Application of neural network based on particle swarm optimization in short-term load forecasting

  • Authors:

  • Dong-Xiao Niu;Bo Zhang;Mian Xing

  • Affiliations:

  • School of Business Administration, North China Electric Power University, Baoding, China;School of Business Administration, North China Electric Power University, Baoding, China;School of Mathematics and Physics, North China Electric Power University, Baoding, China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
  • Year:
  • 2006

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Abstract

To overcome the defects of neural network (NN) using back-propagation algorithm (BPNN) such as slow convergence rate and easy to fall into local minimum, the particle swarm optimization (PSO) algorithm was adopted to optimize BPNN model for short-term load forecasting (SLTF). Since those defects are partly caused by the random selection of network’s initial values, PSO was used to optimize initial weights and thresholds of BPNN model, thus a novel model for STLF was built, namely PSO-BPNN model. The simulation results of daily and weekly loads forecasting for actual power system show that the proposed forecasting model can effectively improve the accuracy of SLTF and this model is stable and adaptable for both workday and rest-day. Furthermore, its forecasting performance is far better than that of simple BPNN model and BPNN model using genetic algorithm to determine the initial values (GA-BPNN).