A deep CNN model for medium-range spatio-temporal wind speed prediction for wind energy applications

The amount of wind farms and wind power production in Europe, on-shore and off-shore, increased rapidly in the past years. To ensure grid stability, omit fees in energy trading, and on-time (re)scheduling of maintenance tasks accurate predictions of wind speed and wind energy is needed. Especially for the prediction range of +48 hours up to 2 weeks ahead at least hourly predictions are envisioned by the users. However, these are either not covered by the high-resolution models or are on a spatial and temporal course scale. 

To address this as a first step we therefore propose a deep CNN based model for wind speed prediction  using the ECMWF ERA5 to train our model using at least seven wind-related temporal variables, i.e. divergence, geopotential, potential vorticity, temperature, relative vorticity, vertical wind velocity and horizontal wind velocity.

The input of the CNN is represented by  the 3-dim tensor (size of the 2-dim figures x time shots), one for each variable. The CNN  outputs the most probable of the six categories in which the wind speed will be during the following 96 hours, in 6h intervals. Different combinations of input data are investigated in terms of temporal input.

We analyse the influence of prediction range on the predicted category as well as the relevance of each of the wind-related variables in the prediction of this category.  The model will be tested and applied to the ECMWF IFS forecasts over Austria. The ensure a higher spatial and temporal resolution an additional step will be used for downscaling the CNN directly to a 1 km grid.

This work is performed as part of the MEDEA project, which is funded by the Austrian Climate Research Program.