Application of the novel fractional grey model FAGMO(1,1,k) to predict China's nuclear energy consumption

TL;DR

This paper introduces a new fractional grey model FAGMO(1,1,k) for predicting China's nuclear energy consumption, demonstrating its superior accuracy over existing models through detailed analysis and validation.

Contribution

The paper presents a novel fractional grey model FAGMO(1,1,k) with a stochastic validation scheme, enhancing prediction accuracy for nuclear energy consumption forecasts.

Findings

FAGMO(1,1,k) outperforms traditional grey models in accuracy.

The model effectively captures the dynamics of China's nuclear energy consumption.

Validation confirms the robustness of the optimal parameters.

Abstract

At present, the energy structure of China is shifting towards cleaner and lower amounts of carbon fuel, driven by environmental needs and technological advances. Nuclear energy, which is one of the major low-carbon resources, plays a key role in China's clean energy development. To formulate appropriate energy policies, it is necessary to conduct reliable forecasts. This paper discusses the nuclear energy consumption of China by means of a novel fractional grey model FAGMO(1,1,k). The fractional accumulated generating matrix is introduced to analyse the fractional grey model properties. Thereafter, the modelling procedures of the FAGMO(1,1,k) are presented in detail, along with the transforms of its optimal parameters. A stochastic testing scheme is provided to validate the accuracy and properties of the optimal parameters of the FAGMO(1,1,k). Finally, this model is used to forecast China's nuclear energy consumption and the results demonstrate that the FAGMO(1,1,k) model provides accurate prediction, outperforming other grey models.