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Prediction of electricity consumption based on genetic algorithm - RBF neural network
In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in t...
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| container_end_page | 342 |
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| creator | Zeng Qing-wei Xu Zhi-Hai Wu Jian |
| description | In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in the study, and genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network. The electricity consumption data and relevant features data of a certain province from September to December in 2007 are used as the experimental data. The experiment results indicate that GA-RBFNN is very suitable for electricity consumption prediction by relevant features data. |
| doi_str_mv | 10.1109/ICACC.2010.5487062 |
| format | conference_proceeding |
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In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in the study, and genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network. The electricity consumption data and relevant features data of a certain province from September to December in 2007 are used as the experimental data. The experiment results indicate that GA-RBFNN is very suitable for electricity consumption prediction by relevant features data.</description><identifier>ISBN: 1424458455</identifier><identifier>ISBN: 9781424458455</identifier><identifier>EISBN: 9781424458486</identifier><identifier>EISBN: 1424458471</identifier><identifier>EISBN: 9781424458479</identifier><identifier>EISBN: 142445848X</identifier><identifier>DOI: 10.1109/ICACC.2010.5487062</identifier><language>eng</language><publisher>IEEE</publisher><subject>Biological cells ; Economic forecasting ; electricity consumption ; Electronic mail ; Energy consumption ; Feedforward neural networks ; genetic algorithm ; Genetic algorithms ; Neural networks ; prediction ; Predictive models ; RBF neural network ; Temperature ; Weather forecasting</subject><ispartof>2010 2nd International Conference on Advanced Computer Control, 2010, Vol.5, p.339-342</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5487062$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5487062$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Zeng Qing-wei</creatorcontrib><creatorcontrib>Xu Zhi-Hai</creatorcontrib><creatorcontrib>Wu Jian</creatorcontrib><title>Prediction of electricity consumption based on genetic algorithm - RBF neural network</title><title>2010 2nd International Conference on Advanced Computer Control</title><addtitle>ICACC</addtitle><description>In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. 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The experiment results indicate that GA-RBFNN is very suitable for electricity consumption prediction by relevant features data.</description><subject>Biological cells</subject><subject>Economic forecasting</subject><subject>electricity consumption</subject><subject>Electronic mail</subject><subject>Energy consumption</subject><subject>Feedforward neural networks</subject><subject>genetic algorithm</subject><subject>Genetic algorithms</subject><subject>Neural networks</subject><subject>prediction</subject><subject>Predictive models</subject><subject>RBF neural network</subject><subject>Temperature</subject><subject>Weather forecasting</subject><isbn>1424458455</isbn><isbn>9781424458455</isbn><isbn>9781424458486</isbn><isbn>1424458471</isbn><isbn>9781424458479</isbn><isbn>142445848X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNo1kN1KAzEUhCMiqHVfQG_yAluTbP72UherQsFS7HVJsmdrdH9KkiJ9e4PWczNnGBg-BqFbSuaUkvr-tXlomjkj2QuuFZHsDBW10pQzzoXmWp6j638jxCUqYvwk-bhgUtMrtFkFaL1Lfhrx1GHowaXgnU9H7KYxHob9b2RNhBbnZwcjJO-w6XdT8OljwCVePy7wCIdg-izpewpfN-iiM32E4qQztFk8vTcv5fLtOSMvS0-VSCV3zGoLosqAnSWOCE6JNWCsIVpK3jqmKmVAK5WJW6sVEOpchq_aWtKumqG7v14PANt98IMJx-1pieoHj-hSMA</recordid><startdate>201003</startdate><enddate>201003</enddate><creator>Zeng Qing-wei</creator><creator>Xu Zhi-Hai</creator><creator>Wu Jian</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201003</creationdate><title>Prediction of electricity consumption based on genetic algorithm - RBF neural network</title><author>Zeng Qing-wei ; Xu Zhi-Hai ; Wu Jian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-4c2b8be53424fb0c05410baeaba08664dc2737ae877004db87e01cc0453d961f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Biological cells</topic><topic>Economic forecasting</topic><topic>electricity consumption</topic><topic>Electronic mail</topic><topic>Energy consumption</topic><topic>Feedforward neural networks</topic><topic>genetic algorithm</topic><topic>Genetic algorithms</topic><topic>Neural networks</topic><topic>prediction</topic><topic>Predictive models</topic><topic>RBF neural network</topic><topic>Temperature</topic><topic>Weather forecasting</topic><toplevel>online_resources</toplevel><creatorcontrib>Zeng Qing-wei</creatorcontrib><creatorcontrib>Xu Zhi-Hai</creatorcontrib><creatorcontrib>Wu Jian</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library Online</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Zeng Qing-wei</au><au>Xu Zhi-Hai</au><au>Wu Jian</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Prediction of electricity consumption based on genetic algorithm - RBF neural network</atitle><btitle>2010 2nd International Conference on Advanced Computer Control</btitle><stitle>ICACC</stitle><date>2010-03</date><risdate>2010</risdate><volume>5</volume><spage>339</spage><epage>342</epage><pages>339-342</pages><isbn>1424458455</isbn><isbn>9781424458455</isbn><eisbn>9781424458486</eisbn><eisbn>1424458471</eisbn><eisbn>9781424458479</eisbn><eisbn>142445848X</eisbn><abstract>In order to avoid the economic loss due to too much or too little of electricity consumption, electricity consumption needs to be predicted. In order to solve the drawbacks of BP neural network, genetic algorithm and RBF neural network (GA-RBFNN) is presented to forecast electricity consumption in the study, and genetic algorithm is introduced and tried in optimizing the parameters of RBF neural network. The electricity consumption data and relevant features data of a certain province from September to December in 2007 are used as the experimental data. The experiment results indicate that GA-RBFNN is very suitable for electricity consumption prediction by relevant features data.</abstract><pub>IEEE</pub><doi>10.1109/ICACC.2010.5487062</doi><tpages>4</tpages></addata></record> |
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| identifier | ISBN: 1424458455 |
| ispartof | 2010 2nd International Conference on Advanced Computer Control, 2010, Vol.5, p.339-342 |
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| language | eng |
| recordid | cdi_ieee_primary_5487062 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological cells Economic forecasting electricity consumption Electronic mail Energy consumption Feedforward neural networks genetic algorithm Genetic algorithms Neural networks prediction Predictive models RBF neural network Temperature Weather forecasting |
| title | Prediction of electricity consumption based on genetic algorithm - RBF neural network |
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