Daily solar radiation prediction based on Genetic Algorithm Optimization of wavelet neural network
Daily solar radiation prediction is a nonlinear and non-stationary process. It's hard to model with a single method. A Genetic Algorithm Optimization of Wavelet Neural Network (GAO-WNN) model was set in this paper. The nonlinear process of daily solar radiation was forecasted by neural network...
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| creator | Jianping Wang Yunlin Xie Chenghui Zhu Xiaobing Xu |
| description | Daily solar radiation prediction is a nonlinear and non-stationary process. It's hard to model with a single method. A Genetic Algorithm Optimization of Wavelet Neural Network (GAO-WNN) model was set in this paper. The nonlinear process of daily solar radiation was forecasted by neural network and the non-stationary process of daily solar radiation was decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transform. Input weights, output weights, scale factors and translation factors were optimized by genetic algorithm. Gradient descent method was used to make further training of the model with temperature, clearness index, and daily radiation data. Simulation results indicate that the method is satisfactory to the prediction of daily solar radiation. |
| doi_str_mv | 10.1109/ICECENG.2011.6057583 |
| format | Conference Proceeding |
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It's hard to model with a single method. A Genetic Algorithm Optimization of Wavelet Neural Network (GAO-WNN) model was set in this paper. The nonlinear process of daily solar radiation was forecasted by neural network and the non-stationary process of daily solar radiation was decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transform. Input weights, output weights, scale factors and translation factors were optimized by genetic algorithm. Gradient descent method was used to make further training of the model with temperature, clearness index, and daily radiation data. 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It's hard to model with a single method. A Genetic Algorithm Optimization of Wavelet Neural Network (GAO-WNN) model was set in this paper. The nonlinear process of daily solar radiation was forecasted by neural network and the non-stationary process of daily solar radiation was decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transform. Input weights, output weights, scale factors and translation factors were optimized by genetic algorithm. Gradient descent method was used to make further training of the model with temperature, clearness index, and daily radiation data. Simulation results indicate that the method is satisfactory to the prediction of daily solar radiation.</description><subject>Biological neural networks</subject><subject>daily solar radiation prediction</subject><subject>Data models</subject><subject>genetic algorithm optimization</subject><subject>Genetic algorithms</subject><subject>Neurons</subject><subject>Optimization</subject><subject>Predictive models</subject><subject>Solar radiation</subject><subject>wavelet neural network</subject><isbn>9781424481620</isbn><isbn>1424481627</isbn><isbn>1424481643</isbn><isbn>9781424481644</isbn><isbn>1424481651</isbn><isbn>9781424481651</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2011</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1UM1OwzAYC0JIwOgTwCEv0JI0P02PUylj0sQuu09fm68Q6J_SwDSenooNX2xLtg8m5IGzhHOWP66LsihfV0nKOE80U5ky4oLccplKabiW4pJEeWb-fcquSTRNH2yG1jk34oZUT-DaI52GFjz1YB0EN_R09Ghd_ScrmNDSWaywx-BqumzfBu_Ce0e3Y3Cd-zlVhoYe4BtbDLTHLw_tTOEw-M87ctVAO2F05gXZPZe74iXebFfrYrmJXc5CnFYGbCWlrMDWaFEYFGBAaWahkTlnWcZApXPMaN1UHESu0CquuTLaWiYW5P406xBxP3rXgT_uz6-IX2cZWHk</recordid><startdate>201109</startdate><enddate>201109</enddate><creator>Jianping Wang</creator><creator>Yunlin Xie</creator><creator>Chenghui Zhu</creator><creator>Xiaobing Xu</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201109</creationdate><title>Daily solar radiation prediction based on Genetic Algorithm Optimization of wavelet neural network</title><author>Jianping Wang ; Yunlin Xie ; Chenghui Zhu ; Xiaobing Xu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-2b8adb444badcede38e3a8a560daf4910770a522b8866fb1a395ed5161586dd03</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Biological neural networks</topic><topic>daily solar radiation prediction</topic><topic>Data models</topic><topic>genetic algorithm optimization</topic><topic>Genetic algorithms</topic><topic>Neurons</topic><topic>Optimization</topic><topic>Predictive models</topic><topic>Solar radiation</topic><topic>wavelet neural network</topic><toplevel>online_resources</toplevel><creatorcontrib>Jianping Wang</creatorcontrib><creatorcontrib>Yunlin Xie</creatorcontrib><creatorcontrib>Chenghui Zhu</creatorcontrib><creatorcontrib>Xiaobing Xu</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 (IEL)</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>Jianping Wang</au><au>Yunlin Xie</au><au>Chenghui Zhu</au><au>Xiaobing Xu</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Daily solar radiation prediction based on Genetic Algorithm Optimization of wavelet neural network</atitle><btitle>2011 International Conference on Electrical and Control Engineering</btitle><stitle>ICECENG</stitle><date>2011-09</date><risdate>2011</risdate><spage>602</spage><epage>605</epage><pages>602-605</pages><isbn>9781424481620</isbn><isbn>1424481627</isbn><eisbn>1424481643</eisbn><eisbn>9781424481644</eisbn><eisbn>1424481651</eisbn><eisbn>9781424481651</eisbn><abstract>Daily solar radiation prediction is a nonlinear and non-stationary process. It's hard to model with a single method. A Genetic Algorithm Optimization of Wavelet Neural Network (GAO-WNN) model was set in this paper. The nonlinear process of daily solar radiation was forecasted by neural network and the non-stationary process of daily solar radiation was decomposed into quasi-stationary at different frequency scales by multi-scale characteristics of wavelet transform. Input weights, output weights, scale factors and translation factors were optimized by genetic algorithm. Gradient descent method was used to make further training of the model with temperature, clearness index, and daily radiation data. Simulation results indicate that the method is satisfactory to the prediction of daily solar radiation.</abstract><pub>IEEE</pub><doi>10.1109/ICECENG.2011.6057583</doi><tpages>4</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Biological neural networks daily solar radiation prediction Data models genetic algorithm optimization Genetic algorithms Neurons Optimization Predictive models Solar radiation wavelet neural network |
| title | Daily solar radiation prediction based on Genetic Algorithm Optimization of wavelet neural network |
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