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Two Day Ahead Short Term Load Forecasting Neural Network Based

Authors: Firas M. Tuaimah

Abstract:

This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity.

The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: Artificial Neural Networks, short-term load forecasting, back propagation learning

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1094078

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References:


[1] S. Q. Quaiyum, Y.I. Khan, S. Rahman, P. Barman "Artificial Neural Network based Short Term Load Forecasting of Power System” IJCA (0975-8887), vol. 30, no. 4, 2011, pp. 1–7.
[2] M.Buhari, S. S. Adamu "Short Term Load Forecasting using Artificial Neural Network” IMECS, 2012, vol. I.
[3] H.S. Hippert, C.E. Pedreira, and R.C. Souza, "Neural networks for short term load forecasting: A review and evaluation” IEEE Trans.Power Syst., vol. 16, no. 1,Feb. 2001, pp. 44–55.
[4] X. Cui, T. E. Potok and P. Palothingal. 2005. Document Clustering using Particle Swarm Optimization, IEEE Transaaction, May 2005.
[5] S. Haykin. "Neural Networks”, 2nd ed., A Comprehensive Foundation, MacMillan Publishing, Englewood Cliffs, N.J. 1999
[6] K.L. Ho, "Short Term Load Forecasting Using a Multilayer neural Network with an Adaptive Learning Algorithm”, IEEE Trans.on Power systems, vol. 7, No.1, pp.141-149, Feb. 1992.
[7] Y. Rui, A. A. El-Keib "A Review of ANN-Based Short term Load Forecasting Models” Department of Electrical Engineering, University of Alabama, Tuscaloosa, AL 35487, Unpublished.