Faculty of Engineering, Vali-e-Asr University of Rafsanjan, P.O.B. 518, Rafsanjan, Iran.
Environ Monit Assess. 2020 Nov 19;192(12):770. doi: 10.1007/s10661-020-08738-9.
In the current research, the efficiency of a solar flat plate collector (SFPC) was examined experimentally, while the system was modeled with an artificial neural network (ANN) under semi-arid weather conditions of Rafsanjan, Iran. Based on the backpropagation algorithm, a feedforward neural network was established to estimate and forecast the outlet flow temperature of SFPC. To identify the most appropriate model, the ANN topology hidden layer, the number of hidden neurons, iteration, and statistical indicators were analyzed. In the first ANN modeling (CASE I), five parameters, including solar radiation, inlet flow temperature, flow rate, ambient temperature, and wind speed, were applied in the input layer of the network, while the output flow temperature (subsequently efficiency) was in the output layer. In the second artificial neural network modeling (CASE II), the wind speed was omitted from the input of the ANN model. Results showed that the ANN with four inputs yields more accurate results for both estimation and prediction of outlet flow temperature.
在当前的研究中，对太阳能平板集热器 (SFPC) 的效率进行了实验检查，同时在伊朗拉夫桑詹的半干旱天气条件下，使用人工神经网络 (ANN) 对系统进行建模。基于反向传播算法，建立了前馈神经网络来估计和预测 SFPC 的出口流量温度。为了确定最合适的模型，分析了 ANN 拓扑隐藏层、隐藏神经元数量、迭代次数和统计指标。在第一个 ANN 建模（CASE I）中，网络输入层应用了五个参数，包括太阳辐射、入口流量温度、流速、环境温度和风速，而输出流量温度（随后的效率）在输出层。在第二个人工神经网络建模（CASE II）中，风速被从 ANN 模型的输入中省略。结果表明，对于出口流量温度的估计和预测，具有四个输入的 ANN 产生更准确的结果。
