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: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
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| Series: | International Journal of Photoenergy |
| Online Access: | http://dx.doi.org/10.1155/2017/4194251 |
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| _version_ | 1832545661657022464 |
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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 1687-529X |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| 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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