A novel structure-adaptive intelligent grey forecasting model with full-order time power terms and its application

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.

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