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),2026,47(5):26-34.[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:
47
Number of periods:
2026 Issue 5
Page number:
26-34
Column:
Public date:
2026-09-09
- 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:
- TM614TP183
- 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 single‑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 exploit 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 stations. Secondly, cluster location information was deconstructed using the maximal information coefficient to construct 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 neighborhoods, 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 unified 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.
- References:
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Last Update:
2026-09-03
