Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters

Zhijian Liu; Hao Li; Xindong Tang; Xinyu Zhang; Fan Lin; Kewei Cheng

doi:10.1186/s40064-016-2242-1

Extreme learning machine: a new alternative for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters

Springerplus. 2016 May 14:5:626. doi: 10.1186/s40064-016-2242-1. eCollection 2016.

Authors

Zhijian Liu^#¹, Hao Li^#², Xindong Tang³, Xinyu Zhang⁴, Fan Lin⁵, Kewei Cheng⁶

Affiliations

¹ Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, 071003 China.
² College of Chemistry, Sichuan University, Chengdu, 610064 China.
³ College of Mathematics, Sichuan University, Chengdu, Sichuan, 610064 China.
⁴ National Center for Quality Supervision and Testing of Solar Heating Systems (Beijing), China Academy of Building Research, Beijing, 100013 China.
⁵ School of Software, Xiamen University, Xiamen, 361005 China.
⁶ School of Computing, Informatics, Decision Systems Engineering (CIDSE), Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85281 USA.

^# Contributed equally.

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.

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.

Keywords: Extreme learning machine; Heat collection rate; Heat loss coefficient; Portable test instruments; Water-in-glass evacuated tube solar water heaters.