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Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine

Author

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  • Zhijian Liu

    (Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

  • Hao Li

    (College of Chemistry, Sichuan University, Chengdu 610064, China)

  • Xinyu Zhang

    (National Center for Quality Supervision and Testing of Solar Heating Systems (Beijing), China Academy of Building Research, Beijing 100013, China)

  • Guangya Jin

    (Department of Power Engineering, School of Energy, Power and Mechanical Engineering, North China Electric Power University, Baoding 071003, China)

  • Kewei Cheng

    (School of Computing, Informatics, Decision Systems Engineering (CIDSE), Ira A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85281, USA)

Abstract

The determinations 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, the direct determination requires complex detection devices and a series of standard experiments, which also wastes too much time and manpower. To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat loss coefficient without a direct determination. Parameters that can be easily obtained by “portable test instruments” were set as independent variables, including tube length, number of tubes, tube center distance, heat water mass in tank, collector area, final temperature and angle between tubes and ground, while the heat collection rate and heat loss coefficient determined by the detection device were set as dependent variables respectively. Nine hundred fifteen samples from in-service water-in-glass evacuated tube solar water heaters were used for training and testing the models. Results show that the multilayer feed-forward neural network (MLFN) with 3 nodes is the best model for the prediction of heat collection rate and the general regression neural network (GRNN) is the best model for the prediction of heat loss coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively).

Suggested Citation

  • Zhijian Liu & Hao Li & Xinyu Zhang & Guangya Jin & Kewei Cheng, 2015. "Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine," Energies, MDPI, vol. 8(8), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:8:p:8814-8834:d:54488
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    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. 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.
    5. Mohanty, Sthitapragyan & Patra, Prashanta K. & Sahoo, Sudhansu S. & Mohanty, Asit, 2017. "Forecasting of solar energy with application for a growing economy like India: Survey and implication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 539-553.
    6. Nie, Yazhou & Deng, Mengsi & Shan, Ming & Yang, Xudong, 2023. "Clean and low-carbon heating in the building sector of China: 10-Year development review and policy implications," Energy Policy, Elsevier, vol. 179(C).

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