Using dynamically adaptive fractional grey model to forecast multi scale energy consumption

Purpose Accurate and reliable energy forecasting is fundamental to strategic decision making for the global transition toward decarbonization and digitalization. This paper proposes a novel nonlinear time-varying fractional grey multivariable model to address the limitations of conventional forecasting methods. Design/methodology/approach The proposed model offers a novel methodological improvement by integrating time-varying coefficients for dynamic parameterization, logarithmic adjustment terms for enhanced nonlinear modeling and a particle swarm algorithm for systematic optimization. Findings Empirical validation across municipal, provincial, and national case studies consistently confirms the superior predictive performance of the proposed model. Comparative analyses with benchmark models show that our model achieves the lowest prediction errors across all evaluation metrics. This outperformance remains robust in both training and testing phases. Practical implications The proposed approach provides a robust methodological framework for energy consumption forecasting, offering substantial improvements in accuracy and reliability for supporting energy strategy formulation and sustainable development planning. Originality/value Its originality stems from the integration of dynamic coefficients and logarithmic adjustment terms. This integrated framework effectively addresses the limitations of static parameters and fixed structures in conventional grey models.