Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.集热速率与热损失系数的测量，对于在用全玻璃真空管式太阳能热水器（water-in-glass evacuated tube solar water heaters）的性能评估至关重要。然而传统测量方法不仅需要昂贵的检测设备，还需遵循一系列繁杂的操作流程。为简化测量流程、降低检测成本，本研究开发了基于人工神经网络（artificial neural networks）的全玻璃真空管式太阳能热水器集热速率与热损失系数测量软件。本研究采用搭载反向传播算法（back-propagation algorithm）的多层前馈神经网络，基于915组全玻璃真空管式太阳能热水器的实测样本完成了该软件的开发与测试。该基于人工神经网络的软件，仅需使用便携式测试仪器采集的参数即可自动获取精准的集热速率与热损失系数，所需参数包括真空管长度、真空管数量、管中心间距、水箱内热水质量、集热面积、真空管与地面的夹角以及最终水温。研究结果表明，该支持个人电脑与安卓（Android）平台的软件，因预测过程中的均方根误差（root mean square error）较低，可高效便捷地实现集热速率与热损失系数的预测。该软件目前可从http://t.cn/RLPKF08下载获取。