短期负荷局部线性嵌入流形学习预测法
考虑短期日负荷预测各时刻点之间的整体性和相关性，提出一种从整体上刻画和预测短期日负荷的新方法。将日24点负荷数据值看作一个24维数据集，从多维角度挖掘负荷复杂的变化规律，建立高维预测模型。利用流形学习理论对建立的高维模型进行有效降维，从而提取高维空间数据的固有属性和整体几何规律，揭示其蕴含的有效信息。采用局部线性嵌入法（locallyl Inearembedding，LLE）对24维负荷数据进行非绳l生降维，在低维空间内进行负荷预测，再用LLE重构得到24个时刻的预测值。仿真结果表明本文提出方法相比于传统一维分量预测法精度更高、速度更快。...
Saved in:
| Published in | 电力系统保护与控制 Vol. 40; no. 7; pp. 25 - 30 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
四川大学电气信息学院,四川成都,610065%四川大学电气信息学院,四川成都610065
2012
智能电网四川省重点实验室,四川成都610065 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1674-3415 |
| DOI | 10.3969/j.issn.1674-3415.2012.07.005 |
Cover
| Abstract | 考虑短期日负荷预测各时刻点之间的整体性和相关性，提出一种从整体上刻画和预测短期日负荷的新方法。将日24点负荷数据值看作一个24维数据集，从多维角度挖掘负荷复杂的变化规律，建立高维预测模型。利用流形学习理论对建立的高维模型进行有效降维，从而提取高维空间数据的固有属性和整体几何规律，揭示其蕴含的有效信息。采用局部线性嵌入法（locallyl Inearembedding，LLE）对24维负荷数据进行非绳l生降维，在低维空间内进行负荷预测，再用LLE重构得到24个时刻的预测值。仿真结果表明本文提出方法相比于传统一维分量预测法精度更高、速度更快。 |
|---|---|
| AbstractList | 考虑短期日负荷预测各时刻点之间的整体性和相关性，提出一种从整体上刻画和预测短期日负荷的新方法。将日24点负荷数据值看作一个24维数据集，从多维角度挖掘负荷复杂的变化规律，建立高维预测模型。利用流形学习理论对建立的高维模型进行有效降维，从而提取高维空间数据的固有属性和整体几何规律，揭示其蕴含的有效信息。采用局部线性嵌入法（locallyl Inearembedding，LLE）对24维负荷数据进行非绳l生降维，在低维空间内进行负荷预测，再用LLE重构得到24个时刻的预测值。仿真结果表明本文提出方法相比于传统一维分量预测法精度更高、速度更快。 TM715; 考虑短期日负荷预测各时刻点之间的整体性和相关性,提出一种从整体上刻画和预测短期日负荷的新方法.将日24点负荷数据值看作一个24维数据集,从多维角度挖掘负荷复杂的变化规律,建立高维预测模型.利用流形学习理论对建立的高维模型进行有效降维,从而提取高维空间数据的固有属性和整体几何规律,揭示其蕴含的有效信息.采用局部线性嵌入法(locally linear embedding,LLE)对24维负荷数据进行非线性降维,在低维空间内进行负荷预测,再用LLE重构得到24个时刻的预测值.仿真结果表明本文提出方法相比于传统一维分量预测法精度更高、速度更快. |
| Author | 黄静 肖先勇 刘旭娜 |
| AuthorAffiliation | 四川大学电气信息学院,四川成都610065 智能电网四川省重点实验室,四川成都610065 |
| AuthorAffiliation_xml | – name: 四川大学电气信息学院,四川成都,610065%四川大学电气信息学院,四川成都610065;智能电网四川省重点实验室,四川成都610065 |
| Author_FL | XIAO Xian-yong HUANG Jing LIU Xu-na |
| Author_FL_xml | – sequence: 1 fullname: HUANG Jing – sequence: 2 fullname: XIAO Xian-yong – sequence: 3 fullname: LIU Xu-na |
| Author_xml | – sequence: 1 fullname: 黄静 肖先勇 刘旭娜 |
| BookMark | eNo9j81Kw0AcxPdQwVr7EEKvif_dze52j1L8goKX3sM2u6kpurEJIt4q6kl79CZUCn5cehAtoiC-jEnbtzBS8TAMDD9mmBVUsrE1CNUwuFRyud51ozS1LubCc6iHmUsAExeEC8BKqPyfL6NqmkZtAIoZ43VZRnQ6HOe3w9nrcDZ4y5778_On6cdX3n_MJtfZ5X0-Ocs-R9n44fv9bj66yCdX-cvNKloK1UFqqn9eQa2tzVZjx2nube82NppOwBhzTMg5NUSGnFJiBJcK2qCE1lryQoLrALMgkFAXnjKBUJSHbUKYYqHxFNG0gmqL2hNlQ2U7fjc-Tmwx6Hd17_cgiOJega0tsGA_tp1eVIBHSXSoklPfwxRknQL9AQvNZ6o |
| ClassificationCodes | TM715 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP W92 ~WA 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.3969/j.issn.1674-3415.2012.07.005 |
| DatabaseName | 中文期刊服务平台 中文科技期刊数据库-CALIS站点 中文科技期刊数据库-7.0平台 中文科技期刊数据库-工程技术 中文科技期刊数据库- 镜像站点 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 |
| DocumentTitleAlternate | Short-term load forecasting based on manifold learning and locally linear embedding theory |
| DocumentTitle_FL | Short-term load forecasting based on manifold learning and locally linear embedding theory |
| EndPage | 30 |
| ExternalDocumentID | jdq201207005 41309830 |
| GroupedDBID | -03 2RA 5XA 5XD 92L ALMA_UNASSIGNED_HOLDINGS CCEZO CEKLB CQIGP GROUPED_DOAJ U1G W92 ~WA 2B. 4A8 92I 93N PSX TCJ |
| ID | FETCH-LOGICAL-c555-ef663e29f6332e769a0b0a7ddd96dd976dc15cc90874aec7a36fb225a5fe4a2d3 |
| ISSN | 1674-3415 |
| IngestDate | Thu May 29 03:55:48 EDT 2025 Wed Feb 14 10:47:31 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 7 |
| Keywords | 流形学习 最小二乘支持向量机 负荷预测 局部线性嵌入 非线性降维 |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c555-ef663e29f6332e769a0b0a7ddd96dd976dc15cc90874aec7a36fb225a5fe4a2d3 |
| Notes | load forecast; manifold learning; locally linear embedding; nonlinear dimension reduction; least square support vector machine Considering the integrity and strong relevance of each point in short-term daily load value, the paper proposes a new model exposing and forecasting the daily load as a whole. We consider the 24 daily load values as a 24-dimensional data set, study the complex load changing rule from the multi-dimensional perspective and establish a high-dimensional load forecasting model. We use manifold theory to make an effective dimension reduction of the high-dimensional model, thus extract the inherence characteristics and overall regularity of the high-dimensional data, revealing the useful information it contains. We adopt locally linear embedding （LLE） to make a nonlinear dimension reduction for the 24-dimendional load data, and forecast the load in low dimensionality space, and then reconstruct the 24 load prediction based on the LLE reconstruction algorithm. Simulation results show that the |
| PageCount | 6 |
| ParticipantIDs | wanfang_journals_jdq201207005 chongqing_primary_41309830 |
| PublicationCentury | 2000 |
| PublicationDate | 2012 |
| PublicationDateYYYYMMDD | 2012-01-01 |
| PublicationDate_xml | – year: 2012 text: 2012 |
| PublicationDecade | 2010 |
| PublicationTitle | 电力系统保护与控制 |
| PublicationTitleAlternate | Relay |
| PublicationTitle_FL | Power System Protection and Control |
| PublicationYear | 2012 |
| Publisher | 四川大学电气信息学院,四川成都,610065%四川大学电气信息学院,四川成都610065 智能电网四川省重点实验室,四川成都610065 |
| Publisher_xml | – name: 智能电网四川省重点实验室,四川成都610065 – name: 四川大学电气信息学院,四川成都,610065%四川大学电气信息学院,四川成都610065 |
| SSID | ssib003155689 ssib017479473 ssib023166999 ssj0002912115 ssib051374514 ssib002424069 ssib036435463 |
| Score | 1.9664446 |
| Snippet | 考虑短期日负荷预测各时刻点之间的整体性和相关性，提出一种从整体上刻画和预测短期日负荷的新方法。将日24点负荷数据值看作一个24维数据集，从多维角度挖掘负荷复杂的变化... TM715; 考虑短期日负荷预测各时刻点之间的整体性和相关性,提出一种从整体上刻画和预测短期日负荷的新方法.将日24点负荷数据值看作一个24维数据集,从多维角度挖掘负荷复杂... |
| SourceID | wanfang chongqing |
| SourceType | Aggregation Database Publisher |
| StartPage | 25 |
| SubjectTerms | 局部线性嵌入 最小二乘支持向量机 流形学习 负荷预测 非线性降维 |
| Title | 短期负荷局部线性嵌入流形学习预测法 |
| URI | http://lib.cqvip.com/qk/90494A/201207/41309830.html https://d.wanfangdata.com.cn/periodical/jdq201207005 |
| Volume | 40 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: Directory of Open Access Journals issn: 1674-3415 databaseCode: DOA dateStart: 20080101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0002912115 providerName: Directory of Open Access Journals |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR3LbtNAcBWKhLggECBaKOqhe6pcHO_7aCeOKiQ4Bam3yM9WHFJa0ktPRcAJOHJDCqrE49IDggiBBPwMSdu_YGbtOBZCFXCINRnv7M7s2Dsz651dQpaFdk0s3MRRuWEOlx53IggLnJinntCp8LjCfOc7d-XaPX57Xaw3Gt9rq5Z2B_FqsvfHvJL_0SrgQK-YJfsPmq0qBQTAoF-4gobh-lc6pqGipkP9Ng0lNS2EQ00DXgK6TQNFQ0GDJi5oCA3VjPoaqQKfBh2kArxflBFUtxDQgvoCbyGmaW-1qe8hAA35koacBob6tkLAaz4tHFiA0eI8y6nLa5nkWAAr96kJLAOMBgUQWG458mOsIFDG5xYDIoQWE5ZMapCumkrE9oEa2zdIasyKldqjRpaCQPnQMqbVSklvtO0sZXtNYHeYVn3qo1mbB8XcCQcMsKgP5MW-T-UDq-qjsqjZ9-Iz0O-WgxlprOXA6ler6nHtn2d3d3XFzGJW6xjRAzCauWfIWQ_ng2ohfen-YGJxbfzE_d6q_xAMwmg4cx_B1ZbSzMJDBt5i_bQC0WSKi_KrN3oansEN-nCVbsXwObJcSnPrNFlwJ5HNrf7GNvhHNl2tn0f9jZpn1b1ILpQh0ZJfPN-XSGNv8zJhR8PDyavh8afh8YvP4w_7J4_fH339Mdl_Nx49Hz99Mxk9Gn87GB--_fnl9cnBk8no2eTjyyuk2wm7rTWnPODDSYQQTpaDu5t5JpeMeZmSJnJjN1JpmhoJPyXTpCmSxLha8ShLVMRkHoP9iUSe8chL2VUy19_qZ9fIElc61jqKci_VPFFJHIk4hsg-1ZlRTLrzZKGSt_eg2MelN9XePFksO6BXvtwPe_fTbewuMIiuWDiN-Do5jwWLWbkbZG6ws5stgp86iG_ah-EX-Zxsig |
| 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=%E7%9F%AD%E6%9C%9F%E8%B4%9F%E8%8D%B7%E5%B1%80%E9%83%A8%E7%BA%BF%E6%80%A7%E5%B5%8C%E5%85%A5%E6%B5%81%E5%BD%A2%E5%AD%A6%E4%B9%A0%E9%A2%84%E6%B5%8B%E6%B3%95&rft.jtitle=%E7%94%B5%E5%8A%9B%E7%B3%BB%E7%BB%9F%E4%BF%9D%E6%8A%A4%E4%B8%8E%E6%8E%A7%E5%88%B6&rft.au=%E9%BB%84%E9%9D%99+%E8%82%96%E5%85%88%E5%8B%87+%E5%88%98%E6%97%AD%E5%A8%9C&rft.date=2012&rft.issn=1674-3415&rft.volume=40&rft.issue=7&rft.spage=25&rft.epage=30&rft_id=info:doi/10.3969%2Fj.issn.1674-3415.2012.07.005&rft.externalDocID=41309830 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F90494A%2F90494A.jpg http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjdq%2Fjdq.jpg |