Earticle

초록

영어

In this paper, we presented the performance of forecasting model and error correction will affect the accuracy of short-term load forecasting. Least squares support vector machines (LS-SVM) based on improved particle swarm optimization is selected as load forecasting model. Forecasting accuracy and generalization performance of LS-SVM depend on selection of its parameters greatly. Adaptive particle swarm optimization (APSO) based on fitness function was put forward to optimize the kernel parameter σ and regularization parameter γ of LS-SVM. Based on the optimized forecasting model, non-parametric error correction model is also presented by iterative method. The error forecasted by non-parametric model was used to update the forecasted load so as to improve the forecasting accuracy. Load data selected from some area in South China as training and forecasting data is used to analyze. Case study illustrates that the proposed forecasting model (NP-APSO-SVM) has more generalized performance and better forecasting accuracy compared with the method of standard SVM.

목차

Abstract
 1. Introduction
 2. Methodology
  2.1 Least Squares Support Vector Machine（LSSVM）
  2.2 Parameter Selection using Improved Particle Swarm Optimization
  2.3 Non-Parametric Error Correction
 3. The Case Study
 4. Summary and Conclusion
 Acknowledgements
 References

키워드

저자

• Kun Yang [ College of Quality and Technology Supervision, Hebei University, Baoding, 071002, China ]
• Shuang Liu [ College of Quality and Technology Supervision, Hebei University, Baoding, 071002, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605