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
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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.
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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。
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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