基于BP神经网络的短期负荷预测
电力系统负荷预测的重要性、分类和主要预测方法,BP神经网络算法的基本理论和预测过程,建立基于BP神经网络的短期负荷预测模型,以加州24 h的电力负荷预测为例进行MATLAB仿真,结果显示预测精度符合电力系统要求。
Gespeichert in:
| Veröffentlicht in: | 设备管理与维修 2017-12 (16), p.27-28 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | chi |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 28 |
|---|---|
| container_issue | 16 |
| container_start_page | 27 |
| container_title | 设备管理与维修 |
| container_volume | |
| creator | 陆万雨 |
| description | 电力系统负荷预测的重要性、分类和主要预测方法,BP神经网络算法的基本理论和预测过程,建立基于BP神经网络的短期负荷预测模型,以加州24 h的电力负荷预测为例进行MATLAB仿真,结果显示预测精度符合电力系统要求。 |
| doi_str_mv | 10.16621/j.cnki.issn1001-0599.2017.11.12 |
| format | Article |
| fullrecord | <record><control><sourceid>cass_wanfa</sourceid><recordid>TN_cdi_wanfang_journals_sbglywx201716015</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cass_id>673590088</cass_id><wanfj_id>sbglywx201716015</wanfj_id><sourcerecordid>673590088</sourcerecordid><originalsourceid>FETCH-LOGICAL-c525-98eaea2249fe39c19289e93d31acb66bd8fad00902fa24e7d107e9eb0329e98b3</originalsourceid><addsrcrecordid>eNo9j71KA0EUhadQMMS8gE8Qix3vncnO7i01-AcBLdIvM7uzITGM4CDRPp2o2ErIgmBppwgJvo1m41u4ErE6cPg4H4exJgJHpQTuDHjqzvq8771DAAwgJOICMOKIHMUaq_3XG6zhfd8AACmSBDW2_VXMPmd3e6fl87Sc35cfD-V8Uj6Oy-JlMSmWr8Xy9v37abx4u9lk67keetv4yzrrHux320dB5-TwuL3bCdJQhAHFVlstRItyKylFEjFZkplEnRqlTBbnOqv0IHItWjbKECJL1oAUFRcbWWfN1exIu1y7XjI4v7xwlTDxpje8Hl39PkMFGFbo1gpNtfeJ8z5LVCRDAohj-QP_O13F</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>基于BP神经网络的短期负荷预测</title><source>国家哲学社会科学学术期刊数据库 (National Social Sciences Database)</source><creator>陆万雨</creator><creatorcontrib>陆万雨</creatorcontrib><description>电力系统负荷预测的重要性、分类和主要预测方法,BP神经网络算法的基本理论和预测过程,建立基于BP神经网络的短期负荷预测模型,以加州24 h的电力负荷预测为例进行MATLAB仿真,结果显示预测精度符合电力系统要求。</description><identifier>ISSN: 1001-0599</identifier><identifier>DOI: 10.16621/j.cnki.issn1001-0599.2017.11.12</identifier><language>chi</language><publisher>中国机械工程学会</publisher><ispartof>设备管理与维修, 2017-12 (16), p.27-28</ispartof><rights>Copyright © Wanfang Data Co. Ltd. All Rights Reserved.</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Uhttp://www.wanfangdata.com.cn/images/PeriodicalImages/sbglywx/sbglywx.jpg</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>陆万雨</creatorcontrib><title>基于BP神经网络的短期负荷预测</title><title>设备管理与维修</title><description>电力系统负荷预测的重要性、分类和主要预测方法,BP神经网络算法的基本理论和预测过程,建立基于BP神经网络的短期负荷预测模型,以加州24 h的电力负荷预测为例进行MATLAB仿真,结果显示预测精度符合电力系统要求。</description><issn>1001-0599</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNo9j71KA0EUhadQMMS8gE8Qix3vncnO7i01-AcBLdIvM7uzITGM4CDRPp2o2ErIgmBppwgJvo1m41u4ErE6cPg4H4exJgJHpQTuDHjqzvq8771DAAwgJOICMOKIHMUaq_3XG6zhfd8AACmSBDW2_VXMPmd3e6fl87Sc35cfD-V8Uj6Oy-JlMSmWr8Xy9v37abx4u9lk67keetv4yzrrHux320dB5-TwuL3bCdJQhAHFVlstRItyKylFEjFZkplEnRqlTBbnOqv0IHItWjbKECJL1oAUFRcbWWfN1exIu1y7XjI4v7xwlTDxpje8Hl39PkMFGFbo1gpNtfeJ8z5LVCRDAohj-QP_O13F</recordid><startdate>20171201</startdate><enddate>20171201</enddate><creator>陆万雨</creator><general>中国机械工程学会</general><general>北京卓众出版有限公司</general><general>安徽省淮北一中,安徽淮北,235000</general><scope>NSCOK</scope><scope>2B.</scope><scope>4A8</scope><scope>92I</scope><scope>93N</scope><scope>PSX</scope><scope>TCJ</scope></search><sort><creationdate>20171201</creationdate><title>基于BP神经网络的短期负荷预测</title><author>陆万雨</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c525-98eaea2249fe39c19289e93d31acb66bd8fad00902fa24e7d107e9eb0329e98b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>chi</language><creationdate>2017</creationdate><toplevel>online_resources</toplevel><creatorcontrib>陆万雨</creatorcontrib><collection>国家哲学社会科学文献中心 (National Center for Philosophy and Social Sciences Documentation)</collection><collection>Wanfang Data Journals - Hong Kong</collection><collection>WANFANG Data Centre</collection><collection>Wanfang Data Journals</collection><collection>万方数据期刊 - 香港版</collection><collection>China Online Journals (COJ)</collection><collection>China Online Journals (COJ)</collection><jtitle>设备管理与维修</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>陆万雨</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>基于BP神经网络的短期负荷预测</atitle><jtitle>设备管理与维修</jtitle><date>2017-12-01</date><risdate>2017</risdate><issue>16</issue><spage>27</spage><epage>28</epage><pages>27-28</pages><issn>1001-0599</issn><abstract>电力系统负荷预测的重要性、分类和主要预测方法,BP神经网络算法的基本理论和预测过程,建立基于BP神经网络的短期负荷预测模型,以加州24 h的电力负荷预测为例进行MATLAB仿真,结果显示预测精度符合电力系统要求。</abstract><pub>中国机械工程学会</pub><doi>10.16621/j.cnki.issn1001-0599.2017.11.12</doi><tpages>2</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1001-0599 |
| ispartof | 设备管理与维修, 2017-12 (16), p.27-28 |
| issn | 1001-0599 |
| language | chi |
| recordid | cdi_wanfang_journals_sbglywx201716015 |
| source | 国家哲学社会科学学术期刊数据库 (National Social Sciences Database) |
| title | 基于BP神经网络的短期负荷预测 |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-03T07%3A40%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-cass_wanfa&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8EBP%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E7%9A%84%E7%9F%AD%E6%9C%9F%E8%B4%9F%E8%8D%B7%E9%A2%84%E6%B5%8B&rft.jtitle=%E8%AE%BE%E5%A4%87%E7%AE%A1%E7%90%86%E4%B8%8E%E7%BB%B4%E4%BF%AE&rft.au=%E9%99%86%E4%B8%87%E9%9B%A8&rft.date=2017-12-01&rft.issue=16&rft.spage=27&rft.epage=28&rft.pages=27-28&rft.issn=1001-0599&rft_id=info:doi/10.16621/j.cnki.issn1001-0599.2017.11.12&rft_dat=%3Ccass_wanfa%3E673590088%3C/cass_wanfa%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rft_cass_id=673590088&rft_wanfj_id=sbglywx201716015&rfr_iscdi=true |