mlsanityData to 13 Oct 2026

Multistream Graph Attention Networks for Wind Speed Forecasting

arXiv 2108.07063 ↗First posted 16 Aug 2021cs.LGcited by 12

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Abstract (arXiv)

Reliable and accurate wind speed prediction has significant impact in many industrial sectors such as economic, business and management among others. This paper presents a new model for wind speed prediction based on Graph Attention Networks (GAT). In particular, the proposed model extends GAT architecture by equipping it with a learnable adjacency matrix as well as incorporating a new attention mechanism with the aim of obtaining attention scores per weather variable. The output of the GAT based model is combined with the LSTM layer in order to exploit both the spatial and temporal characteristics of the multivariate multidimensional historical weather data. Real weather data collected from several cities in Denmark and Netherlands are used to conduct the experiments and evaluate the performance of the proposed model. We show that in comparison to previous architectures used for wind speed prediction, the proposed model is able to better learn the complex input-output relationships of the weather data. Furthermore, thanks to the learned attention weights, the model provides an additional insights on the most important weather variables and cities for the studied prediction task.

Key facts. Cited by 12 papers in the map (data through 13 Oct 2026). Builds on 16 of its 35 references that are in the map. First posted on arXiv 16 Aug 2021.

Builds on

16 of 35 references.

References · 16

  1. 2017Attention Is All You Needcited by 60,106
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,318
  3. 2017Graph Attention Networkscited by 8,888
  4. 2018How Powerful are Graph Neural Networks?cited by 4,373
  5. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,432
  6. 2016Geometric deep learning: going beyond Euclidean datacited by 1,045
  7. 2018Large-Scale Learnable Graph Convolutional Networkscited by 113
  8. 2020SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecturecited by 71
  9. 2018DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecastcited by 28
  10. 2021Broad-UNet: Multi-scale feature learning for nowcasting taskscited by 25
  11. 2021Deep Graph Convolutional Networks for Wind Speed Predictioncited by 19
  12. 2020Wind speed prediction using multidimensional convolutional neural networkscited by 17
4 more
  1. 2021Symbolic regression for scientific discovery: an application to wind speed forecastingcited by 16
  2. 2020Deep coastal sea elements forecasting using U-Net based modelscited by 14
  3. 2021TENT: Tensorized Encoder Transformer for Temperature Forecastingcited by 7
  4. 2020Deep multi-stations weather forecasting: explainable recurrent convolutional neural networkscited by 4

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 1; 2025: 1; 2026: 3.

12 papers.

  1. 2022AA-TransUNet: Attention Augmented TransUNet For Nowcasting Taskscited by 22
  2. 2023Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radarcited by 9
  3. 2023Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networkscited by 8
  4. 2024Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcastingcited by 7
  5. 2023SAR-UNet: Small Attention Residual UNet for Explainable Nowcasting Taskscited by 6
  6. 2023WF-UNet: Weather Fusion UNet for Precipitation Nowcastingcited by 2
  7. 2022GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equationscited by 1
  8. 2023Interpolation of mountain weather forecasts by machine learningcited by 1
  9. 2025Integrating Weather Station Data and Radar for Precipitation Nowcasting: SmaAt-fUsion and SmaAt-Krige-GNetcited by 0
  10. 2026SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcastingcited by 0
  11. 2026A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regionscited by 0
  12. 2026Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfallcited by 0