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 language | English |
---|---|
Title of host publication | European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) |
Publisher | i6doc |
Pages | 147-152 |
Number of pages | 6 |
ISBN (Print) | 978287587082-7 |
DOIs | |
Publication status | Published - 2021 |
Event | 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - Online, Belgium Duration: 6 Oct 2021 → 8 Oct 2021 https://www.esann.org/ |
Symposium
Symposium | 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
---|---|
Abbreviated title | ESANN 2021 |
Country/Territory | Belgium |
Period | 6/10/21 → 8/10/21 |
Internet address |