Earticle

초록

영어

Current grey clustering analysis methods have some defects. So, this paper proposes a prediction model based on improved grey clustering analysis. Firstly, it constructs the grey classical domain and the grey sector domain based on prediction subjects and data and according to relevant theory about grey clustering analysis. Secondly, it categorizes samples according to features of prediction subjects and confirms the analysis categories corresponding to the classical domain. Thirdly, based on the grey system theory, it constructs the grey correlation coefficient model and grey correlation degree model so as to obtain the weighed grey correlation degree. Thus, prediction subjects can be divided into proper category. Finally, power load forecasting in the power industry is taken as a case to prove that the model is reliable and has efficacy.

목차

Abstract
 1. Introduction
 2. Grey Sequence and Grey Correlation Coefficient
  2.1. Grey Sequence
  2.2. Grey Correlation Coefficient
 3. An Improved Prediction Model Based on Grey Clustering Analysis
  3.1 Grey Classical Domain and Grey Sector Domain
  3.2. The Weight of Prediction Feature
  3.3. Processing Prediction Features
  3.4. Grey Correlation Coefficient and Grey Correlation Degree
  3.5. The Model and the Algorithm
 4. Case Study of Power Loading Forecast
 5. Conclusion
 Acknowledgments
 References

키워드

저자

• Wang Ya [ Institute of Information Engineering, Xuchang University Xuchang, Henan Province, 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