Solar radiation prediction using Artificial Neural Network techniques: A review
Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models....
Gespeichert in:
Veröffentlicht in: | Renewable & sustainable energy reviews 2014-05, Vol.33, p.772-781 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 781 |
---|---|
container_issue | |
container_start_page | 772 |
container_title | Renewable & sustainable energy reviews |
container_volume | 33 |
creator | Yadav, Amit Kumar Chandel, S.S. |
description | Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study. |
doi_str_mv | 10.1016/j.rser.2013.08.055 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_1520363044</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S1364032113005959</els_id><sourcerecordid>1520363044</sourcerecordid><originalsourceid>FETCH-LOGICAL-c429t-8d9fe79ce5fdf52093da161d376c487bd040b81477bfaae53ff9c601e496f9cc3</originalsourceid><addsrcrecordid>eNp9kD9PwzAQxSMEEqXwBZiyILEknGMncRBLVfFPqugAzJbrnMElTco5oeLb49KKkene8N67u18UnTNIGbDiapmSR0ozYDwFmUKeH0QjJssqgaKCw6B5IRLgGTuOTrxfArBclnwUzZ-7RlNMuna6d10brwlrZ37l4F37Fk-od9YZp5v4CQf6Hf2mo4-4R_Peus8B_XU8iQm_HG5OoyOrG49n-zmOXu9uX6YPyWx-_zidzBIjsqpPZF1ZLCuDua1tnkHFa80KVvOyMEKWixoELCQTZbmwWmPOra1MAQxFVQRl-Di63PWuqdte0KuV8wabRrfYDV6xUMoLDkIEa7azGuq8J7RqTW6l6VsxUFt6aqm29NSWngKpAr0Qutj3a290Y0m3xvm_ZCa5FFzw4LvZ-TA8GwCQ8sZhawJEQtOrunP_rfkBtimGjw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1520363044</pqid></control><display><type>article</type><title>Solar radiation prediction using Artificial Neural Network techniques: A review</title><source>ScienceDirect Journals (5 years ago - present)</source><creator>Yadav, Amit Kumar ; Chandel, S.S.</creator><creatorcontrib>Yadav, Amit Kumar ; Chandel, S.S.</creatorcontrib><description>Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.</description><identifier>ISSN: 1364-0321</identifier><identifier>EISSN: 1879-0690</identifier><identifier>DOI: 10.1016/j.rser.2013.08.055</identifier><language>eng</language><publisher>Kidlington: Elsevier Ltd</publisher><subject>Applied sciences ; Artificial Neural Network ; Energy ; Exact sciences and technology ; Meteorological data ; Natural energy ; Solar energy ; Solar radiation ; Solar radiation models ; Solar radiation prediction</subject><ispartof>Renewable & sustainable energy reviews, 2014-05, Vol.33, p.772-781</ispartof><rights>2013</rights><rights>2015 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c429t-8d9fe79ce5fdf52093da161d376c487bd040b81477bfaae53ff9c601e496f9cc3</citedby><cites>FETCH-LOGICAL-c429t-8d9fe79ce5fdf52093da161d376c487bd040b81477bfaae53ff9c601e496f9cc3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.rser.2013.08.055$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=28384343$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Yadav, Amit Kumar</creatorcontrib><creatorcontrib>Chandel, S.S.</creatorcontrib><title>Solar radiation prediction using Artificial Neural Network techniques: A review</title><title>Renewable & sustainable energy reviews</title><description>Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.</description><subject>Applied sciences</subject><subject>Artificial Neural Network</subject><subject>Energy</subject><subject>Exact sciences and technology</subject><subject>Meteorological data</subject><subject>Natural energy</subject><subject>Solar energy</subject><subject>Solar radiation</subject><subject>Solar radiation models</subject><subject>Solar radiation prediction</subject><issn>1364-0321</issn><issn>1879-0690</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><recordid>eNp9kD9PwzAQxSMEEqXwBZiyILEknGMncRBLVfFPqugAzJbrnMElTco5oeLb49KKkene8N67u18UnTNIGbDiapmSR0ozYDwFmUKeH0QjJssqgaKCw6B5IRLgGTuOTrxfArBclnwUzZ-7RlNMuna6d10brwlrZ37l4F37Fk-od9YZp5v4CQf6Hf2mo4-4R_Peus8B_XU8iQm_HG5OoyOrG49n-zmOXu9uX6YPyWx-_zidzBIjsqpPZF1ZLCuDua1tnkHFa80KVvOyMEKWixoELCQTZbmwWmPOra1MAQxFVQRl-Di63PWuqdte0KuV8wabRrfYDV6xUMoLDkIEa7azGuq8J7RqTW6l6VsxUFt6aqm29NSWngKpAr0Qutj3a290Y0m3xvm_ZCa5FFzw4LvZ-TA8GwCQ8sZhawJEQtOrunP_rfkBtimGjw</recordid><startdate>20140501</startdate><enddate>20140501</enddate><creator>Yadav, Amit Kumar</creator><creator>Chandel, S.S.</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7TG</scope><scope>7U6</scope><scope>C1K</scope><scope>KL.</scope><scope>SOI</scope></search><sort><creationdate>20140501</creationdate><title>Solar radiation prediction using Artificial Neural Network techniques: A review</title><author>Yadav, Amit Kumar ; Chandel, S.S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c429t-8d9fe79ce5fdf52093da161d376c487bd040b81477bfaae53ff9c601e496f9cc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2014</creationdate><topic>Applied sciences</topic><topic>Artificial Neural Network</topic><topic>Energy</topic><topic>Exact sciences and technology</topic><topic>Meteorological data</topic><topic>Natural energy</topic><topic>Solar energy</topic><topic>Solar radiation</topic><topic>Solar radiation models</topic><topic>Solar radiation prediction</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yadav, Amit Kumar</creatorcontrib><creatorcontrib>Chandel, S.S.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Environment Abstracts</collection><jtitle>Renewable & sustainable energy reviews</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yadav, Amit Kumar</au><au>Chandel, S.S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Solar radiation prediction using Artificial Neural Network techniques: A review</atitle><jtitle>Renewable & sustainable energy reviews</jtitle><date>2014-05-01</date><risdate>2014</risdate><volume>33</volume><spage>772</spage><epage>781</epage><pages>772-781</pages><issn>1364-0321</issn><eissn>1879-0690</eissn><abstract>Solar radiation data plays an important role in solar energy research. These data are not available for location of interest due to absence of a meteorological station. Therefore, the solar radiation has to be predicted accurately for these locations using various solar radiation estimation models. The main objective of this study is to review Artificial Neural Network (ANN) based techniques in order to identify suitable methods available in the literature for solar radiation prediction and to identify research gaps. The study shows that Artificial Neural Network techniques predict solar radiation more accurately in comparison to conventional methods. The prediction accuracy of ANN models is found to be dependent on input parameter combinations, training algorithm and architecture configurations. Further research areas in ANN technique based methodologies are also identified in the present study.</abstract><cop>Kidlington</cop><pub>Elsevier Ltd</pub><doi>10.1016/j.rser.2013.08.055</doi><tpages>10</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1364-0321 |
ispartof | Renewable & sustainable energy reviews, 2014-05, Vol.33, p.772-781 |
issn | 1364-0321 1879-0690 |
language | eng |
recordid | cdi_proquest_miscellaneous_1520363044 |
source | ScienceDirect Journals (5 years ago - present) |
subjects | Applied sciences Artificial Neural Network Energy Exact sciences and technology Meteorological data Natural energy Solar energy Solar radiation Solar radiation models Solar radiation prediction |
title | Solar radiation prediction using Artificial Neural Network techniques: A review |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T22%3A35%3A42IST&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=Solar%20radiation%20prediction%20using%20Artificial%20Neural%20Network%20techniques:%20A%20review&rft.jtitle=Renewable%20&%20sustainable%20energy%20reviews&rft.au=Yadav,%20Amit%20Kumar&rft.date=2014-05-01&rft.volume=33&rft.spage=772&rft.epage=781&rft.pages=772-781&rft.issn=1364-0321&rft.eissn=1879-0690&rft_id=info:doi/10.1016/j.rser.2013.08.055&rft_dat=%3Cproquest_cross%3E1520363044%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=1520363044&rft_id=info:pmid/&rft_els_id=S1364032113005959&rfr_iscdi=true |