Journal of Applied Sciences1812-56541812-5662Asian Network for Scientific Information10.3923/jas.2007.2006.2010KrishnaiahDuduku KumaresanSiva Kumar IsidoreMatthew SarbatlyRosalam 122007715This study outlines the artificial neural networks application to improve the prediction capability by investigating the effect of data sampling, network type and configuration as well as the inclusion of past data at the neural network input. Multi layered perception and Elman network were used. Validation results using input data based on 5 min and 1 h sampling was compared. It was found that the 1 h sampling yielded better prediction. Different network configurations were also compared and it was observed that although the larger network showed better prediction capability during the training phase, it was the smaller network that demonstrated better prediction in the validation stage. The inclusion of past data into the neural network was also studied. The generalisation degraded as more past data were included.]]>Baxter, C.W., S.J. Stanley and Q. Zhang,199948129136De Villiers, J. and E. Barnard,19924136141Evans, J., C. Enoch, M. Johnson and P. Williams,199898141145Fletcher, I., A. Adgar, C.S. Cox and T.J. Boehme,20012001Han, T., E. Nahm, K. Woo, Kim and C. Ryu,19971997Holger, R.M., N. Morgan and C.W.K. Chow,200419485494Isidore, J.M., S. Kumaresan and Abdul Noor,20002000pp: 453460Joo, D.S., D.J. Choi and H. Park,20003432953302Mirsepassi, A., B. Cathers and H.B. Dharmappa,19951516521Skapura, D.M.,1996Yu, R.F., S.F. Kang, S.L. Liaw and M.C. Chen,200042403408Jang, J.S.R.,199323665685