A self-adaptive intelligence grey predictive model with alterable structure and its application

The adaptability of the traditional GM (1, 1) model is poor because it is a rigorous homogenous exponent model with a single fixed structure. To improve the adaptability of the traditional grey model, a self-adaptive intelligence grey predictive model with an alterable structure is proposed in this paper. The proposed model has the advantages of adjustable parameters and is characterised by its variable structure as a homogenous/non-homogenous exponent model or as a single-variable linear-auto-regression model. It can be used to automatically compute the relative optimal modelling parameters and adaptively choose a more reasonable model structure based on the real data characteristics of a modelling sequence. Hence, this novel model outperforms traditional grey models with a single fixed structure. To verify its efficiency and applicability, the proposed model was used to simulate China's electricity consumption from 2001 to 2013 and to forecast it in 2014 using real data; the results indicate that the novel model has better simulative and predictive accuracy than the GM (1, 1) and DGM (1, 1) models.