Design of high-performance water-in-glass evacuated tube solar water heaters by a high-throughput screening based on machine learning: A combined modeling and experimental study

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• 作者：Zhijian Liu^a ; ¹ ; ^{lzj6035@163.com} ; Hao Li^b ; ¹ ; Kejun Liu^c ; ¹ ; Hancheng Yu^d ; Kewei Cheng^e
• 关键词：Water-in-glass evacuated tube solar water heater (WGET-SWH) ; Heat collection rate ; High-throughput screening (HTS) ; Artificial neural networks (ANNs)
• 刊名：Solar Energy
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：142
• 期：Complete
• 页码：61-67
• 全文大小：1303 K
• 卷排序：142

文摘

A novel method for designing water-in-glass evacuated tube SWHs was proposed. 3.538125 × 108 combinations of extrinsic properties were screened by a machine learning model. Two new designs with high predicted heat collection rates (HCRs) were installed & measured. The two new designs had higher measured HCRs than all the 915 samples in our previous database.