A novel structure-adaptive intelligent grey forecasting model with full-order time power terms and its application. (June 2018)

• Record Type: Journal Article
• Authors: Li, Shoujun; Ma, Xiaoping; Yang, Chunyu
• Is Part Of: Computers & industrial engineering. Volume 120(2018)
• Page Start: 53
• Page End: 67
• Publication Date: 2018-06
• Subjects: Grey forecasting model -- Full-order time power -- Connotation method -- Whitenization method -- Self-adaptive -- Alterable structure; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.04.016
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: The paper proposed a novel adaptive intelligent grey forecasting model (FOTP-GM(1, 1)), which has alterable structure that can be changed automatically according to actual application. It shows high forecasting precision and can simulate accurately three kinds of raw data sequences: one is homogeneous exponential sequence; another is non-homogeneous exponential sequence with constant, velocity and acceleration terms; the third is regressive linear sequence. The practicality and effectiveness was verified by comparison with other first-order models with one variable such as GM(1, 1), NGM(1, 1, k), DGM(1, 1), NDGM, SAIGM, and GM(1, 1, t α ). Then the proposed model has been employed to predict China's total production volume of hydropower, nuclear power and wind power successfully. Highlights: A novel grey prediction model with full order time power terms is proposed. Alterable structure of FOTP-GM(1, 1) model make it suit more kinds of sequences. Quasi-exponential sequence with velocity, acceleration terms is predicted precisely. FOTP-GM(1, 1) has higher accuracy than existing GM(1, 1) derived univariate models. Total production volume of hydropower, nuclear power and wind power is predicted. Abstract: To solve the problem that traditional grey models cannot simulate accurately any given non-homogeneous exponential sequence with velocity and acceleration terms, a novel grey forecasting model with full-order time power terms (abbreviated as FOTP-GM(1, 1)) is proposed. Firstly, two forms of sequence functions of the restored values are brought forward based respectively on whitenization method and connotation method. Then, Four forecasting properties are put forward to demonstrate that FOTP-GM(1, 1) is a more general model with higher accuracy and adaptability than traditional models. Then a visual comparison method is introduced to facilitate selection of a more reasonable structure from all possible structures of the FOTP-GM(1, 1) model. To verify its feasibility and efficiency, performance comparisons and suitability analyses are given by 2 examples. The first example shows that the simulative accuracy given by connotation method is higher than that by whitenization method, and it confirms that NDGM(1, 1) and SAIGM(1, 1) models are all special cases of the FOTP-GM(1, 1) model. Then by quantitative analysis and visual comparison, the second example shows that FOTP-GM(1, 1) model has better adaptability and broader universality. In the last, FOTP-GM(1, 1) model is employed to forecast the potential total production volume of hydropower, nuclear power and wind power from 2017 to 2021 in China. Thus practicality of the proposed model is tested.