Earticle

초록

영어

In order to accurately, fast and efficiently forecast the short-term load of power system, an improved particle swarm optimization algorithm is proposed to optimize the parameters of fuzzy radial basis function fuzzy neural network(FRBFNN) model in order to train the FRBFNN model for obtaining the optimized FRBFNN(IWPSRFN) method. In the proposed IWPSRFN method, the linear decreasing weight method is used to adjust the inertia weight of PSO algorithm. The global optimization ability of improved PSO algorithm is used to adjust the parameters of FRBFNN model by putting these parameters in the particle encoding, then the optimal values are found in the large number of viable solutions by continuous iteration of improved PSO algorithm. The found optimal values are regarded as the parameters of FRBFNN model to obtain the final IWPSRFN method for forecasting short-term load of power system. Finally, a certain region is selected to test the effectiveness of IWPSRFN method, the experiment results show that the improved PSO algorithm can effectively optimize the weights of FRBFNN and solve the slow convergence speed, and the IWPSRFN method can obtain the higher prediction accuracy and is an effective method for forecasting short-term load.

목차

Abstract
 1. Introduction
 2. PSO Algorithm and Improved PSO Algorithm
  2.1. PSO Algorithm
  2.2. An Improved PSO Algorithm
 3. Fuzzy Neural Network(FNN)
 4. The IWPSRFN Model and Algorithm
  4.1. The Optimized RBFFNN Model Based on Improved PSO Algorithm
  4.2. The Steps of IWPSRFN Model
 5. Application of the IWPSRFN Algorithm in Load Forecasting
 6. Conclusion
 Acknowledgment
 References

키워드

저자

• Yang Liu [ College of Information Science and Engineering, Hunan City University, Yiyang 413000, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605