title: Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters.
abstract: 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.
  
  
  RESULTS
  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.
authors.affiliation: Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003 China.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Liu
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, 610064 China.
authors.forename: Hao
authors.initials: H
authors.lastname: Li
authors.affiliation: College of Mathematics, Sichuan University, Chengdu, Sichuan, 610064 China.
authors.forename: Xindong
authors.initials: X
authors.lastname: Tang
authors.affiliation: National Center for Quality Supervision and Testing of Solar Heating Systems (Beijing), China Academy of Building Research, Beijing, 100013 China.
authors.forename: Xinyu
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Software, Xiamen University, Xiamen, 361005 China.
authors.forename: Fan
authors.initials: F
authors.lastname: Lin
authors.affiliation: School of Computing, Informatics, Decision Systems Engineering (CIDSE), Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85281 USA.
authors.forename: Kewei
authors.initials: K
authors.lastname: Cheng
keywords: Extreme learning machine; Heat collection rate; Heat loss coefficient; Portable test instruments; Water-in-glass evacuated tube solar water heaters