An Improved Grey Prediction Model Integrating Periodic Decomposition and Aggregation for Renewable Energy Forecasting: Case Studies of Solar and Wind Power

Due to the prevalent “small data”, “seasonal”, and “periodicity” characteristics in China’s renewable energy power generation data, there are certain difficulties in long-term power generation prediction. For this reason, this paper uses the data preprocessing method of periodical aggregation to enhance the “quasi-exponentiality” characteristics of original data, eliminate “seasonality” and “periodicity”, use the DGM (1,1) model to predict aggregated data, and then use the periodical component factor to reduce the DGM (1,1)-predicted data. A seasonal discrete grey prediction model based on periodical aggregation is constructed. The proposed methodology employs streamlined data preprocessing coupled with conventional grey prediction modeling to enable the precise forecasting of nonlinear periodic sequences. This approach demonstrates an enhanced operational efficiency by mitigating the structural complexity and implementation barriers inherent in classical seasonal grey prediction frameworks. Validation experiments conducted on China’s photovoltaic (PV) and wind power generation datasets through comparative multi-model analysis confirm the model’s superior predictive accuracy, with performance metrics significantly outperforming benchmark methods across both training and validation cohorts.