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International Journal of Photoenergy
Volume 2017, Article ID 4194251, 10 pages
Research Article

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

1Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
2Institute for Computational and Engineering Sciences, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USA
3Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China
4Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005-1827, USA
5School of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, China

Correspondence should be addressed to Hao Li; ude.saxetu@oahil

Received 12 July 2017; Revised 6 August 2017; Accepted 5 September 2017; Published 24 September 2017

Academic Editor: Zhonghao Rao

Copyright © 2017 Hao Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.