Loading…

Reliability Evaluation of GSR Prediction Using Neural Networks with Variant Atmospheric Parameters

Global Solar Radiation (GSR) has fluctuations in its measured values. This occurs by actions of several factors including clouds, dust, reflections, and others. The ambiguity associated with its prospective values forms a challenge for many engineering applications and manufacturers of solar-based s...

Full description

Saved in:
Bibliographic Details
Main Authors: Al-Omary, Murad, Albatayneh, Aiman, Aljarrah, Rafat, Alzaareer, Khaled
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 1161
container_issue
container_start_page 1156
container_title
container_volume
creator Al-Omary, Murad
Albatayneh, Aiman
Aljarrah, Rafat
Alzaareer, Khaled
description Global Solar Radiation (GSR) has fluctuations in its measured values. This occurs by actions of several factors including clouds, dust, reflections, and others. The ambiguity associated with its prospective values forms a challenge for many engineering applications and manufacturers of solar-based systems. The intermittent nature of global solar radiation conflicts with the necessity to find correct and reliable values in advance. The neural network-based prediction has been adopted to fulfill a prior knowledge about these values for being highly efficient compared to the stochastic and statistic approaches. Despite that, the reliability of those networks is considered variant for being largely dependent on different inputs. This work evaluates the reliability of different neural networks that specifically use atmospheric parameters, considering them as single inputs and combinations of two and three parameters. The results appeared that the network that uses (Zenith Angle, Air Temperature, and Relative Humidity) is the most reliable one with 0.997 recorded for the correlation coefficient. Oppositely, the network of only (Air Temperature) is the network of the lowest reliability according to the 0.603 that is found for the correlation coefficient.
doi_str_mv 10.1109/SSD54932.2022.9955790
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9955790</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9955790</ieee_id><sourcerecordid>9955790</sourcerecordid><originalsourceid>FETCH-LOGICAL-i118t-d846c89b6723ebbd1053be59bc436a20747ac063270a28e563762078c948dfde3</originalsourceid><addsrcrecordid>eNotkM1OAjEYAKuJiYg8gTHpCyz2_-dIENGEKGHFK2l3v5XqwpK2SHh7iXKaZA5zGITuKRlSSuxDWT5KYTkbMsLY0FoptSUX6IYqJYWmxMhL1GNCi4IIoa7RIKUvQghVVFohesgvoA3OhzbkI578uHbvcui2uGvwtFzgeYQ6VH9mmcL2E7_CPrr2hHzo4nfCh5DX-MPF4LYZj_KmS7s1xFDhuYtuAxliukVXjWsTDM7so-XT5H38XMzepi_j0awIlJpc1EaoylivNOPgfU2J5B6k9ZXgyjGihXYVUZxp4pgBqbhWJ2sqK0zd1MD76O6_GwBgtYth4-JxdV7CfwEngVaV</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Reliability Evaluation of GSR Prediction Using Neural Networks with Variant Atmospheric Parameters</title><source>IEEE Xplore All Conference Series</source><creator>Al-Omary, Murad ; Albatayneh, Aiman ; Aljarrah, Rafat ; Alzaareer, Khaled</creator><creatorcontrib>Al-Omary, Murad ; Albatayneh, Aiman ; Aljarrah, Rafat ; Alzaareer, Khaled</creatorcontrib><description>Global Solar Radiation (GSR) has fluctuations in its measured values. This occurs by actions of several factors including clouds, dust, reflections, and others. The ambiguity associated with its prospective values forms a challenge for many engineering applications and manufacturers of solar-based systems. The intermittent nature of global solar radiation conflicts with the necessity to find correct and reliable values in advance. The neural network-based prediction has been adopted to fulfill a prior knowledge about these values for being highly efficient compared to the stochastic and statistic approaches. Despite that, the reliability of those networks is considered variant for being largely dependent on different inputs. This work evaluates the reliability of different neural networks that specifically use atmospheric parameters, considering them as single inputs and combinations of two and three parameters. The results appeared that the network that uses (Zenith Angle, Air Temperature, and Relative Humidity) is the most reliable one with 0.997 recorded for the correlation coefficient. Oppositely, the network of only (Air Temperature) is the network of the lowest reliability according to the 0.603 that is found for the correlation coefficient.</description><identifier>EISSN: 2474-0446</identifier><identifier>EISBN: 1665471085</identifier><identifier>EISBN: 9781665471084</identifier><identifier>DOI: 10.1109/SSD54932.2022.9955790</identifier><language>eng</language><publisher>IEEE</publisher><subject>Atmospheric parameters ; Clouds ; Correlation coefficient ; Energy Prediction ; Fluctuations ; Global Solar Radiation ; Humidity ; Knowledge engineering ; Neural networks ; Reliability ; Solar energy</subject><ispartof>2022 19th International Multi-Conference on Systems, Signals &amp; Devices (SSD), 2022, p.1156-1161</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/9955790$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9955790$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Al-Omary, Murad</creatorcontrib><creatorcontrib>Albatayneh, Aiman</creatorcontrib><creatorcontrib>Aljarrah, Rafat</creatorcontrib><creatorcontrib>Alzaareer, Khaled</creatorcontrib><title>Reliability Evaluation of GSR Prediction Using Neural Networks with Variant Atmospheric Parameters</title><title>2022 19th International Multi-Conference on Systems, Signals &amp; Devices (SSD)</title><addtitle>SSD</addtitle><description>Global Solar Radiation (GSR) has fluctuations in its measured values. This occurs by actions of several factors including clouds, dust, reflections, and others. The ambiguity associated with its prospective values forms a challenge for many engineering applications and manufacturers of solar-based systems. The intermittent nature of global solar radiation conflicts with the necessity to find correct and reliable values in advance. The neural network-based prediction has been adopted to fulfill a prior knowledge about these values for being highly efficient compared to the stochastic and statistic approaches. Despite that, the reliability of those networks is considered variant for being largely dependent on different inputs. This work evaluates the reliability of different neural networks that specifically use atmospheric parameters, considering them as single inputs and combinations of two and three parameters. The results appeared that the network that uses (Zenith Angle, Air Temperature, and Relative Humidity) is the most reliable one with 0.997 recorded for the correlation coefficient. Oppositely, the network of only (Air Temperature) is the network of the lowest reliability according to the 0.603 that is found for the correlation coefficient.</description><subject>Atmospheric parameters</subject><subject>Clouds</subject><subject>Correlation coefficient</subject><subject>Energy Prediction</subject><subject>Fluctuations</subject><subject>Global Solar Radiation</subject><subject>Humidity</subject><subject>Knowledge engineering</subject><subject>Neural networks</subject><subject>Reliability</subject><subject>Solar energy</subject><issn>2474-0446</issn><isbn>1665471085</isbn><isbn>9781665471084</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkM1OAjEYAKuJiYg8gTHpCyz2_-dIENGEKGHFK2l3v5XqwpK2SHh7iXKaZA5zGITuKRlSSuxDWT5KYTkbMsLY0FoptSUX6IYqJYWmxMhL1GNCi4IIoa7RIKUvQghVVFohesgvoA3OhzbkI578uHbvcui2uGvwtFzgeYQ6VH9mmcL2E7_CPrr2hHzo4nfCh5DX-MPF4LYZj_KmS7s1xFDhuYtuAxliukVXjWsTDM7so-XT5H38XMzepi_j0awIlJpc1EaoylivNOPgfU2J5B6k9ZXgyjGihXYVUZxp4pgBqbhWJ2sqK0zd1MD76O6_GwBgtYth4-JxdV7CfwEngVaV</recordid><startdate>20220506</startdate><enddate>20220506</enddate><creator>Al-Omary, Murad</creator><creator>Albatayneh, Aiman</creator><creator>Aljarrah, Rafat</creator><creator>Alzaareer, Khaled</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20220506</creationdate><title>Reliability Evaluation of GSR Prediction Using Neural Networks with Variant Atmospheric Parameters</title><author>Al-Omary, Murad ; Albatayneh, Aiman ; Aljarrah, Rafat ; Alzaareer, Khaled</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i118t-d846c89b6723ebbd1053be59bc436a20747ac063270a28e563762078c948dfde3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Atmospheric parameters</topic><topic>Clouds</topic><topic>Correlation coefficient</topic><topic>Energy Prediction</topic><topic>Fluctuations</topic><topic>Global Solar Radiation</topic><topic>Humidity</topic><topic>Knowledge engineering</topic><topic>Neural networks</topic><topic>Reliability</topic><topic>Solar energy</topic><toplevel>online_resources</toplevel><creatorcontrib>Al-Omary, Murad</creatorcontrib><creatorcontrib>Albatayneh, Aiman</creatorcontrib><creatorcontrib>Aljarrah, Rafat</creatorcontrib><creatorcontrib>Alzaareer, Khaled</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/IET Electronic Library</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>Al-Omary, Murad</au><au>Albatayneh, Aiman</au><au>Aljarrah, Rafat</au><au>Alzaareer, Khaled</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Reliability Evaluation of GSR Prediction Using Neural Networks with Variant Atmospheric Parameters</atitle><btitle>2022 19th International Multi-Conference on Systems, Signals &amp; Devices (SSD)</btitle><stitle>SSD</stitle><date>2022-05-06</date><risdate>2022</risdate><spage>1156</spage><epage>1161</epage><pages>1156-1161</pages><eissn>2474-0446</eissn><eisbn>1665471085</eisbn><eisbn>9781665471084</eisbn><abstract>Global Solar Radiation (GSR) has fluctuations in its measured values. This occurs by actions of several factors including clouds, dust, reflections, and others. The ambiguity associated with its prospective values forms a challenge for many engineering applications and manufacturers of solar-based systems. The intermittent nature of global solar radiation conflicts with the necessity to find correct and reliable values in advance. The neural network-based prediction has been adopted to fulfill a prior knowledge about these values for being highly efficient compared to the stochastic and statistic approaches. Despite that, the reliability of those networks is considered variant for being largely dependent on different inputs. This work evaluates the reliability of different neural networks that specifically use atmospheric parameters, considering them as single inputs and combinations of two and three parameters. The results appeared that the network that uses (Zenith Angle, Air Temperature, and Relative Humidity) is the most reliable one with 0.997 recorded for the correlation coefficient. Oppositely, the network of only (Air Temperature) is the network of the lowest reliability according to the 0.603 that is found for the correlation coefficient.</abstract><pub>IEEE</pub><doi>10.1109/SSD54932.2022.9955790</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2474-0446
ispartof 2022 19th International Multi-Conference on Systems, Signals & Devices (SSD), 2022, p.1156-1161
issn 2474-0446
language eng
recordid cdi_ieee_primary_9955790
source IEEE Xplore All Conference Series
subjects Atmospheric parameters
Clouds
Correlation coefficient
Energy Prediction
Fluctuations
Global Solar Radiation
Humidity
Knowledge engineering
Neural networks
Reliability
Solar energy
title Reliability Evaluation of GSR Prediction Using Neural Networks with Variant Atmospheric Parameters
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T17%3A25%3A32IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Reliability%20Evaluation%20of%20GSR%20Prediction%20Using%20Neural%20Networks%20with%20Variant%20Atmospheric%20Parameters&rft.btitle=2022%2019th%20International%20Multi-Conference%20on%20Systems,%20Signals%20&%20Devices%20(SSD)&rft.au=Al-Omary,%20Murad&rft.date=2022-05-06&rft.spage=1156&rft.epage=1161&rft.pages=1156-1161&rft.eissn=2474-0446&rft_id=info:doi/10.1109/SSD54932.2022.9955790&rft.eisbn=1665471085&rft.eisbn_list=9781665471084&rft_dat=%3Cieee_CHZPO%3E9955790%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i118t-d846c89b6723ebbd1053be59bc436a20747ac063270a28e563762078c948dfde3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9955790&rfr_iscdi=true