A self-adaptive intelligence grey predictive model with alterable structure and its application. (April 2016)

• Record Type: Journal Article
• Authors: Zeng, Bo; Meng, Wei; Tong, Mingyu
• Is Part Of: Engineering applications of artificial intelligence. Volume 50(2016:Feb.)
• Page Start: 236
• Page End: 244
• Publication Date: 2016-04
• Subjects: Intelligence grey prediction model -- Self-adaptive -- Alterable structure -- Prediction of China׳s electricity consumption; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.12.011
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.