Accurate prediction of photovoltaic and wind power generation is essential for maintaining stable energy supply in integrated energy systems. However, the strong stochasticity and complex fluctuations in these energy sources pose significant challenges to forecasting. Traditional methods often fail to handle the non-stationary characteristics of the generation series effectively. To address this, we propose a novel hybrid prediction framework that integrates variational mode decomposition, the Pearson correlation coefficient, and a benchmark prediction model. Experimental results demonstrate the outstanding performance of the proposed method, achieving an R² value exceeding 0.995 along with minimal MAE and RMSE. The approach effectively mitigates hysteresis issues during prediction. Furthermore, the model shows strong adaptability; even when substituting different benchmark models, it maintains an R² above 0.99. When applied in a building heating system, accurate predictions help reduce indoor temperature fluctuations, enhance energy supply stability, and lower energy consumption, highlighting its practical value for improving energy efficiency and operational reliability.