# Deep Graph Convolutional Networks for Wind Speed Prediction

arXiv: https://arxiv.org/abs/2101.10041 · HTML: https://mlsanity.com/paper/2101.10041/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 36 references that are in the map.
- First posted on arXiv 25 Jan 2021.

## Builds on (most cited first, 9 of 9)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [Image Generation from Scene Graphs](https://mlsanity.com/paper/1804.01622/) (2018)
- [Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks](https://mlsanity.com/paper/1903.01306/) (2019)
- [Deformable Shape Completion with Graph Convolutional Autoencoders](https://mlsanity.com/paper/1712.00268/) (2017)
- [Wind speed prediction using multidimensional convolutional neural networks](https://mlsanity.com/paper/2007.12567/) (2020)
- [Deep coastal sea elements forecasting using U-Net based models](https://mlsanity.com/paper/2011.03303/) (2020)

## Built on by (most cited first, 19 of 19)

- [Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey](https://mlsanity.com/paper/2303.14483/) (2023)
- [AA-TransUNet: Attention Augmented TransUNet For Nowcasting Tasks](https://mlsanity.com/paper/2202.04996/) (2022)
- [Symbolic regression for scientific discovery: an application to wind speed forecasting](https://mlsanity.com/paper/2102.10570/) (2021)
- [Multistream Graph Attention Networks for Wind Speed Forecasting](https://mlsanity.com/paper/2108.07063/) (2021)
- [TENT: Tensorized Encoder Transformer for Temperature Forecasting](https://mlsanity.com/paper/2106.14742/) (2021)
- [Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures](https://mlsanity.com/paper/2208.13585/) (2022)
- [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)
- [Multi Scale Graph Wavenet for Wind Speed Forecasting](https://mlsanity.com/paper/2109.15239/) (2021)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses](https://mlsanity.com/paper/2201.03229/) (2022)
- [Application of BERT in Wind Power Forecasting-Teletraan's Solution in Baidu KDD Cup 2022](https://mlsanity.com/paper/2307.09248/) (2023)
- [GCN-FFNN: A Two-Stream Deep Model for Learning Solution to Partial Differential Equations](https://mlsanity.com/paper/2204.13744/) (2022)
- [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)
- [Beyond MSE: Improving Precipitation Nowcasting with Multi-Quantile Regression](https://mlsanity.com/paper/2605.30122/) (2026)
- [Temporal Context Conditioning for Seasonality-Aware Precipitation Nowcasting of High-Intensity Rainfall](https://mlsanity.com/paper/2606.09959/) (2026)
- [Zhinv: Real-time hub-height wind field reconstruction using only local sparse observations](https://mlsanity.com/paper/2607.25298/) (2026)

## Abstract (arXiv)

Wind speed prediction and forecasting is important for various business and management sectors. In this paper, we introduce new models for wind speed prediction based on graph convolutional networks (GCNs). Given hourly data of several weather variables acquired from multiple weather stations, wind speed values are predicted for multiple time steps ahead. In particular, the weather stations are treated as nodes of a graph whose associated adjacency matrix is learnable. In this way, the network learns the graph spatial structure and determines the strength of relations between the weather stations based on the historical weather data. We add a self-loop connection to the learnt adjacency matrix and normalize the adjacency matrix. We examine two scenarios with the self-loop connection setting (two separate models). In the first scenario, the self-loop connection is imposed as a constant additive. In the second scenario a learnable parameter is included to enable the network to decide about the self-loop connection strength. Furthermore, we incorporate data from multiple time steps with temporal convolution, which together with spatial graph convolution constitutes spatio-temporal graph convolution. We perform experiments on real datasets collected from weather stations located in cities in Denmark and the Netherlands. The numerical experiments show that our proposed models outperform previously developed baseline models on the referenced datasets. We provide additional insights by visualizing learnt adjacency matrices from each layer of our models.