  The forecasting of local GIC effects has largely relied on the forecasting of
dB/dt as a proxy and, to date, little attention has been paid to directly
forecasting the geoelectric field or GICs themselves. We approach this problem
with machine learning tools, specifically recurrent neural networks or LSTMs by
taking solar wind observations as input and training the models to predict two
different kinds of output: first, the geoelectric field components Ex and Ey;
and second, the GICs in specific substations in Austria. The training is
carried out on the geoelectric field and GICs modelled from 26 years of
one-minute geomagnetic field measurements, and results are compared to GIC
measurements from recent years. The GICs are generally predicted better by an
LSTM trained on values from a specific substation, but only a fraction of the
largest GICs are correctly predicted. This model had a correlation with
measurements of around 0.6, and a root-mean-square error of 0.7 A. The
probability of detecting mild activity in GICs is around 50%, and 15% for
larger GICs.

Geophysics;Solar and Stellar Astrophysics