title: Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters.
abstract: Measurements of heat collection rate and heat loss coefficient are crucial for the evaluation of in service water-in-glass evacuated tube solar water heaters. However, conventional measurement requires expensive detection devices and undergoes a series of complicated procedures. To simplify the measurement and reduce the cost, software based on artificial neural networks for measuring heat collection rate and heat loss coefficient of water-in-glass evacuated tube solar water heaters was developed. Using multilayer feed-forward neural networks with back-propagation algorithm, we developed and tested our program on the basis of 915 measured samples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rate and heat loss coefficient using simply "portable test instruments" acquired parameters, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, angle between tubes and ground and final temperature. Our results show that this software (on both personal computer and Android platforms) is efficient and convenient to predict the heat collection rate and heat loss coefficient due to it slow root mean square errors in prediction. The software now can be downloaded from http://t.cn/RLPKF08.
authors.affiliation: Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Liu
authors.affiliation: College of Software Engineering, Sichuan University, Chengdu, Sichuan, 610064, PR China.
authors.forename: Kejun
authors.initials: K
authors.lastname: Liu
authors.affiliation: College of Chemistry, Sichuan University, Chengdu, Sichuan, 610064, PR China.
authors.forename: Hao
authors.initials: H
authors.lastname: Li
authors.affiliation: National Center for Quality Supervision and Testing of Solar Heating Systems (Beijing), China Academy of Building Research, Beijing, 100013, PR China.
authors.forename: Xinyu
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding, Hebei, 071003, PR China.
authors.forename: Guangya
authors.initials: G
authors.lastname: Jin
authors.affiliation: School of Computing, Informatics, and Decision Systems Engineering (CIDSE), Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, Arizona, United States of America.
authors.forename: Kewei
authors.initials: K
authors.lastname: Cheng
mesh_terms: D005898:Glass; D006361:Heating / Q000295:instrumentation*; D006358:Hot Temperature; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D012984:Software; D013472:Sunlight; D013997:Time Factors; D014867:Water
chemical_list: D014867:Water