A novel structure adaptive discrete grey Riccati model and its application in energy production and consumption

To address climate change, the international community has broadly supported clean energy. In recent years, energy structure has consistently transitioned toward low-carbon and environmentally friendly solutions, and it has now entered a critical phase. Therefore, accurately predicting future energy development trends is of significant importance for energy structure transformation. This paper proposes a novel structurally adaptive discrete grey Riccati model, called SADGRM(1,1), for energy data prediction. The Hausdorff fractional-order cumulative generation operation is utilized to implement the principle of prioritizing new information. By integrating a nonlinear dynamic structural component based on the grey Riccati model, the model can adapt its structure to the data characteristics, thereby enhancing its flexibility and adaptability. The differential evolution algorithm (DE) was chosen to optimize the model parameters after evaluating the performance of several algorithms. To address reliability issues arising from the optimization algorithm, Monte Carlo simulations and probability density analyses were conducted to validate the robustness of the model. The results indicate that the model is stable and reliable. Three actual cases of hydropower generation, urban natural gas supply and average daily natural gas consumption are predicted. The results indicate that the novel model is an effective instrument for energy forecasting and offers considerable reference value for decision-making in energy development.