Short-term Power Prediction of Wind Power Clusters Based on Wind Field Spatial and Meteorological Fusion
[1]WEI Zhenzhu,LIU Mingyu,WANG Yulong,et al.Short-term Power Prediction of Wind Power Clusters Based on Wind Field Spatial and Meteorological Fusion[J].Journal of Zhengzhou University (Engineering Science),2027,48(XX):1-9.[doi:10.13705/j.issn.1671-6833.2026.02.005]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
48
Number of periods:
2027 XX
Page number:
1-9
Column:
Public date:
2027-12-10
- Title:
- Short-term Power Prediction of Wind Power Clusters Based on Wind Field Spatial and Meteorological Fusion
- Keywords:
- wind power cluster forecasting; spatio-temporal graph convolutional neural network; multi-head self-attention mechanism; graph data structure; deep learning
- CLC:
- TM614；TP183
- Abstract:
- Given that traditional wind power cluster prediction methods fail to effectively account for the spatial meteorological correlations among stations and struggle to efficiently deduce the overall cluster power based on single-station predictions, this paper proposes a multi-dimensional spatiotemporal information fusion framework for stations based on an attention-based spatiotemporal embedding mechanism. This framework aims to fully exploit the complex spatiotemporally coupled characteristics embedded within discrete Numerical Weather Prediction (NWP) heterogeneous meteorological information. Firstly, a Multi-Head Self-Attention mechanism is employed to directly fuse spatial features, enhancing the model’s ability to capture spatial power correlations across multiple stations. Secondly, cluster location information is deconstructed using the Maximal Information Coefficient (MIC) to construct a non-Euclidean graph data structure reflecting meteorological correlations. This is combined with a Spatial-Temporal Attention mechanism to achieve cross-fusion of spatiotemporal features between stations and their neighborhoods, dynamically adjusting the influence weights among stations to capture spatiotemporal dynamic dependencies. Furthermore, an encoder-decoder architecture integrates spatial and spatiotemporal features into a unified semantic space to capture temporal continuity within sequences. Finally, the proposed model is verified based on the actual wind farm operation data of a certain region in Northwest China.Experimental results show that when the proposed method is compared with the other 6 prediction models, the reduction amplitudes of theERMSE error index are 15.32, 13.55, 17.80, 16.13, 6.90, and 2.64 percentage points respectively. The decrease amplitudes of theEMAE error index are 20.56, 16.15, 18.67, 12.59, 8.38, and 3.02 percentage points respectively, which effectively verify its advancement and adaptability.
- References:
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Last Update:
2026-05-26
