Highly reliable wind power prediction is feasible and promising for smart grids integrated with large amounts of wind power. However

the strong fluctuation features of wind power make wind power less predictable. This paper proposes a novel wind power prediction approach

incorporating wind power ex-ante and ex-post decomposition and correction. Firstly

the initial wind power during the wind power decomposition stage is decomposed into trend

fluctuation

and residual data

respectively

and the corresponding preliminary prediction models are developed

respectively. Secondly

in the error correction stage

the errors produced by the preliminary prediction model are corrected by persistence methods to compensate for final prediction errors. Moreover

the proposed model's comprehensive deterministic and probabilistic analysis is investigated in depth. Finally

the outcomes of numerical simulations demonstrate that the proposed approach can achieve good performance since it can reduce wind power forecast errors compared to other established deterministic models and uncertainty models.