Environmental policy-driven electricity consumption prediction: A novel buffer-corrected Hausdorff fractional grey...

In the global quest for carbon neutrality, electricity is a critical sector for carbon reduction, for electricity consumption and carbon emissions are closely associated. Electricity consumption forecasts are divided into short-term and long-term, but previous studies have focused more on the former, while the latter is the foundation of power system planning and directly relates to urban development. To address the issue, this research proposed an innovative hybrid Hausdorff fractional grey model (HfGM) for electricity consumption prediction, weakening buffer operator (WBo) was incorporated to minimize interference of external shocks to original data, the optimal core parameters of HfGM were searched by a newly developed multi-objective enhanced version of slime mould algorithm in two stages, achieving Pareto optimal solutions theoretically. Experiments results demonstrated the proposed model outperformed comparative models, leading to its application in predicting electricity consumption trends in China during the 15th Five-Year Plan period and assessing the corresponding environmental impacts. Beyond advancing grey model theory, the research provides essential policy recommendations for integrating environmental management into electricity demand planning, assisting policymakers in addressing demand growth and supporting carbon reduction goals.