A novel structural adaptive discrete grey prediction model and its application in forecasting renewable energy generation

The rapidly growing renewable energy generation instigates stochastic volatility of electricity supply that may compromise the power grid's stability and increase the grid imbalance cost. Therefore, accurate mid-to-long term renewable energy generation forecasting is of great significance for integrating renewable energy systems with smart grid and energy strategic planning. For this purpose, a new structural adaptive discrete grey prediction model is proposed. Overall, the proposed model possesses three main contributions. Firstly, the introduction of nonlinear term and periodic term strengthens the ability of the traditional DGM (1,1) model to capture the nonlinear and linear development trend of time series and improves the adaptability of the grey prediction model to arbitrary periodic time series. Secondly, the emerging coefficients are determined by the particle swarm optimization algorithm and hold-out cross-validation method, and the adaptive selection of the model structure is realized. From the perspective of expert system, it reduces the need for modeling knowledge. Thirdly, the consistency of stretching, unbiasedness, and compatibility with other grey models are discussed, which further verified the feasibility and practicability of the proposed model. Besides, the performance of the proposed model is compared with those of a series of grey prediction models and non-grey prediction methods to verify the feasibility and superiority of this new approach by three real cases. The results indicate that the proposed model benefits from its adaptive structure and produces reliable predictions.

Automated summary

The proposed structural adaptive discrete grey prediction model introduces nonlinear and periodic terms to capture nonlinear and linear trends in time series, improving adaptability. The model's coefficients are determined using particle swarm optimization and hold-out cross-validation, enabling adaptive selection of the model structure. Consideration of consistency, unbiasedness, and compatibility with other grey models further validates the feasibility and practicability of the model.