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Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
Joint Authors
Liu, Zhijian
Li, Hao
Liu, Kejun
Zhang, Zhien
Source
International Journal of Photoenergy
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-09-24
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Hao& Liu, Zhijian& Liu, Kejun& Zhang, Zhien. 2017. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening. International Journal of Photoenergy،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1168239
Modern Language Association (MLA)
Li, Hao…[et al.]. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening. International Journal of Photoenergy No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1168239
American Medical Association (AMA)
Li, Hao& Liu, Zhijian& Liu, Kejun& Zhang, Zhien. Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening. International Journal of Photoenergy. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1168239
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1168239