论坛 2023, 52(8): 162-171
基于组合神经网络的分布式光伏超短期功率预测方法
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杨锡运1 , 马文兵1 , 彭琰2, 孟令卓超1, 王晨旭2, 马骏超2
作者信息
  • 1.华北电力大学控制与计算机工程学院,北京 102206
  • 2.国网浙江省电力有限公司电力科学研究院,浙江 杭州 310014
  • 杨锡运(1973),女,教授,博士生导师,主要研究方向为新能源发电控制,

通讯作者:

马文兵(1997),男,硕士研究生,主要研究方向为新能源发电与机器学习方面,
Distributed photovoltaic ultra-short-term power prediction method based on combined neural network
Xiyun YANG1 , Wenbing MA1 , Yan PENG2, Lingzhuochao MENG1, Chenxu WANG2, Junchao MA2
Affiliations
  • 1.School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
  • 2.Electric Power Research Institute of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou 310014, China
出版时间: 2023-08-25 doi: 10.19666/j.rlfd.202212235
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分布式光伏电站在电力系统中的渗透率逐年升高,为保障电网安全稳定运行,提出一种基于组合神经网络的分布式光伏超短期功率预测方法。首先利用一维卷积神经网络(1DCNN)与长短时记忆(LSTM)神经网络构建1DCNN&1DCNN-LSTM组合神经网络模型,获取多位置数值天气预报(NWP)信息与历史功率信息;然后利用组合神经网络模型进行空间相关性光伏功率预测与时间序列预测,并在组合神经网络模型中加入全连接神经网络(FCNN),利用全连接神经网络对2种预测结果进行学习与权重分配,实现了分布式光伏发电功率的超短期预测。采用河北某光伏电站实测数据进行验证,验证结果表明,该方法能够有效提高分布式光伏预测精度,具有一定的实用价值。

分布式光伏  /  超短期功率预测  /  LSTM  /  1DCNN  /  深度学习

The penetration rate of distributed photovoltaic power stations in the power system is increasing year by year, to ensure the safe and stable operation of the power grid, a distributed photovoltaic ultra-short-term power prediction method based on combined neural networks is proposed. Firstly, a 1DCNN&1DCNN-LSTM combined neural network model is constructed by using 1D convolutional neural network (1DCNN) and long short-term memory (LSTM) neural networks, to obtain multi location numerical weather prediction (NWP) information and historical power information, using combined neural network model for spatially correlated photovoltaic power prediction and time series prediction; and a fully connected neural network (FCNN) is added to the combined neural network model, which is used to learn and assign weights to the two prediction results, achieving ultra-short-term prediction of distributed photovoltaic power generation. The validation was conducted using measured data from a photovoltaic power station in Hebei, and the results showed that this method can effectively improve the accuracy of distributed photovoltaic prediction and has certain practical value.

distributed photovoltaic  /  ultra-short-term power prediction  /  LSTM  /  1DCNN  /  deep learning
杨锡运, 马文兵, 彭琰, 孟令卓超, 王晨旭, 马骏超. 基于组合神经网络的分布式光伏超短期功率预测方法. 热力发电, 2023 , 52 (8) : 162 -171 . DOI: 10.19666/j.rlfd.202212235
Xiyun YANG, Wenbing MA, Yan PENG, Lingzhuochao MENG, Chenxu WANG, Junchao MA. Distributed photovoltaic ultra-short-term power prediction method based on combined neural network[J]. Thermal Power Generation, 2023 , 52 (8) : 162 -171 . DOI: 10.19666/j.rlfd.202212235
  • 国网浙江省电力有限公司科技项目(5211DS220009)
2023年第52卷第8期
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doi: 10.19666/j.rlfd.202212235
  • 接收时间:2022-12-02
  • 首发时间:2026-01-26
  • 出版时间:2023-08-25
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  • 收稿日期:2022-12-02
基金
Science and Technology Project of State Grid Zhejiang Electric Power Co., Ltd.(5211DS220009)
国网浙江省电力有限公司科技项目(5211DS220009)
作者信息
    1.华北电力大学控制与计算机工程学院,北京 102206
    2.国网浙江省电力有限公司电力科学研究院,浙江 杭州 310014

通讯作者:

马文兵(1997),男,硕士研究生,主要研究方向为新能源发电与机器学习方面,
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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