郑州大学学报（工学版）2026，Vol.47Issue(5)：26-34,9.DOI:10.13705/j.issn.1671-6833.2026.02.005
基于风场空间与气象融合的风电集群短期功率预测
Short-term Power Prediction of Wind Power Clusters Based on Wind Field Spatial and Meteorological Fusion
摘要
Abstract
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.关键词
风电集群功率预测/时空图卷积神经网络/多头自注意力机制/图数据结构/深度学习Key words
wind power cluster forecasting/spatio-temporal graph convolutional neural network/multi-head self-at-tention mechanism/graph data structure/deep learning分类
信息技术与安全科学引用本文复制引用
魏臻珠,刘明宇,王昱龙,周燕,蒋建东..基于风场空间与气象融合的风电集群短期功率预测[J].郑州大学学报（工学版）,2026,47(5):26-34,9.基金项目
河南省高等学校重点科研项目(24A470009) （24A470009）
