Journal
INTERNATIONAL JOURNAL OF PHOTOENERGY
Volume 2017, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2017/4194251
Keywords
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Funding
- Major Basic Research Development and Transformation Program of Qinghai province [2016-NN-141]
- Natural Science Foundation of Hebei [E2017502051]
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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 highperformance 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 (WGETSWH) 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.
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