  Accurate wind speed forecasting is of great importance for many economic,
business and management sectors. This paper introduces a new model based on
convolutional neural networks (CNNs) for wind speed prediction tasks. In
particular, we show that compared to classical CNN-based models, the proposed
model is able to better characterise the spatio-temporal evolution of the wind
data by learning the underlying complex input-output relationships from
multiple dimensions (views) of the input data. The proposed model exploits the
spatio-temporal multivariate multidimensional historical weather data for
learning new representations used for wind forecasting. We conduct experiments
on two real-life weather datasets. The datasets are measurements from cities in
Denmark and in the Netherlands. The proposed model is compared with traditional
2- and 3-dimensional CNN models, a 2D-CNN model with an attention layer and a
2D-CNN model equipped with upscaling and depthwise separable convolutions.
