Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

References

• Asgari, H. 2014. Modelling, simulation and control of gas turbines using artificial neural networks. PhD thesis, Mechanical Engineering, University of Canterbury, Christchurch, New Zealand.
   Google Scholar
• Beale, M.H., M.T. Hagan, and H.B. Demuth. 2014. Neural network toolbox™ user’s guide, R2014a ed. Natick, MA: The MathWorks.
   Google Scholar
• Bradshaw, P., and R.C. Pankhurst. 1964. The design of low-speed wind tunnel. Progress in Aerospace Science5 (1):1–10. doi:10.1016/0376-0421(64)90003-X.
   Google Scholar
• Cybenko, G. 1989. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals, and Systems 2 (4):303–14. doi:10.1007/BF02551274.
   Google Scholar
• Diaconescu, E. 2008. The use of NARX neural networks to predict chaotic time series.Computer Research 3(3):182–91.
   Google Scholar
• Eugen, H. 2012. NARX neural networks for sequence processing tasks. Master of Science thesis, Universitat Politècnica de Catalunya – UniversitatRovira i Virgili – Universitat de Barcelona.
   Google Scholar
• Gao, Y., and E.M. Joo. 2005. NARMAX time series model prediction: Feedforward and recurrent fuzzy neural network approaches. Fuzzy Sets and Systems 150:331–50. doi:10.1016/j.fss.2004.09.015.
   Web of Science ®Google Scholar
• Geetha, N.B., and R. Velraj. 2012. Passive cooling methods for energy efficient buildings with and without thermal energy storage---A review. Energy Education Science and Technology Part A: Energy Science and Research 29 (2):913–46.
   Google Scholar
• Giabaklou, Z., and J.A. Ballinger. 1996. A passive evaporative cooling system by natural ventilation. Building and Environment 31 (6):503–07. doi:10.1016/0360-1323(96)00024-8.
   Web of Science ®Google Scholar
• Hunt, K.J., D. Sbarbaro, R. Zbikowski, and P.J. Gawthrop. 1992. Neural networks for control systems-survey. Automatica 28 (6):1083–112. doi:10.1016/0005-1098(92)90053-I.
   Web of Science ®Google Scholar
• Karlik, B., and A.V. Olgac. 2010. Performance analysis of various activation functions in generalized MLP architectures of neural networks. International Journal of Artificial Intelligence and Expert Systems 1(4):111–22.
   Google Scholar
• Khalil, E.E. 2011. Energy-eficiency in arcnditionedbildings: The green buildings dream. International Journal of Thermal & Environmental Engineering 2:9–18. doi:10.5383/ijtee.02.01.002.
   Google Scholar
• La, D., Y.J. Dai, Y. Li, R.Z. Wang, and T.S. Ge. 2010. Technical development of rotary desiccant dehumidification and air conditioning: A review. Renewable and Sustainable Energy Reviews 14 (1):130–47. doi:10.1016/j.rser.2009.07.016.
   Web of Science ®Google Scholar
• Liao, C. M., and K. H.Chiu. 2002. Wind tunnel modeling the system performance of alternative evaporative cooling pads in Taiwan region. Building and Environment 37:177–87. doi:10.1016/S0360-1323(00)00098-6.
   Web of Science ®Google Scholar
• Lin, T. N., B. G.Horne, P.Tino, and C. L.Giles. 1996. Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks 7(6):1329–38. doi:10.1109/72.548162.
   PubMed Web of Science ®Google Scholar
• Mohanraj, M., S. Jayaraj, and C. Muraleedharan. 2008. Modeling of a direct expansion solar assisted heat pump using artificial neural networks. International Journal of Green Energy 5(6):520–32. doi:10.1080/15435070802498499.
   Web of Science ®Google Scholar
• Pan, X., B.Lee, and C.Zhang. 2013. A comparison of neural network backpropagation algorithms for electricity load forecasting. IEEE International Workshop on Intelligent Energy Systems (IWIES), Vienna 22–27 doi:10.1109/IWIES.2013.6698556.
   Google Scholar
• Rae, W.H., and A. Pope. 1984. Low-speed wind tunnel testing. New York: Wiley.
   Google Scholar
• Siegelmann, H.T., B.G. Horne, and C.L. Giles. 1997. Computational capabilities of recurrent NARX neural networks. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: A publication of the IEEE Systems, Man, and Cybernetics Society 27(2):208–15. doi:10.1109/3477.558801.
   PubMed Web of Science ®Google Scholar
• Xiaofeng, L., and Y. Chunshan. 2014. Research and application of data mining and NARX neural Networks in Load Forecasting. International Journal of Database Theory and Application 7(2):13–24. doi:10.14257/ijdta.2014.7.2.02
   Google Scholar
• Yu, H., and M.Bogdan. 2011. Levenberg–Marquardt training. Industrial Electronics Handbook (5 12): 12–15.
   Google Scholar
• Zulkeflee, S.A., A.S. Suhairi, and A. Norashid. 2011. Nonlinear autoregressive with Exogenous inputs based model predictive control for batch citronellyllaurate esterification reactor. In T. Zheng (ed.). Advanced Model Predictive Control. Rijeka, Croatia:, InTech
   Google Scholar