Deep Graph Convolutional Networks for Wind Speed Prediction

Tomasz Stanczyk, Siamak Mehrkanoon

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

Abstract

In this paper, we introduce a new model for wind speed prediction based on spatio-temporal graph convolutional networks. Here, weather stations are treated as nodes of a graph with a learnable adjacency matrix, which determines the strength of relations between the stations based on the historical weather data. The self-loop connection is added to the learnt adjacency matrix and its strength is controlled by additional learnable parameter. Experiments performed on real datasets collected from weather stations located in Denmark and the Netherlands show that our proposed model outperforms previously developed baseline models on the referenced datasets.

Original languageEnglish
Title of host publicationEuropean Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)
Publisheri6doc
Pages147-152
Number of pages6
ISBN (Print)978287587082-7
DOIs
Publication statusPublished - 2021
Event29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Online, Belgium
Duration: 6 Oct 20218 Oct 2021
https://www.esann.org/

Symposium

Symposium29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Abbreviated titleESANN 2021
Country/TerritoryBelgium
Period6/10/218/10/21
Internet address

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