A novel conformable fractional non-homogeneous grey Bernoulli model based on Salp Swarm Algorithm and its application

Abstract

The nonlinear grey Bernoulli model uses the first-order accumulated generating operation (1-AGO) to accumulate the sequence. However, 1-AGO violates the principle of new information priority, and the prediction accuracy needs to be improved. For this purpose, this paper proposed a conformable fractional non-homogeneous grey Bernoulli model by combing the conformable fractional accumulation operator and the non-homogeneous grey Bernoulli model to forecast biofuels production. The article discussed the properties of the proposed model and proved that the novel model is a more general extension of other grey models. The Salp Swarm Algorithm was used to optimize the nonlinear multi-objective parameters and improve prediction accuracy. Furthermore, three examples were used to verify the proposed model’s performance capability, which verified that the competitiveness of the new model is a better grey model. Then the novel model could be used to predict biofuels production in the US and China. Biofuels production from 2009 to 2016 was used to construct models, and the data from 2017 to 2020 was used to verify the model’s accuracy. The results indicated that the proposed model has higher accuracy in terms of prediction. Finally, the novel model could predict biofuels production of the above countries from 2021 to 2025. The forecast results suggested that biofuels production in the US will change moderately. And in China, biofuels production will show an increasing trend in the next few years.