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

M. A. Farahat(1*), A. F. Abd Elgawed(2), H. M. M. Mustafa(3), A. Ibrahim(4)

(1) Electrical Power & Machines Department, Faculty of Engineering, Zagazig University, Egypt
(2) Electrical Power & Machines Department, Faculty of Engineering, Zagazig University, Egypt
(3) Managing Director of Information Systems in the Egyptian Electricity Holding Company, Egypt
(4) Electrical Power & Machines Department, Faculty of Engineering, Zagazig University, Egypt
(*) Corresponding author

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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.
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Short Term Load Forecasting; BP Neural Network; Particle Swarm Optimization

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