Earticle

초록

영어

In this paper, a new model, core vector regression (CVR) optimized by memetic algorithm (MA), is presented to predict electric daily load. Support vector regression (SVR) has obtained wide focus in recent years to solve nonlinear regression problems in many fields. However, it is limited on large scale dataset problem because of its high time and space complexity. Hence, CVR is proposed to improve the SVR on solving large scale dataset problem. Proper parameters selection of CVR model determines the complexity and accuracy of the model. In this paper, MA is proposed to optimize the parameters of CVR, which is called MA-CVR. Electric load is the time-dependent data which shows recurrent pattern weekly, seasonally and yearly. In this paper, we adopt MA optimization method and choose adaptive parameters dynamically based on time recurrent character of electric load data. Experimental results show that MA-CVR outperforms the existing model optimized by genetic algorithm which is called GA-CVR.

목차

Abstract
 1. Introduction
 2. Problem Statement
 3. The Proposed Algorithm
  3.1. Core Vector Regressions
  3.2. Memetic Algorithm
  3.3. MA-CVR Model
 4. Case Studies
  4.1. Historical Data
  4.2. Construction of Datasets And Samples
  4.3. Model Parameters
  4.4 Evaluation Method
  4.5 Results
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Yuancheng Li [ School of Control and Computer Engineering, North China Electric Power University, Beijing, China ]
• Rong Ma [ School of Control and Computer Engineering, North China Electric Power University, Beijing, China ]
• Liqun Yang [ School of Control and Computer Engineering, North China Electric Power University, Beijing, China ]
• Pu Chen [ School of Control and Computer Engineering, North China Electric Power University, Beijing, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605