Forced circulation solar water heater performance prediction by TRNSYS and ANN
The design and applicability of solar water heating systems requires a satisfactory prediction of collector outlet water temperature and the useful energy delivered over a wide range of climatic conditions. Transient system simulation program is extensively used for this purpose, and recently artifi...
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| Main Authors: | , |
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| Format: | Journal |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=23944493660&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/62130 |
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| Institution: | Chiang Mai University |
| Summary: | The design and applicability of solar water heating systems requires a satisfactory prediction of collector outlet water temperature and the useful energy delivered over a wide range of climatic conditions. Transient system simulation program is extensively used for this purpose, and recently artificial neural networks have also been considered. This article presents the results of a study carried out to compare the performance prediction by these two methods in a tropical location under different climatic conditions. Experimental collector outlet temperature, storage tank temperatures and the useful energy values were compared with the results of the simulation by these two methods. Hourly and daily values under different conditions were also compared. Details of the experimental set up and observations, the módeling procedure used and the statistical measures to compare the capabilities of the two methods under clear, partly cloudy, and cloudy conditions have been described. The prediction of both the methods are found to be good for both hourly and daily estimations, and the specific requirements for satisfactory performance prediction of each of the methods have been detailed. © 2005 Taylor & Francis Ltd. |
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