期刊
SOLAR ENERGY
卷 142, 期 -, 页码 61-67出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2016.12.015
关键词
Water-in-glass evacuated tube solar water heater (WGET-SWH); Heat collection rate; High-throughput screening (HTS); Artificial neural networks (ANNs)
资金
- Major Basic Research Development and Transformation Program of Qinghai province [2016-NN-141]
How to design water-in-glass evacuated tube solar water heater (WGET-SWH) with high heat collection rates has long been a question. Here, we propose a high-throughput screening (HTS) method based on machine learning to design and screen 3.538125 x 10(8) possible combinations of extrinsic properties of WGET-SWH, to discover promising WGET-SWHs by comparing their predicted heat collection rates. Two new-designed WGET-SWHs were installed experimentally and showed higher heat collection rates (11.32 and 11.44 MJ/m(2), respectively) than all the 915 measured samples in our previous database. This study shows that we can use the HTS method to modify the design of WGET-SWH with just few knowledge about the highly complicated correlations between the extrinsic properties and heat collection rates of solar water heaters. (C) 2016 Elsevier Ltd. All rights reserved.
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