Damping accumulative NDAGM(1,N, α ) power model and its applications
Grey Systems: Theory and Application
ISSN: 2043-9377
Article publication date: 10 May 2024
Issue publication date: 24 September 2024
Abstract
Purpose
In this essay, a new NDAGM(1,N,α) power model is recommended to resolve the hassle of the distinction between old and new information, and the complicated nonlinear traits between sequences in real behavior systems.
Design/methodology/approach
Firstly, the correlation aspect sequence is screened via a grey integrated correlation degree, and the damped cumulative generating operator and power index are introduced to define the new model. Then the non-structural parameters are optimized through the genetic algorithm. Finally, the pattern is utilized for the prediction of China’s natural gas consumption, and in contrast with other models.
Findings
By altering the unknown parameters of the model, theoretical deduction has been carried out on the newly constructed model. It has been discovered that the new model can be interchanged with the traditional grey model, indicating that the model proposed in this article possesses strong compatibility. In the case study, the NDAGM(1,N,α) power model demonstrates superior integrated performance compared to the benchmark models, which indirectly reflects the model’s heightened sensitivity to disparities between new and old information, as well as its ability to handle complex linear issues.
Practical implications
This paper provides a scientifically valid forecast model for predicting natural gas consumption. The forecast results can offer a theoretical foundation for the formulation of national strategies and related policies regarding natural gas import and export.
Originality/value
The primary contribution of this article is the proposition of a grey multivariate prediction model, which accommodates both new and historical information and is applicable to complex nonlinear scenarios. In addition, the predictive performance of the model has been enhanced by employing a genetic algorithm to search for the optimal power exponent.
Keywords
Acknowledgements
This work was supported by the research on the mechanism of digital rural development to empower the growth of agricultural carbon productivity under the “dual carbon goals” of the Henan Provincial Soft Science Research Program (project number: 242400410173) and the Henan Provincial Soft Science Research Program (project number: 222400410391).
Citation
Li, Y., Wang, C. and Liu, J. (2024), "Damping accumulative NDAGM(1,N,
Publisher
:Emerald Publishing Limited
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