Mixed-frequency grey prediction model with fractional lags for electricity demand and estimation of coal power phase-out scale

Accurate medium-term and long-term electricity demand forecasting is essential for a structured phase-out of coal power plants and the advancement of a low-carbon power sector. To this end, a novel fractional lag-based mixed-frequency discrete grey model (FMDGM(1,N)) that integrates high-frequency data through the Nakagami function is proposed, enabling comprehensive utilization of multi-frequency features and addressing the limitations of traditional single-frequency electricity demand forecasting frameworks. Unlike conventional mixed-frequency grey prediction models relying on integer lag parameters, the proposed model introduces mathematical functions to capture developmental trends between adjacent time points, successfully extending integer lag parameters into the fractional domain. This innovation enhances model performance and allows for more accurate representation of lag effects among electricity demand drivers. Experimental results demonstrate the model's superior performance and robustness across various data scenarios, significantly outperforming other grey prediction models, regression models, and neural network models in electricity demand forecasting. The forecast indicates that China's electricity demand will reach 11816 TWh by 2030, with a coal power capacity of 1238 GW. This study provides a robust tool for energy planning and low-carbon transition.