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.

Coal-fired power plants often face high thermal energy consumption for district heating and inadequate cascade utilization of waste heat. To address these challenges, this study proposes an integrated heat recovery system that couples a low-temperature economizer with a lithium bromide (LiBr) absorption heat pump. A 350 MW supercritical unit serves as the case study, where a cascaded heating system is configured, using turbine extraction steam as the driving heat source to recover waste heat from both pre-desulfurization flue gas and condenser circulating water. A steady-state simulation model is developed to evaluate the system performance. The results show that the system recovers 8.026 MW of waste heat from the flue gas, achieves a heat pump coefficient of performance (COP) of 1.718, and reduces the heating extraction steam consumption by 32.5% compared with conventional approaches. These findings demonstrate a viable strategy for energy-saving retrofits and enhanced operational flexibility of existing coal-fired units, particularly in regions with substantial heating demands.