Earticle

초록

영어

Optimized GM(1,1) model based on least absolute criteria is proposed in this paper. Since the initial condition of original GM(1,1) model is not very suitable, we use the modified latest data which generating from the accumulative generating operation as the new initial condition. And the least absolute criteria is applied instead of least square criteria to improve the stability and prediction accuracy of GM(1,1) model. Then the particle swarm optimization is adapted to the parameters optimization. At the end, the optimized GM(1,1) model is used to predict the whole social electricity consumption of China and the result shows its prediction accuracy is better than the original model and the GM(1,1) model with latest initial condition.

목차

Abstract
 1. Introduction
 2. Original GM (1,1) Model
 3. Optimized GM(1,1) Model
  3.1. Optimization of Initial Condition
  3.2. PSO-GM(1,1) Model based on Least Absolute Criteria
 4. Model Construction and Prediction
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Zhang Zheng [ School of Management, Hefei University of Technology, Hefei, 230009, P.R. China ]
• Yang Shanlin [ School of Management, Hefei University of Technology, Hefei, 230009, P.R. China ]
• Liu Huizhou [ Tongling Power Supply Company, Anhui Electric Power Corporation, Tongling, 244000, P.R. China ]
• Yu Bengong [ School of Management, Hefei University of Technology, Hefei, 230009, P.R. China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Smart Home

• 간기

  격월간

• pISSN

  1975-4094

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605