Artificial neural networks used for the performance prediction of a thermosiphon solar water heater
Artificial neural networks used for the performance prediction of a thermosiphon solar water heater
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DOI：
10.1016/s0960-1481(98)00787-3
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发表时间：
1999-09-01
期刊：
影响因子：
8.7
通讯作者：
Dentsoras, A
中科院分区：
文献类型：
--
作者：
Kalogirou, SA;Panteliou, S;Dentsoras, A
人工神经网络（ANN）被广泛接受为一种技术，为解决复杂和不明确的问题提供了另一种方法。它们可以从示例中学习，具有容错能力，能够处理非线性问题，并且一旦经过训练就可以高速执行预测。人工神经网络已被用于各种应用，它们已被证明是特别有效的系统建模以及系统识别。本文的目的是训练一个人工神经网络（ANN）来学习预测热虹吸管太阳能家用热水系统的性能。这种性能是根据提取的有用能量和储存的水温上升来衡量的。一个人工神经网络已被训练使用四种类型的系统，所有采用相同的收集器面板在不同的天气条件下的性能数据。通过这种方式，网络被训练接受和处理一些不寻常的案件。作为输入的数据是，储罐热损失系数（U值），系统的类型（开放或封闭），存储量，以及来自真实的每日总太阳辐射，每日总漫射辐射，环境空气温度和储罐中的水温在一天开始时的实验的共54个读数。网络输出是从系统中提取的有用能量和水温上升。对训练数据集获得的两个输出参数的多重决定统计系数（R-2值）分别等于0.9914和0.9808。这两个值都是令人满意的，因为R-2值越接近1，映射越好。所有四个系统的未知数据随后被用来调查预测的准确性。这些数据包括在不同天气条件下为训练网络而考虑的系统的性能数据。最大偏差分别为1 MJ和2.2 ℃。随机数据也被用来从实验测量获得的性能方程和人工神经网络预测上述两个参数。由此获得的预测值非常具有可比性。这些结果表明，所提出的方法调用成功地用于特定的热虹吸管系统在这里使用的不同类型的配置的盟友的性能的估计。本模型的最大优点是网络能够从示例中学习，从而逐步提高其性能。这是通过在网络中嵌入实验知识来实现的。(C)1999 Elsevier Science Ltd.保留所有权利。
Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly effective in system modelling as well as for system identification. The objective of this work is to train an artificial neural network (ANN) to learn to predict the performance of a thermosiphon solar domestic water heating system. This performance is measured in terms of the useful energy extracted and of the stored water temperature rise. An ANN has been trained using performance data for four types of systems, all employing the same collector panel under varying weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were, the storage tank heat loss coefficient (U-value), the type of system (open or closed), the storage volume, and a total of fifty-four readings from real experiments of total daily solar radiation, total daily diffuse radiation, ambient air temperature, and the water temperature in storage tank at the beginning of the day. The network output is the useful energy extracted from the system and the water temperature rise. The statistical coefficient of multiple determination (R-2-value) obtained for the training data set was equal to 0.9914 and 0.9808 for the two output parameters respectively. Both values are satisfactory because the closer R-2-value is to unity the better is the mapping. Unknown data for all four systems were subsequently used to investigate the accuracy of prediction. These include performance data for the systems considered for the training of the network at different weather conditions. Predictions with maximum deviations of 1 MJ and 2.2 degrees C were obtained respectively. Random data were also used both with the performance equations obtained from the experimental measurements and with the artificial neural network to predict the above two parameters. The predicted values thus obtained were very comparable. These results indicate that the proposed method call successfully be used for the estimation of the performance of the particular thermosiphon system at ally of the different types of configuration used here. The greatest advantage of the present model is the capacity of the network to learn from examples and thus gradually improve its performance. This is done by embedding experimental knowledge in the network. (C) 1999 Elsevier Science Ltd. All rights reserved.