Wind power is the most promising renewable energy source after hydropower because of its mature technology and low price, and has great potential for carbon emission reduction. Long-term forecasts of its power generation can help power companies to develop operational plans, grid configuration and power dispatch, and can also provide a basis for the government to formulate energy and environmental policies. However, due to the characteristics of China's monsoon climate and wind power industry development, wind power generation data are characterized by nonlinear cycles and small data volume, which makes accurate prediction more difficult. To this end, this paper develops a new prediction model and applies it to the long-term prediction of wind power generation in China, and proposes some targeted policy recommendations based on the prediction results to promote the development of China's wind power industry.

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.

Wind power is the most promising renewable energy source after hydropower because of its mature technology and low price, and has great potential for carbon emission reduction. However, wind power generation has the characteristics of intermittency and randomness. The grid connection of wind power will have a temporary impact on the national grid, which is not conducive to grid regulation and affects the regional production activity plan. Secondly, as an important clean energy in China, wind power generation can accurately predict its development trend, which is also conducive to China's energy planning and energy structure adjustment. For this reason, it is of great importance to accurately forecast wind power generation. Due to the complex nonlinear characteristics of wind power generation data in China, such as cyclical seasonality and trend, traditional models cannot accurately portray its characteristics and the forecasting effect is poor. For this reason, based on the idea of divide and conquer, this paper performs empirical modal decomposition (EMD) of Chinese wind power generation data, which decomposes the complex time series into several simple time series. Experiments show that for data with periodic seasonality and trend, the EMD can always be decomposed into two terms, where the residual is the trend term and the intrinsic mode (IMF) is the seasonal cycle fluctuation term. Due to the quasi-exponential characteristics of the trend term, a DGM model is established based on the trend term data, while a multiplicative seasonal model is established to derive the seasonal factor for the decomposed data, and the seasonal factor is used to correct the predicted value of DGM, and then the wind power generation is predicted. The newly established combined EMD-DGM model outperforms the traditional prediction model in both the test and training sets.