基于风场空间与气象融合的风电集群短期功率预测OA

Given that traditional wind power cluster prediction methods failed to effectively account for the spatial meteorological correlations among stations and struggle to efficiently deduce the overall cluster power based on sin-gle-station predictions,in this study a multi-dimensional spatiotemporal information fusion framework was proposed for stations based on an attention-based spatiotemporal embedding mechanism.This framework aimed to fully ex-ploit the complex spatiotemporally coupled characteristics embedded within discrete numerical weather prediction(NWP)heterogeneous meteorological information.Firstly,a multi-head self-attention mechanism was employed to directly fuse spatial features,enhancing the model's ability to capture spatial power correlations across multiple sta-tions.Secondly,cluster location information was deconstructed using the maximal information coefficient to con-struct a non-Euclidean graph data structure reflecting meteorological correlations.This was combined with a spatial-temporal attention mechanism to achieve cross-fusion of spatiotemporal features between stations and their neighbor-hoods,dynamically adjusting the influence weights among stations to capture spatiotemporal dynamic dependencies.Furthermore,an encoder-decoder architecture was used to integrate spatial and spatiotemporal features into a uni-fied semantic space to capture temporal continuity within sequences.Finally,the proposed model was verified based on the actual wind farm operation data of a certain region in Northwest China.Experimental results showed that the two error evaluation indexes of the proposed method RMSE and MAE were significantly lower than those of the other six prediction models,which effectively verified its advancement and adaptability.