Advancing Grey Modeling with a Novel Time-Varying Approach for Predicting Solar Energy Generation in the United States

This paper proposes a novel time-varying discrete grey model (TVDGM(1,1)) to precisely forecast solar energy generation in the United States. First, the model utilizes the anti-forgetting curve as the weight function for the accumulation of the original sequence, which effectively ensures the prioritization of new information within the model. Second, the time response function of the model is derived through mathematical induction, which effectively addresses the common jump errors encountered when transitioning from difference equations to differential equations in traditional grey models. Research shows that compared to seven other methods, this model achieves better predictive performance, with an error rate of only 2.95%. Finally, this method is applied to forecast future solar energy generation in the United States, and the results indicate an average annual growth rate of 23.67% from 2024 to 2030. This study advances grey modeling techniques using a novel time-varying approach while providing critical technical and data support for energy planning.