Environmental policy-driven electricity consumption prediction: A novel buffer-corrected Hausdorff fractional grey model informed by two-stage enhanced multi-objective optimization.

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.

Research of a combination system based on fuzzy sets and multi-objective marine predator algorithm for point and interval prediction of wind speed.

Short-term wind speed forecasting is fundamental to improving the stability of power grid operation and enhancing its transmission efficiency; thus, it has long been a research hotspot. Nonetheless, quantities of literature in this field only used the single prediction model and overemphasized deterministic prediction, which resulted in deficient forecasting performance. To address these issues, a novel point and interval combination prediction system was developed in this paper.