  Geometric deep learning has gained tremendous attention in both academia and
industry due to its inherent capability of representing arbitrary structures.
Due to exponential increase in interest towards renewable sources of energy,
especially wind energy, accurate wind speed forecasting has become very
important. . In this paper, we propose a novel deep learning architecture,
Multi Scale Graph Wavenet for wind speed forecasting. It is based on a graph
convolutional neural network and captures both spatial and temporal
relationships in multivariate time series weather data for wind speed
forecasting. We especially took inspiration from dilated convolutions, skip
connections and the inception network to capture temporal relationships and
graph convolutional networks for capturing spatial relationships in the data.
We conducted experiments on real wind speed data measured at different cities
in Denmark and compared our results with the state-of-the-art baseline models.
Our novel architecture outperformed the state-of-the-art methods on wind speed
forecasting for multiple forecast horizons by 4-5%.
