Abstract
BackgroundHeat 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.FindingsTo 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.ConclusionsAs 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.
Highlights
Heat collection rate and heat loss coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters
In spite of these progresses, there still remains a question that needed to be solved: given that the lowest average root mean square error (RMSE) for the prediction of heat loss coefficient (0.73 in support vector machine (SVM), 0.71 in artificial neural networks (ANNs)) is still relatively higher than those of heat collection rates (0.29 in SVM, 0.14 in ANN), can we further improve the RMSEs when predicting the heat loss coefficients? the RMSEs of predicting heat loss coefficients are relatively low, which is acceptable to further applications, results show that the precision in predicting the heat loss coefficients can still be improved because their RMSEs in testing is still higher than those of predicted heat collection rates (Liu et al 2015a, b)
Though its average RMSE in predicting heat collection rates is slightly higher than the SVM and the multilayer feed-forward neural network (MLFN) with 12 nodes (MLFN-12), the average RMSE in predicting heat loss coefficients are lower than previous machine learning methods, which indicates that the Extreme learning machine (ELM) can reduce the prediction errors of heat loss coefficients
Summary
Heat collection rate and heat loss coefficient are crucial indicators for the evaluation of in service water-in-glass evacuated tube solar water heaters. Based on the 915 data groups, artificial neural networks (ANNs) and support vector machine (SVM) were successfully proved to be efficient and precise for predicting the heat collection rates and heat loss coefficients in testing set (Liu et al 2015a). To further solve this problem, in this short report, we show that ELM has a lower RMSE for predicting the heat loss coefficients of water-in-glass evacuated tube solar water heaters.
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