A novel fractional discrete grey model with variable weight buffer operator and its applications in renewable energy prediction.

With the continuous depletion of global fossil energy, optimizing the energy structure has become the focus of attention of all countries. With the support of policy and finance, renewable energy occupies an important position in the energy structure of the USA. Being able to predict the trend of renewable energy consumption in advance plays a vital role in economic development and policymaking. Aiming at the small and changeable annual data of renewable energy consumption in the USA, a fractional delay discrete model of variable weight buffer operator based on grey wolf optimizer is proposed in this paper. Firstly, the variable weight buffer operator method is used to preprocess the data, and then, a new model is constructed by using the discrete modeling method and the concept of fractional delay term. The parameter estimation and time response formula of the new model are deduced, and it is proved that the new model combined with the variable weight buffer operator satisfies the new information priority principle of the final modeling data. The grey wolf optimizer is used to optimize the order of the new model and the weight of the variable weight buffer operator. Based on the renewable energy consumption data of solar energy, total biomass energy and wind energy in the field of renewable energy, the grey prediction model is established. The results show that the model has better prediction accuracy, adaptability and stability than the other five models mentioned in this paper. According to the forecast results, the consumption of solar and wind energy in the USA will increase incrementally in the coming years, while the consumption of biomass will decrease year by year.