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Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters

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  • Zhijian Liu
  • Kejun Liu
  • Hao Li
  • Xinyu Zhang
  • Guangya Jin
  • Kewei Cheng

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 915measuredsamples of water-in-glass evacuated tube solar water heaters. This artificial neural networks-based software program automatically obtained accurate heat collection rateand 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.

Suggested Citation

  • Zhijian Liu & Kejun Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Artificial Neural Networks-Based Software for Measuring Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0143624
    DOI: 10.1371/journal.pone.0143624
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    References listed on IDEAS

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    1. Mekhilef, S. & Saidur, R. & Safari, A., 2011. "A review on solar energy use in industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(4), pages 1777-1790, May.
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    1. Athanasios Anagnostis & Serafeim Moustakidis & Elpiniki Papageorgiou & Dionysis Bochtis, 2022. "A Hybrid Bimodal LSTM Architecture for Cascading Thermal Energy Storage Modelling," Energies, MDPI, vol. 15(6), pages 1-24, March.
    2. Zhijian Liu & Hao Li & Guoqing Cao, 2017. "Quick Estimation Model for the Concentration of Indoor Airborne Culturable Bacteria: An Application of Machine Learning," IJERPH, MDPI, vol. 14(8), pages 1-9, July.
    3. He, Zhaoyu & Guo, Weimin & Zhang, Peng, 2022. "Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    4. Chen, Xiaomeng & Yang, Xudong & Li, Muran, 2022. "Combining horizontal evacuated tubes with booster mirror reflector to achieve seasonal reverse output: Technical and experimental investigation," Renewable Energy, Elsevier, vol. 188(C), pages 450-464.
    5. Mao, Chunliu & Li, Muran & Li, Na & Shan, Ming & Yang, Xudong, 2019. "Mathematical model development and optimal design of the horizontal all-glass evacuated tube solar collectors integrated with bottom mirror reflectors for solar energy harvesting," Applied Energy, Elsevier, vol. 238(C), pages 54-68.

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