Modeling of solar domestic water heating systems using Artificial Neural Networks

Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can be trained to predict results 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 useful in system modeling and for system identification. The objective of this work was to train an ANN to learn to predict the useful energy extracted and the temperature rise in the stored water of solar domestic water heating (SDHW) systems with the minimum of input data. An ANN has been trained based on 30 known cases of systems, varying from collector areas between 1.81 m(2) and 4.38 m(2). Open and closed systems have been considered both with horizontal and vertical storage tanks. In addition to the above, an attempt was made to consider a large variety of 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 collector area, storage tank heat loss coefficient (U-value), tank type, storage volume, type of system, and ten readings from real experiments of total daily solar radiation, mean ambient air temperature, and the water temperature in the storage tank at the beginning of a day. The network output is the useful energy extracted from the system and the temperature rise in the stored water. The statistical R-2-value obtained for the training data set was equal to 0.9722 and 0.9751 for the two output parameters respectively. Unknown data were subsequently used to investigate the accuracy of prediction. These include systems considered for the training of the network at different weather conditions and completely unknown systems. Predictions within 7.1% and 9.7% were obtained respectively. These results indicate that the proposed method can successfully be used for the estimation of the useful energy extracted from the system and the temperature rise in the stored water. The advantages of this approach compared to the conventional algorithmic methods are the speed, the simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network. Additionally, actual weather data have been used for the training of the network, which leads to more realistic results as compared to other modeling programs, which rely on TMY data that are not necessarily similar to the actual environment in which a system operates. (C) 1999 Elsevier Science Ltd. All rights reserved.

人工神经网络（ANN）作为一种提供解决复杂和定义不明确问题的替代方法的技术被广泛接受。它们可以通过实例进行训练来预测结果，具有容错能力，能够处理非线性问题，并且一旦训练完成就可以高速进行预测。人工神经网络已被用于多种应用中，并且在系统建模和系统识别方面已显示出特别的用途。 这项工作的目的是训练一个人工神经网络，使其能够在最少的输入数据下学习预测太阳能家用热水（SDHW）系统中提取的有用能量以及储水的温度升高。基于30个已知的系统案例对一个人工神经网络进行了训练，这些案例的集热器面积在1.81平方米到4.38平方米之间变化。考虑了带有水平和垂直储水箱的开式和闭式系统。除此之外，还尝试考虑了多种天气条件。通过这种方式，对网络进行训练以接受和处理一些特殊情况。作为输入的数据有集热器面积、储水箱热损失系数（U值）、水箱类型、储水体积、系统类型，以及来自实际实验的每日总太阳辐射、平均环境空气温度和一天开始时储水箱水温的十个读数。网络的输出是从系统中提取的有用能量以及储水的温度升高。对于训练数据集，两个输出参数获得的统计R²值分别等于0.9722和0.9751。 随后使用未知数据来研究预测的准确性。这些数据包括在不同天气条件下用于网络训练的系统以及完全未知的系统。分别获得了在7.1%和9.7%以内的预测结果。这些结果表明，所提出的方法可以成功地用于估计从系统中提取的有用能量以及储水的温度升高。与传统算法方法相比，这种方法的优势在于速度、简单性以及网络从实例中学习的能力。这是通过在网络中嵌入经验知识来实现的。此外，实际天气数据已被用于网络的训练，与其他依赖于典型气象年（TMY）数据的建模程序相比，这会产生更符合实际的结果，因为TMY数据不一定与系统运行的实际环境相似。（C）1999年爱思唯尔科学有限公司。保留所有权利。