Article(id=1223202686414209361, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20230949, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1685808000000, receivedDateStr=2023-06-04, revisedDate=1689177600000, revisedDateStr=2023-07-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1769563846448, onlineDateStr=2026-01-28, pubDate=1703433600000, pubDateStr=2023-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769563846448, onlineIssueDateStr=2026-01-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769563846448, creator=13701087609, updateTime=1769563846448, updator=13701087609, issue=Issue{id=1223202678788965355, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='12', pageStart='1', pageEnd='228', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769563844630, creator=13701087609, updateTime=1769563913308, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1223202966899901286, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1223202966899901287, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1223202678788965355, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=220, endPage=224, ext={EN=ArticleExt(id=1223202688704299499, articleId=1223202686414209361, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Photovoltaic Power Probabilistic Prediction Based on BRICH Clustering and Copula-Monte Carlo Simulation, columnId=1222940986393744094, journalTitle=Water Resources and Power, columnName=ENERGY, runingTitle=null, highlight=null, articleAbstract=

Photovoltaic power generation is affected by the chaotic characteristics of meteorology, and its stochastic, volatile and intermittent characteristics affect the operation of power systems seriously. Aiming at the problem of large dimension of original PV power generation data and the vulnerability of power generation to weather conditions, a data processing method based on Principal Component Analysis (PCA) and BRICH clustering was proposed to reduce the dimensionality of model input variables and facilitate statistical modeling. Secondly, a Copula-Monte Carlo-based probabilistic PV power probabilistic prediction model was constructed to calculate the probabilistic interval prediction of PV power output given the future point prediction values. The model was evaluated based on interval coverage and average width of prediction interval. Finally, the summer data of the actual photovoltaic power station were taken as an example for verification analysis. The results show that the Copula-Monte Carlo method can intuitively show the fluctuation range and expected value of photovoltaic power generation, and is superior to other power prediction models.

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光伏发电功率受气象的混沌特性影响,其随机性、波动性、间歇性对电力系统的运行影响较严重。针对原始光伏发电数据维度大及发电功率易受天气状况影响的问题,提出了基于主成分分析和BRICH聚类的数据处理方法,从而降低模型输入变量维度,易于统计建模;其次,搭建了基于Copula-Monte Carlo的光伏发电功率概率预测模型,计算光伏出力在给定未来点预测值的情况下实际出力的概率区间预测,并基于区间覆盖率、预测区间平均宽度对模型进行评价。最后,以实际光伏电站夏季数据为例进行验证分析。结果表明,Copula-Monte Carlo方法可直观展示光伏发电功率的波动区间和期望值,且优于其他功率预测模型。

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黄牧涛(1973-),女,博士、研究员,研究方向为新型电力系统精准预测与优化调度、能源博弈论、智慧综合能源系统规划与调控等,E-mail:

, authorsList=黄牧涛, 高素花, 汪旸, 曾令康, 魏聪颖, 陈兴邦)}, authors=[Author(id=1223202689610269275, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223202686414209361, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=huangmutao@hust.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1223202689731904102, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223202686414209361, authorId=1223202689610269275, language=EN, stringName=Mu-tao HUANG, firstName=Mu-tao, middleName=null, lastName=HUANG, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1a, address=1a.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1223202689832567408, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1223202686414209361, authorId=1223202689610269275, language=CN, stringName=黄牧涛, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1a, address=1.a.华中科技大学电气与电子工程学院,湖北 武汉 430074, bio={"content":"

黄牧涛(1973-),女,博士、研究员,研究方向为新型电力系统精准预测与优化调度、能源博弈论、智慧综合能源系统规划与调控等,E-mail:

"}, bioImg=null, bioContent=

黄牧涛(1973-),女,博士、研究员,研究方向为新型电力系统精准预测与优化调度、能源博弈论、智慧综合能源系统规划与调控等,E-mail:

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基于BRICH聚类和Copula-Monte Carlo模拟的光伏发电功率概率预测
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黄牧涛 1a , 高素花 1b , 汪旸 2 , 曾令康 2 , 魏聪颖 2 , 陈兴邦 1a
水电能源科学 | 能源 2023,41(12): 220-224
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水电能源科学 | 能源 2023, 41(12): 220-224
基于BRICH聚类和Copula-Monte Carlo模拟的光伏发电功率概率预测
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黄牧涛1a , 高素花1b, 汪旸2, 曾令康2, 魏聪颖2, 陈兴邦1a
作者信息
  • 1.a.华中科技大学电气与电子工程学院,湖北 武汉 430074
  • 1.b.华中科技大学土木与水利工程学院,湖北 武汉 430074
  • 2.国家电网有限公司华中分部调度控制中心,湖北 武汉 430077
  • 黄牧涛(1973-),女,博士、研究员,研究方向为新型电力系统精准预测与优化调度、能源博弈论、智慧综合能源系统规划与调控等,E-mail:

Photovoltaic Power Probabilistic Prediction Based on BRICH Clustering and Copula-Monte Carlo Simulation
Mu-tao HUANG1a , Su-hua GAO1b, Yang WANG2, Ling-kang ZENG2, Cong-ying WEI2, Xing-bang CHEN1a
Affiliations
  • 1a.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 1b.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 2.Dispatching and Control Center of Central China Branch of State Grid Corporation of China, Wuhan 430077, China
出版时间: 2023-12-25 doi: 10.20040/j.cnki.1000-7709.2023.20230949
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光伏发电功率受气象的混沌特性影响,其随机性、波动性、间歇性对电力系统的运行影响较严重。针对原始光伏发电数据维度大及发电功率易受天气状况影响的问题,提出了基于主成分分析和BRICH聚类的数据处理方法,从而降低模型输入变量维度,易于统计建模;其次,搭建了基于Copula-Monte Carlo的光伏发电功率概率预测模型,计算光伏出力在给定未来点预测值的情况下实际出力的概率区间预测,并基于区间覆盖率、预测区间平均宽度对模型进行评价。最后,以实际光伏电站夏季数据为例进行验证分析。结果表明,Copula-Monte Carlo方法可直观展示光伏发电功率的波动区间和期望值,且优于其他功率预测模型。

概率预测  /  光伏发电功率  /  BRICH聚类  /  Copula函数  /  Monte Carlo模拟  /  天气分型

Photovoltaic power generation is affected by the chaotic characteristics of meteorology, and its stochastic, volatile and intermittent characteristics affect the operation of power systems seriously. Aiming at the problem of large dimension of original PV power generation data and the vulnerability of power generation to weather conditions, a data processing method based on Principal Component Analysis (PCA) and BRICH clustering was proposed to reduce the dimensionality of model input variables and facilitate statistical modeling. Secondly, a Copula-Monte Carlo-based probabilistic PV power probabilistic prediction model was constructed to calculate the probabilistic interval prediction of PV power output given the future point prediction values. The model was evaluated based on interval coverage and average width of prediction interval. Finally, the summer data of the actual photovoltaic power station were taken as an example for verification analysis. The results show that the Copula-Monte Carlo method can intuitively show the fluctuation range and expected value of photovoltaic power generation, and is superior to other power prediction models.

probabilistic prediction  /  PV power generation  /  BRICH clustering  /  Copula function  /  Monte Carlo simulation  /  weather fractal
黄牧涛, 高素花, 汪旸, 曾令康, 魏聪颖, 陈兴邦. 基于BRICH聚类和Copula-Monte Carlo模拟的光伏发电功率概率预测. 水电能源科学, 2023 , 41 (12) : 220 -224 . DOI: 10.20040/j.cnki.1000-7709.2023.20230949
Mu-tao HUANG, Su-hua GAO, Yang WANG, Ling-kang ZENG, Cong-ying WEI, Xing-bang CHEN. Photovoltaic Power Probabilistic Prediction Based on BRICH Clustering and Copula-Monte Carlo Simulation[J]. Water Resources and Power, 2023 , 41 (12) : 220 -224 . DOI: 10.20040/j.cnki.1000-7709.2023.20230949
  • 国家电网有限公司科技项目资助(SGHZ0000DKJS2200330)
2023年第41卷第12期
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doi: 10.20040/j.cnki.1000-7709.2023.20230949
  • 接收时间:2023-06-04
  • 首发时间:2026-01-28
  • 出版时间:2023-12-25
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  • 收稿日期:2023-06-04
  • 修回日期:2023-07-13
基金
国家电网有限公司科技项目资助(SGHZ0000DKJS2200330)
作者信息
    1.a.华中科技大学电气与电子工程学院,湖北 武汉 430074
    1.b.华中科技大学土木与水利工程学院,湖北 武汉 430074
    2.国家电网有限公司华中分部调度控制中心,湖北 武汉 430077
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species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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