  This study focuses on multi-step spatio-temporal wind speed forecasting for
the Norwegian continental shelf. The study aims to leverage spatial
dependencies through the relative physical location of different measurement
stations to improve local wind forecasts. Our multi-step forecasting models
produce either 10-minute, 1- or 4-hour forecasts, with 10-minute resolution,
meaning that the models produce more informative time series for predicted
future trends. A graph neural network (GNN) architecture was used to extract
spatial dependencies, with different update functions to learn temporal
correlations. These update functions were implemented using different neural
network architectures. One such architecture, the Transformer, has become
increasingly popular for sequence modelling in recent years. Various
alterations have been proposed to better facilitate time series forecasting, of
which this study focused on the Informer, LogSparse Transformer and Autoformer.
This is the first time the LogSparse Transformer and Autoformer have been
applied to wind forecasting and the first time any of these or the Informer
have been formulated in a spatio-temporal setting for wind forecasting. By
comparing against spatio-temporal Long Short-Term Memory (LSTM) and Multi-Layer
Perceptron (MLP) models, the study showed that the models using the altered
Transformer architectures as update functions in GNNs were able to outperform
these. Furthermore, we propose the Fast Fourier Transformer (FFTransformer),
which is a novel Transformer architecture based on signal decomposition and
consists of two separate streams that analyse the trend and periodic components
separately. The FFTransformer and Autoformer were found to achieve superior
results for the 10-minute and 1-hour ahead forecasts, with the FFTransformer
significantly outperforming all other models for the 4-hour ahead forecasts.
