A novel grey model with conformable fractional opposite-direction accumulation and its application

Abstract

To better reflect the principle of new information priority, this paper proposes a new conformable fractional opposite-direction accumulation operator and constructs a new grey prediction model, the CFGNOM (1,1) model. Three cases are used to verify the effectiveness of the CFGNOM (1,1) model, and a grey modeling prediction idea for emergencies is proposed. The CFGNOM (1,1) model is used to predict the per capita primary energy consumption of South and Central America over the next three years. The following conclusions can be drawn. First, the new conformable fractional opposite-direction accumulation method is effective and reasonable and can make full use of the information contained in the latest data to form new sequences. Second, compared with other existing fractional grey models and the BP and SVM models, the CFGNOM (1,1) model has higher prediction accuracy and can make better use of new information for modeling. Third, the application idea of a grey model for emergencies is accurate and can also be applied to the future economic and social prediction affected by the pandemic situation. Fourth, the prediction results show that the per capita energy consumption of South and Central America in 2021 is 49.9–52.7 gigajoules, the per capita energy consumption in 2022 is 48.4–51.8 gigajoules, and the per capita energy consumption in 2023 is 46.8–50.1 gigajoules.