Al-Waeli A. H. A., Sopiana, K., Kazemb, H. K., Yousif, J. H., Chaichanc, M. T., Ibrahima, A., Mat, S. & Ruslana, M. (2018). Comparison of prediction methods of PV/T nanofluid and nano-PCM system using a measured dataset and artificial neural network. Solar Energy, 162, 378-396.
Baek, S. M., Nam, J. H., Hong, H. & Kim, C. (2011). Effect of brine flow rate on the performance of a spiral-jacketed thermal storage tank used for SDHW systems: A computational fluid dynamics study. Applied Thermal Engineering, 31, 2716-2725
Bellos, E., Tzivanidis, C., Antonopoulos, K. A. & Gkinis, G. (2016). Thermal enhancement of solar parabolic trough collectors by using nanofluids and converging-diverging absorber tube. Renewable Energy, 94, 213–22.
Cetiner, C., Halici, F., Cacur, H. & Taymaz, I. (2005). Generating hot water by solar energy and application of neural network. Applied Thermal Engeering, 25 (8-9), 1337–48.
Farkas, I. & Geczy-Vıg, P. (2003). Neural network modelling of flat-plate solar collectors. Computer and Electronic in Agriculture, 40 (1-3), 87–102.
Fischer, S., Frey, P. & Drück, H. (2012). Comparison between state-of-the-art and neural network modelling of solar collectors. Solar Energy, 86 (1), 3268–3277.
Forristall, R. (2003). Heat transfer analysis and modeling of a parabolic trough solar receiver implemented in engineering equation solver. Colorado: National Renewable Energy Laboratory (NREL).
Ghritlahre, H. K. & Prasad, R. K. (2018). Application of ANN technique to predict the performance of solar collector systems - A review. Renewable and Sustainable Energy Reviews, 84, 75-88.
Iranmanesh, M., Akhijahani, H. S., & Jahromi, M. S. B. (2020). CFD modeling and evaluation the performance of a solar cabinet dryer equipped with evacuated tube solar collector and thermal storage system. Renewable Energy, 145, 1192-1213.
Kalogirou, S. A. (2006). Prediction of flat-plate collector performance parameters using arti-ficial neural network. Solar Energy, 80 (3), 248–59.
Kalogirou, S. A, Panteliou, S. & Dentsoras, A. (1999). Modeling of solar domestic water heating systems using artificial neural networks. Solar Energy, 65(6), 335–342.
Kumaresan, G., Sridhar, R. & Velraj, R. (2012). Performance studies of a solar parabolic trough collector with a thermal energy storage system. Energy, 47 (1), 395-402.
Lecoeuche, S. & Lalot, S. (2005). Prediction of the daily performance of solar collectors. International Communication of Heat and Mass Transfer, 32 (5), 603–11.
Motahayyer, M., Arabhosseini, A., Samimi-Akhijahani, H. & Khashechi, M. (2018). Application of computational fluid dynamics in optimization design of absorber plate of solar dryer. Iranian Journal of Biosystem Engineering, 49 (2), 285-294. (In Farsi)
Schmidhuber, J. (2015). Deep Learning in Neural Networks: An Overview. Neural Networks, 61, 85-117.
Scapino, L., Zondag, H. A., Diriken, J., Rindt, C. C. M., Van Bael, J. & Sciacovelli, A. (2019). Modeling the performance of a sorption thermal energy storage reactor using artificial neural networks. Applied Energy, 253, 1-15.
Tay, N. H. S., Bruno, F. & Belusko, M. (2012). Experimental validation of a CFD model for tubes in a phase change thermal energy storage system. International Journal of heat and Mass Transfer, 55 (4), 574-585.
Varol, Y., Koca, A., Oztop, H. F. & Avci, E. (2010). Forecasting of thermal energy storage performance of phase change material in a solar collector using soft computing techniques. Expert System Applied, 37 (4), 2724–2732.
Xiaohong, G., Bin, L., Yongxian, G. & Xiugan, Y. (2011). Two-dimensional transient thermal analysis of PCM canister of a heat pipe receiver under microgravity. Applied Thermal Engineering, 31 (5): 735–41.
Xie, H., Liu, L., Ma, F. & Fan, H. (2009). Performance prediction of solar collectors using artificial neural networks. Proceeding of the international conference on artificial intelligence and computational intelligence, 573–576.
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