Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression

Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, acco...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Dian li yu neng yuan 2012-09, Vol.4 (5), p.380-385
Hauptverfasser:	Ye, Shijie, Zhu, Guangfu, Xiao, Zhi
Format:	Artikel
Sprache:	eng
Schlagworte:	China Domestic Economic factors Forecasting Mathematical analysis Regression Tasks Vectors (mathematics)
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page	385
container_issue	5
container_start_page	380
container_title	Dian li yu neng yuan
container_volume	4
creator	Ye, Shijie Zhu, Guangfu Xiao, Zhi
description	Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China's 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algo-rithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.
doi_str_mv	10.4236/epe.2012.45050
format	Article
fullrecord	<record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1266713652</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>1266713652</sourcerecordid><originalsourceid>FETCH-LOGICAL-c1570-6a126a4ebe030881d7af8e6e146c5854508211724aa1e4fb289b08b1fe435e293</originalsourceid><addsrcrecordid>eNotkE1Lw0AQhhdRsNRePe_RS-J-Z3PUYlUICLWK4GHZJJMaaXbjbnrw37ttncsMMw8vzIPQNSW5YFzdwgg5I5TlQhJJztCMlqLIuKb6_DiXGRP84xItYvwmqYSSSpUz9Fl5t8UbCAOuvG3xygdobJz6tLWuxWto_DCAa-3Uexdx5wNefvXO4nsbocXe4df9OPow4XdopnRdwzZAjIm-Qhed3UVY_Pc5els9bJZPWfXy-Ly8q7KGyoJkylKmrIAaCCda07awnQYFVKhGapke0ozSgglrKYiuZrqsia5pB4JLYCWfo5tT7hj8zx7iZIY-NrDbWQd-H02KVwXlSrKE5ie0CT7GAJ0ZQz_Y8GsoMQeRJok0B5HmKJL_Acv2ZX4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1266713652</pqid></control><display><type>article</type><title>Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression</title><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>Ye, Shijie ; Zhu, Guangfu ; Xiao, Zhi</creator><creatorcontrib>Ye, Shijie ; Zhu, Guangfu ; Xiao, Zhi</creatorcontrib><description>Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China's 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algo-rithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.</description><identifier>ISSN: 1949-243X</identifier><identifier>ISSN: 1947-3818</identifier><identifier>EISSN: 1947-3818</identifier><identifier>DOI: 10.4236/epe.2012.45050</identifier><language>eng</language><subject>China ; Domestic ; Economic factors ; Forecasting ; Mathematical analysis ; Regression ; Tasks ; Vectors (mathematics)</subject><ispartof>Dian li yu neng yuan, 2012-09, Vol.4 (5), p.380-385</ispartof><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c1570-6a126a4ebe030881d7af8e6e146c5854508211724aa1e4fb289b08b1fe435e293</citedby><cites>FETCH-LOGICAL-c1570-6a126a4ebe030881d7af8e6e146c5854508211724aa1e4fb289b08b1fe435e293</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27915,27916</link.rule.ids></links><search><creatorcontrib>Ye, Shijie</creatorcontrib><creatorcontrib>Zhu, Guangfu</creatorcontrib><creatorcontrib>Xiao, Zhi</creatorcontrib><title>Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression</title><title>Dian li yu neng yuan</title><description>Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China's 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algo-rithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.</description><subject>China</subject><subject>Domestic</subject><subject>Economic factors</subject><subject>Forecasting</subject><subject>Mathematical analysis</subject><subject>Regression</subject><subject>Tasks</subject><subject>Vectors (mathematics)</subject><issn>1949-243X</issn><issn>1947-3818</issn><issn>1947-3818</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNotkE1Lw0AQhhdRsNRePe_RS-J-Z3PUYlUICLWK4GHZJJMaaXbjbnrw37ttncsMMw8vzIPQNSW5YFzdwgg5I5TlQhJJztCMlqLIuKb6_DiXGRP84xItYvwmqYSSSpUz9Fl5t8UbCAOuvG3xygdobJz6tLWuxWto_DCAa-3Uexdx5wNefvXO4nsbocXe4df9OPow4XdopnRdwzZAjIm-Qhed3UVY_Pc5els9bJZPWfXy-Ly8q7KGyoJkylKmrIAaCCda07awnQYFVKhGapke0ozSgglrKYiuZrqsia5pB4JLYCWfo5tT7hj8zx7iZIY-NrDbWQd-H02KVwXlSrKE5ie0CT7GAJ0ZQz_Y8GsoMQeRJok0B5HmKJL_Acv2ZX4</recordid><startdate>20120901</startdate><enddate>20120901</enddate><creator>Ye, Shijie</creator><creator>Zhu, Guangfu</creator><creator>Xiao, Zhi</creator><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>20120901</creationdate><title>Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression</title><author>Ye, Shijie ; Zhu, Guangfu ; Xiao, Zhi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1570-6a126a4ebe030881d7af8e6e146c5854508211724aa1e4fb289b08b1fe435e293</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>China</topic><topic>Domestic</topic><topic>Economic factors</topic><topic>Forecasting</topic><topic>Mathematical analysis</topic><topic>Regression</topic><topic>Tasks</topic><topic>Vectors (mathematics)</topic><toplevel>online_resources</toplevel><creatorcontrib>Ye, Shijie</creatorcontrib><creatorcontrib>Zhu, Guangfu</creatorcontrib><creatorcontrib>Xiao, Zhi</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Dian li yu neng yuan</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ye, Shijie</au><au>Zhu, Guangfu</au><au>Xiao, Zhi</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression</atitle><jtitle>Dian li yu neng yuan</jtitle><date>2012-09-01</date><risdate>2012</risdate><volume>4</volume><issue>5</issue><spage>380</spage><epage>385</epage><pages>380-385</pages><issn>1949-243X</issn><issn>1947-3818</issn><eissn>1947-3818</eissn><abstract>Long-term load forecasting (LTLF) is a challenging task because of the complex relationships between load and factors affecting load. However, it is crucial for the economic growth of fast developing countries like China as the growth rate of gross domestic product (GDP) is expected to be 7.5%, according to China's 11th Five-Year Plan (2006-2010). In this paper, LTLF with an economic factor, GDP, is implemented. A support vector regression (SVR) is applied as the training algo-rithm to obtain the nonlinear relationship between load and the economic factor GDP to improve the accuracy of forecasting.</abstract><doi>10.4236/epe.2012.45050</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record>
fulltext	fulltext
identifier	ISSN: 1949-243X
ispartof	Dian li yu neng yuan, 2012-09, Vol.4 (5), p.380-385
issn	1949-243X 1947-3818 1947-3818
language	eng
recordid	cdi_proquest_miscellaneous_1266713652
source	Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals
subjects	China Domestic Economic factors Forecasting Mathematical analysis Regression Tasks Vectors (mathematics)
title	Long Term Load Forecasting and Recommendations for China Based on Support Vector Regression
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T07%3A22%3A21IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Long%20Term%20Load%20Forecasting%20and%20Recommendations%20for%20China%20Based%20on%20Support%20Vector%20Regression&rft.jtitle=Dian%20li%20yu%20neng%20yuan&rft.au=Ye,%20Shijie&rft.date=2012-09-01&rft.volume=4&rft.issue=5&rft.spage=380&rft.epage=385&rft.pages=380-385&rft.issn=1949-243X&rft.eissn=1947-3818&rft_id=info:doi/10.4236/epe.2012.45050&rft_dat=%3Cproquest_cross%3E1266713652%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1266713652&rft_id=info:pmid/&rfr_iscdi=true