Multistream Graph Attention Networks for Wind Speed Forecasting

D. Aykas, S. Mehrkanoon*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review


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.
Original languageEnglish
Title of host publication2021 IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings
Number of pages8
ISBN (Electronic)9781728190488
ISBN (Print)9781728190488
Publication statusPublished - 2021
EventIEEE Symposium Series on Computational Intelligence (IEEE SSCI) - Orlando, United States
Duration: 5 Dec 20217 Dec 2021


SymposiumIEEE Symposium Series on Computational Intelligence (IEEE SSCI)
Country/TerritoryUnited States
Internet address


  • Wind speed prediction
  • graph attention networks
  • attention per weather variable
  • learnable adjacency matrix
  • attention visualisation

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