Forecasting China’s total renewable energy capacity using a novel dynamic fractional order discrete grey model

Abstract

Accurately forecasting renewable energy capacity is crucial for informing national energy policies and promoting sustainable socio-economic growth. This study proposes a novel dynamic fractional-order discrete grey model (DFDGM (1,1)) for forecasting China's total renewable energy capacity. The model constructs a dynamic fractional time delay polynomial, which enhances the adaptability of the model to different sample sizes. The time response function of model is directly derived from the discrete equation, preventing errors caused by the transition from discreteness to continuity. Furthermore, the proposed model represents the unified form of six grey models, effectively expanding the model's application domain and scope. The method introduces a double error approach to facilitate optimal parameter selection, successfully addressing overfitting and randomness challenges in parameter selection. Empirical studies show that the proposed model outperforms eleven other methods, with fitting and forecasting errors of 0.39% and 1.13%, respectively. Finally, the proposed model forecasts the future development trend of China’s total renewable energy capacity. In general, this research not only improves the precision of predictions but also progresses the dynamic theory in prediction studies.