Forecasting Renewable Energy Consumption Using a Novel Fractional Grey Reverse Accumulation Model - Yipeng Zhang; Huiping Wang (Systems)
The accumulation operation is the most fundamental method for processing data in grey models, playing a decisive role in the accuracy of model predictions. However, the traditional forward accumulation method does not adhere to the principle of prioritizing new information. Therefore, we propose a novel fractional reverse accumulation, which increases the accumulation coefficient for new data to fully utilize the new information carried by the latest data. This led to the development of a novel grey model, termed the FGRM(1,1). This model was validated using renewable energy consumption data from France, Spain, the UK, and Europe, and the results demonstrated that the FGRM(1,1) outperformed other models in terms of simulation error, prediction error, and comprehensive error. The predictions indicated significant growth in renewable energy consumption for France and Spain, moderate growth for the UK, and robust growth for Europe overall. These findings highlight the effectiveness of the proposed model in utilizing new information and provide insights into energy transition and emission reduction potential in Europe.