# Multistream Graph Attention Networks for Wind Speed Forecasting

arXiv: https://arxiv.org/abs/2108.07063 · HTML: https://mlsanity.com/paper/2108.07063/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 16 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Large-Scale Learnable Graph Convolutional Networks](https://mlsanity.com/paper/1808.03965/) (2018)
- [SmaAt-UNet: Precipitation Nowcasting using a Small Attention-UNet Architecture](https://mlsanity.com/paper/2007.04417/) (2020)
- [DeepDownscale: a Deep Learning Strategy for High-Resolution Weather Forecast](https://mlsanity.com/paper/1808.05264/) (2018)
- [Broad-UNet: Multi-scale feature learning for nowcasting tasks](https://mlsanity.com/paper/2102.06442/) (2021)
- [Deep Graph Convolutional Networks for Wind Speed Prediction](https://mlsanity.com/paper/2101.10041/) (2021)
- [Wind speed prediction using multidimensional convolutional neural networks](https://mlsanity.com/paper/2007.12567/) (2020)
- [Symbolic regression for scientific discovery: an application to wind speed forecasting](https://mlsanity.com/paper/2102.10570/) (2021)
- [Deep coastal sea elements forecasting using U-Net based models](https://mlsanity.com/paper/2011.03303/) (2020)
- [TENT: Tensorized Encoder Transformer for Temperature Forecasting](https://mlsanity.com/paper/2106.14742/) (2021)
- [Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks](https://mlsanity.com/paper/2009.11239/) (2020)

## Built on by (most cited first, 12 of 12)

- [AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks](https://mlsanity.com/paper/2202.04996/) (2022)
- [Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar](https://mlsanity.com/paper/2302.00817/) (2023)
- [Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks](https://mlsanity.com/paper/2306.13690/) (2023)
- [Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting](https://mlsanity.com/paper/2401.07958/) (2024)
- [SAR-UNet: Small Attention Residual UNet for Explainable Nowcasting Tasks](https://mlsanity.com/paper/2303.06663/) (2023)
- [WF-UNet: Weather Fusion UNet for Precipitation Nowcasting](https://mlsanity.com/paper/2302.04102/) (2023)
- [GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations](https://mlsanity.com/paper/2204.13744/) (2022)
- [Interpolation of mountain weather forecasts by machine learning](https://mlsanity.com/paper/2308.13983/) (2023)
- [Integrating Weather Station Data and Radar for Precipitation Nowcasting: SmaAt-fUsion and SmaAt-Krige-GNet](https://mlsanity.com/paper/2502.16116/) (2025)
- [SmaAT-QMix-UNet: A Parameter-Efficient Vector-Quantized UNet for Precipitation Nowcasting](https://mlsanity.com/paper/2603.21879/) (2026)
- [A Diffusion-Contrastive Graph Neural Network with Virtual Nodes for Wind Nowcasting in Unobserved Regions](https://mlsanity.com/paper/2604.10328/) (2026)
- [Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall](https://mlsanity.com/paper/2606.09959/) (2026)

## 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.