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Particle Swarm Optimization-Based RBF Neural Network Load Forecasting Model

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2 Author(s)
Ning Lu ; Digital Eng. & Simulation Res. Center, Huazhong Univ. of Sci. & Technol., Wuhan ; Jianzhong Zhou

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. Electric power load is difficult to be forecasted accurately for its complicacy and uncertainty if no numerical algorithm model is applied. In order to improve the precision of electric power system short term load forecasting, a new model is put forward in this paper. Both particle swarm optimization (PSO) algorithm and radial basis function(RBF) neural network are taken into use in this paper. PSO is a novel random optimization method which has been found to be powerful in solving nonlinear optimization problems. In this paper, PSO is applied to optimize the weighting factor of neural network. Theoretical analysis and simulation prove that the load forecasting model which optimized by PSO is more accurate than the traditional RBF neural network model.

Published in:

Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific

Date of Conference:

27-31 March 2009