平板太阳能集热器中阴影对能量增益降低的影响人工智能的应用

IF 8.2 Q1 AGRICULTURE, MULTIDISCIPLINARY
Morteza Taki, Rouhollah Farhadi
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引用次数: 2

摘要

由于太阳能集热器吸收板上的阴影造成的能量损失会降低太阳能的增益。在一些研究中,采用数学模型计算了平板太阳能集热器中由于阴影而导致的能量增益减少。在本研究中,建立了神经网络方法来模拟能量增益降低。利用K-fold交叉验证方法,采用两种训练算法(LM和BR)和正切s型激活函数(TanSig)建立了具有1个隐藏层和5-30个神经元的多层感知器(MLP)。在第一部分中,使用了六套太阳能集热器尺寸(1×1;1×1.5;1×2;1.5×1.5;1.5×2和2×2)。在第二部分中,使用所有维度范围作为输入。第一部分的结果表明,基于BR训练算法的MLP能够以最小的MAPE和RMSE预测所有类别的能量增益减少。最佳结果与(1.5×1.5)尺寸相关,MAPE为0.15±0.09%,RMASE为4.42±2.43 khm−2 year−1。第二部分的结果表明,BR是一种比LM更好的训练算法。最佳拓扑(5-27-1)的MAPE和R2因子分别为0.0610±0.0051%和0.9999±0.0001。灵敏度分析结果表明,在平板太阳能集热器中,由于阴影的存在,高度对总能量增益降低的影响最大。最后,本研究结果表明,通过使用人工神经网络并减少能量损失,可以提高太阳能集热器在工业和农业等所有应用中的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the energy gain reduction due to shadow in flat-plate solar collectors; Application of artificial intelligence

Energy lost due to shadow in the absorber plate of solar collectors can decrease the solar energy gain. In some studies, mathematical modeling was applied for calculating the energy gain reduction due to shadow in flat-plate solar collectors. In this study, ANN method was developed for modeling the energy gain reduction. Multilayer Perceptron (MLP) with one hidden layer and a range of neurons (5–30) by two training algorithms (LM and BR) and tangent sigmoid activation function (TanSig) were used by help of K-fold cross validation method. In the first section, six set of solar collector dimensions were used (1×1; 1×1.5; 1×2; 1.5×1.5; 1.5×2 and 2×2). In the second section all the range of dimensions were used as the inputs. The results of the first section showed that MLP with BR training algorithm can predict the energy gain reduction with minimum MAPE and RMSE in all the categories. The best results related to (1.5×1.5) dimension that achieved a MAPE of 0.15 ± 0.09% and RMASE of 4.42 ± 2.43 KJm−2 year−1, respectively. The results of the second section indicated that BR is a better training algorithm than LM. The MAPE and R2 factors for the best topology (5-27-1) were 0.0610 ± 0.0051% and 0.9999 ± 0.0001, respectively. The results of the sensitivity analysis showed that height has the biggest impact on total energy gain reduction due to shadow in flat-plate solar collectors. Finally, the results of this study indicated that by using ANN and decrease the energy lost, the efficiency of solar collectors can be increased in all applications such as industry and agriculture.

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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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