[HTML][HTML] Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures
To improve the security and reliability of wind energy production, short-term forecasting has
become of utmost importance. This study focuses on multi-step spatio-temporal wind speed
forecasting for the Norwegian continental shelf. In particular, the study considers 14 offshore
measurement stations and aims to leverage spatial dependencies through the relative
physical location of different stations to improve local wind forecasts and simultaneously
output different forecasts for each of the 14 locations. Our multi-step forecasting models …
become of utmost importance. This study focuses on multi-step spatio-temporal wind speed
forecasting for the Norwegian continental shelf. In particular, the study considers 14 offshore
measurement stations and aims to leverage spatial dependencies through the relative
physical location of different stations to improve local wind forecasts and simultaneously
output different forecasts for each of the 14 locations. Our multi-step forecasting models …
Spatio-Temporal Wind Speed Forecasting using Graph Networks and Novel Transformer Architectures
L Ødegaard Bentsen, N Dilp Warakagoda… - arXiv e …, 2022 - ui.adsabs.harvard.edu
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 …
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 …
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