  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.
