Seasonal electricity consumption forecasting: an approach with novel weakening buffer operator and fractional order accumulation grey model

Purpose Accurate prediction of seasonal power consumption trends with impact disturbances provides a scientific basis for the flexible balance of the long timescale power system. Consequently, it fosters reasonable scheduling plans, ensuring the safety of the system and improving the economic dispatching efficiency of the power system. Design/methodology/approach First, a new seasonal grey buffer operator in the longitudinal and transverse dimensional perspectives is designed. Then, a new seasonal grey modeling approach that integrates the new operator, full real domain fractional order accumulation generation technique, grey prediction modeling tool and fruit fly optimization algorithm is proposed. Moreover, the rationality, scientificity and superiority of the new approach are verified by designing 24 seasonal electricity consumption forecasting approaches, incorporating case study and amalgamating qualitative and quantitative research. Findings Compared with other comparative models, the new approach has superior mean absolute percentage error and mean absolute error. Furthermore, the research results show that the new method provides a scientific and effective mathematical method for solving the seasonal trend power consumption forecasting modeling with impact disturbance. Originality/value Considering the development trend of longitudinal and transverse dimensions of seasonal data with impact disturbance and the differences in each stage, a new grey buffer operator is constructed, and a new seasonal grey modeling approach with multi-method fusion is proposed to solve the seasonal power consumption forecasting problem. Highlights The highlights of the paper are as follows: A new seasonal grey buffer operator is constructed. The impact of shock perturbations on seasonal data trends is effectively mitigated. A novel seasonal grey forecasting approach with multi-method fusion is proposed. Seasonal electricity consumption is successfully predicted by the novel approach. The way to adjust China's power system flexibility in the future is analyzed.