### Short Term Load Forecasting Using BP Neural Network Optimized by Particle Swarm Optimization

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#### Abstract

Electric power system load forecasting plays an important role in the Energy Management System (EMS), which has great effect on the operation, controlling and planning of electric power system. A precise electric power system short term load forecasting will lead to economic cost saving and right decisions on generating electric power. A short-term load forecasting (STLF) method based on back propagation (BP) neural network which is optimized by particle swarm optimization (PSO) algorithm is presented in this paper. The PSO is used to optimize the initial parameters of the BP neural network, then based on the optimized result, the BP neural network is used for short-term load forecasting. The experiment results show the method in the paper has greater improvement in both accuracy and velocity of convergence for BP neural network. Consequently, the model is practical and effective and provides alternative for forecasting electricity load. *Copyright © Praise Worthy Prize - All rights reserved.*

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R.J Piers, k.Adamson. Methodologies for Load Forecasting, Intelligent systems, 2006 3rd International IEEE Conference, Sept 2006, PP.800-806.

N.Amjady. Short – Term Hourly Load Forecasting Using Time - Series Modeling with Peak Load Estimation Capability, IEEE Trans. Power Syst. 16(4) (2001) 798- 805.

Yang H T, Huang C M, Huang C L, Identiﬁcation of ARMAX model for short term load forecasting: An evolutionary programming approach. IEEE Trans. Power Syst., 1996, 11(1): 403-408.

Chen Jf, Wang WM, Huang CM., Analysis of an adaptive time series autoregressive moving – average (ARMA) model for short term load forecasting. Electric Power Syst Res 1995; 187-96.

S. Ruzic, A. Vuckovic, and N. Nikolic. Weather Sensitive Method for Short-Term Load Forecasting in Electric Power Utility of Serbia. IEEE Transactions on Power Systems, 18:1581–1586, 2003.

Ekmen I, Topalli A. Four methods for short term load forecasting using the benefits of artificial load intelligence, Electr Eng 2003; 85(4):229-33.

Charytoniuk W, Chen M. Very short term Forecasting using artificial neural networks, IEEE Trans Power Syst 2000;3:292-9;

S.J. Kiartzis and A.G. Bakirtzis. A Fuzzy Expert System for Peak Load Forecasting, Application to the Greek Power System Proceedings of the 10th Mediterranean Electrotechnical Conference, 3:1097–1100, 2000.

Farahat, M.A., Talaat, M., The using of curve fitting prediction optimized by genetic algorithms for short-term load forecasting, (2012) International Review of Electrical Engineering (IREE), 7 (6), pp. 6209-6215.

Ashour, Z.H., Farahat, M.A., A new artificial neural network approach with selected inputs for short term electric load forecasting, (2008) International Review of Electrical Engineering (IREE), 3 (1), pp. 32-36.

S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Prentice-Hall Inc., 1999.

Zhang Caiqing, Lin Ming and Tang Mingyang. BP Neural Network Optimized with PSO Algorithm for Daily Load Forecasting, in: Information Management, Innovation Management and Industrial Engineering, IEEE Conference Publications vol.3, 2008, PP. 82 – 85.

A. Carlisle and G Dozier, (2001) an off-the-shelf PSO, Proceeding of the workshop on Particle Swarm Optimization, Indianapolis.

C. Eberhart, Y. Shi. Parameters selection in particle swarm optimization, in: W. Porto, N. Saravanan, D. Waagen, E. Eiben (Eds.), Evolutionary Programming VII, vol. 1447, Springer-Verlag, 1998, pp. 591–600.

F. Van den Bergh, A. Engelbrecht. Cooperative learning in neural networks using particle swarm optimizers, S. Afr. Comput. J. 26 (2000) 84–90.

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