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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DOI: 10.1371/journal.pone.0143624
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References listed on IDEAS
- 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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- 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).
- 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.
- 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.
- 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.
- 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.
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