A novel structure adaptive new information priority discrete grey prediction model and its application in renewable energy generation forecasting

Renewable energy has made a significant contribution to global power generation. Therefore, accurate mid-to -long term renewable energy generation forecasting is becoming more and more important for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, a novel structure adaptive new information priority discrete grey prediction model is established, and the disturbance analysis shows that it is suitable for small sample modeling. Firstly, the fractional dynamic weighted coefficient is introduced to define the accumulative generating operator satisfying the new information priority principle, which can realize the effective utilization of system information with insufficient information and accurately extract the development mode of system sequence from sparse samples. In terms of model structure, the nonlinear term and periodic fluctuation term are introduced to simulate the nonlinear and periodic fluctuation trend of renewable energy generation data, which improves the adaptability of the grey prediction model to the nonlinear and fluctuating time series. By designing the comparative experiment of optimization algorithm, Grey Wolf Optimizer (GWO) is selected to optimize the structural parameters of the model, giving the proposed model higher flexibility and stronger adaptability. In order to illustrate the performance of the model, the new model is compared with statistical econometrics technology, artificial neural network (ANN) and various existing grey models in three cases: World biofuel power generation, China's renewable energy power generation and small-scale solar photovoltaic power generation in the United States. The experimental results show that the proposed model is superior to other competitive models in terms of fitting accuracy and prediction accuracy. In addition, Monte-Carlo Simulation and probability density analysis further show that the proposed model can provide the best prediction effect and has high robustness.