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

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...

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Main Authors: Hao Li, Zhijian Liu, Kejun Liu, Zhien Zhang
Format: Article
Language:English
Published: Wiley 2017-01-01
Series:International Journal of Photoenergy
Online Access:http://dx.doi.org/10.1155/2017/4194251
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author Hao Li
Zhijian Liu
Kejun Liu
Zhien Zhang
author_facet Hao Li
Zhijian Liu
Kejun Liu
Zhien Zhang
author_sort Hao Li
collection DOAJ
description 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.
format Article
id doaj-art-a2cf4ad70ffb470db05d8db3307d981e
institution Kabale University
issn 1110-662X
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language English
publishDate 2017-01-01
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record_format Article
series International Journal of Photoenergy
spelling doaj-art-a2cf4ad70ffb470db05d8db3307d981e2025-02-03T07:25:14ZengWileyInternational Journal of Photoenergy1110-662X1687-529X2017-01-01201710.1155/2017/41942514194251Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput ScreeningHao Li0Zhijian Liu1Kejun Liu2Zhien Zhang3Department of Chemistry, The University of Texas at Austin, 105 E. 24th Street, Stop A5300, Austin, TX 78712, USADepartment of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, ChinaDepartment of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005-1827, USASchool of Chemistry and Chemical Engineering, Chongqing University of Technology, Chongqing 400054, ChinaPredicting 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.http://dx.doi.org/10.1155/2017/4194251
spellingShingle Hao Li
Zhijian Liu
Kejun Liu
Zhien Zhang
Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
International Journal of Photoenergy
title Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
title_full Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
title_fullStr Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
title_full_unstemmed Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
title_short Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening
title_sort predictive power of machine learning for optimizing solar water heater performance the potential application of high throughput screening
url http://dx.doi.org/10.1155/2017/4194251
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