Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network

IF 4.6 Q1 OPTICS Journal of Physics-Photonics Pub Date : 2023-11-01 DOI:10.1088/1742-6596/2636/1/012040
Jue Li, Ting Xia, Ruofan Wang, Haijun Chen, Xiran Xu
{"title":"Performance Prediction of a Dual-axis Tracking Solar Trough Collector Based on Artificial Neural Network","authors":"Jue Li, Ting Xia, Ruofan Wang, Haijun Chen, Xiran Xu","doi":"10.1088/1742-6596/2636/1/012040","DOIUrl":null,"url":null,"abstract":"Abstract A dual-axis tracking parabolic trough solar collector, using a certain straight-trough tube, was set up to experimentally investigate the heat collecting performance. An artificial neural network(ANN) model was developed. Experimental data were used to train and predict the mean temperature of Heat transfer fluid in the solar trough collector based on the developed model. The Levenberg-Marquardt (LM) method was also applied to optimize the weights and thresholds for the classic BP Newton algorithm, providing an ANN model with 9 hidden nodes and 30,000 training times. The predicted values are all in good agreement with the experimental data, with a mean relative error of 0.21% and a maximum error of 1.2%. In comparison, the mean relative error of the one-dimensional steady-state model reaches 1.07%. It indicates that the ANN exhibits excellent performance in predicting the export temperature of the solar collector with a specific flow rate of Heat transfer fluid. This ANN model is promising to predict the performance of solar trough collectors under different operating and environmental conditions.","PeriodicalId":44008,"journal":{"name":"Journal of Physics-Photonics","volume":"30 10","pages":"0"},"PeriodicalIF":4.6000,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Physics-Photonics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/1742-6596/2636/1/012040","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract A dual-axis tracking parabolic trough solar collector, using a certain straight-trough tube, was set up to experimentally investigate the heat collecting performance. An artificial neural network(ANN) model was developed. Experimental data were used to train and predict the mean temperature of Heat transfer fluid in the solar trough collector based on the developed model. The Levenberg-Marquardt (LM) method was also applied to optimize the weights and thresholds for the classic BP Newton algorithm, providing an ANN model with 9 hidden nodes and 30,000 training times. The predicted values are all in good agreement with the experimental data, with a mean relative error of 0.21% and a maximum error of 1.2%. In comparison, the mean relative error of the one-dimensional steady-state model reaches 1.07%. It indicates that the ANN exhibits excellent performance in predicting the export temperature of the solar collector with a specific flow rate of Heat transfer fluid. This ANN model is promising to predict the performance of solar trough collectors under different operating and environmental conditions.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于人工神经网络的双轴跟踪太阳能槽式集热器性能预测
摘要建立了一种采用直槽管的双轴跟踪抛物面槽太阳能集热器,对其集热性能进行了实验研究。建立了人工神经网络(ANN)模型。利用实验数据对槽式集热器内传热流体的平均温度进行了训练和预测。采用Levenberg-Marquardt (LM)方法对经典BP Newton算法的权值和阈值进行优化,得到了一个包含9个隐藏节点和3万次训练次数的ANN模型。预测值与实验数据吻合较好,平均相对误差为0.21%,最大误差为1.2%。相比之下,一维稳态模型的平均相对误差达到1.07%。结果表明,人工神经网络在预测特定传热流体流速下太阳能集热器出口温度方面表现出优异的性能。该人工神经网络模型有望预测太阳能槽式集热器在不同运行和环境条件下的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
期刊最新文献
Wavefront shaping simulations with augmented partial factorization An efficient compact blazed grating antenna for optical phased arrays Highly reflective and high-Q thin resonant subwavelength gratings A practical guide to digital micro-mirror devices (DMDs) for wavefront shaping A modular GUI-based program for genetic algorithm-based feedback-assisted wavefront shaping
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1