A novel dynamic fractional-order discrete grey power model for forecasting China's total solar energy capacity

Abstract

-Precise prediction of the total solar energy capacity is pivotal for the progress of the nation's solar energy industry, the optimization of energy structure and the sustainable development of energy systems. This study proposes a novel dynamic fractional order discrete grey power model (DFDGPM(1,1)) for predicting China's total solar energy capacity. The model introduces a power exponent to capture the nonlinear characteristics among system behavior variables. Additionally, it incorporates a fractional-order accumulation operator and a dynamic time-delay function, which not only describe the time-delay effect between China's economic development and solar energy growth but also enhance the model's adaptability to different samples. The model demonstrates strong compatibility and can degenerate into 10 existing grey models. Empirical research shows that the model's fitting error is close to 0 %, with a prediction error of only 1.07 %, which is significantly better than 11 other methods. The forecast findings indicate that China's total solar energy capacity will experience an annual growth rate of 29.41 % from 2022 to 2030. This method promotes the development of dynamic forecasting technology and provides the necessary technical and data support for renewable energy field.