Journal
SPRINGERPLUS
Volume 5, Issue -, Pages -Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1186/s40064-016-2242-1
Keywords
Water-in-glass evacuated tube solar water heaters; Portable test instruments; Heat collection rate; Heat loss coefficient; Extreme learning machine
Categories
Funding
- Fundamental Research Funds for the Central Universities [2015MS108]
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Background: Heat collection rate and heat loss coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, the direct determination requires complex detection devices and a series of standard experiments, wasting too much time and manpower. Findings: To address this problem, we previously used artificial neural networks and support vector machine to develop precise knowledge-based models for predicting the heat collection rates and heat loss coefficients of water-in-glass evacuated tube solar water heaters, setting the properties measured by portable test instruments as the independent variables. A robust software for determination was also developed. However, in previous results, the prediction accuracy of heat loss coefficients can still be improved compared to those of heat collection rates. Also, in practical applications, even a small reduction in root mean square errors (RMSEs) can sometimes significantly improve the evaluation and business processes. Conclusions: As a further study, in this short report, we show that using a novel and fast machine learning algorithm-extreme learning machine can generate better predicted results for heat loss coefficient, which reduces the average RMSEs to 0.67 in testing.
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