A new information priority grey prediction model for forecasting wind electricity generation with targeted regional hierarchy

Abstract

Amidst the global transition to renewable energy electricity generation, this study addresses the need for improved wind electricity generation forecasting, which is pivotal in optimizing energy strategies and meeting sustainability targets. The research introduces an innovative adaptation of the grey prediction methods, named UNAGO-GM (1, 1), which is enhanced by the unified new-information priority accumulating generation operator (UNAGO) that emphasizes more recent data. This advanced model significantly improves the model's adaptability and accuracy in forecasting wind electricity generation within the regional hierarchy. To demonstrate the proposed model’s superior forecasting efficacy, the comparative experiments are conducted in the hierarchical forecasting fashion across national, regional, and global levels by contrasting UNAGO-GM (1, 1) with prevailing benchmarks like ARIMA and LSTM. The results indicate UNAGO-GM (1, 1)’ s highest forecasting accuracy and universality, with 54.07% and 156.48% improvement rates concerning MAPEs and RMSEs across all benchmarks and forecasting levels. Furthermore, UNAGO-GM (1, 1)’s output stability is validated through the robustness tests applying Monte Carlo simulations for the intelligent algorithm stability and matrix perturbation analyses. In conclusion, the novel UNAGO-GM (1, 1) model represents a significant breakthrough in renewable energy forecasting, providing precise and adaptable predictions essential for strategic planning and policy formulation in the transition towards sustainable energy sources.