A Novel Structure Adaptive Discrete Grey Bernoulli Model and Its Application in Renewable Energy Power Generation Prediction

Currently, the renewable energy power generation industry has entered a new stage, and accurate renewable energy power generation prediction is of great significance for the strategic planning of energy systems. However, renewable energy power generation data is characterized by nonlinearity and poor information, which brings challenges to accurately predict its development trend. Thus, this paper proposes a novel discrete grey Bernoulli model based on the spiral structure accumulated generating operator to deal with this problem. The spiral structure accumulated generating operator is introduced into the grey model to realize the effective utilization of renewable energy data information. Meanwhile, with the introduction of time delay structure, periodic structure and Bernoulli structure, the novel model can effectively characterize the nonlinearity, volatility, and time lag information between economic growth and energy development of renewable energy data. In addition, using the Differential Evolution optimization (DE) algorithm for nonlinear parameter optimization can effectively improve the stability and accuracy of the model, and also makes the model have the ability of structural self-adaptation. Finally, the new model was used to predict the bioenergy and wind power generation data. Based on comparative experiments and grey correlation analysis, the predictive performance of the novel model is verified, and the prediction results are highly correlated with those of authoritative organization. The experimental results show that the novel model is an effective predictive tool for renewable energy generation, which is an important reference value for energy development decision-making.