  It has long been known that large grounded conducting networks on the surface
of Earth are affected by solar activity and geomagnetic storms. Power networks
are such extensive grounded conductors and are susceptible to geomagnetically
induced currents (GICs). GICs at any specific node in a power network are
assumed to be linearly related to the horizontal vector components of an
induced plane-wave geoelectric field by a pair of network parameters. These
network parameters are not easily measured in the network, but may be estimated
empirically. In this work, we present a new approach of using an ensemble of
network parameters estimates. The ensembles include a huge number of parameter
pair estimates calculated from simultaneously solving pairs of time instances
of the governing GIC equation. Each individual estimate is not the true state
of the system, but a possible state. Taking the ensemble as a whole though
gives the most probable parameter estimate. The most probable parameter
estimate for both network parameters, as defined by their respective ensembles,
is used directly in the modelling of GICs. The ensembles themselves however
allow for further analysis into the nature of GICs. An improvement is seen when
comparing the out-of-sample performance of the ensemble estimates with previous
GIC modelling in the South African power network during the Halloween Storm of
2003. For the first time, it is shown that errors in the GIC modelling chain
are absorbed into the network parameter estimates. Using a range of estimates
from the ensemble, a GIC prediction band is produced. This band corresponds to
an error estimate for predicted GIC. Furthermore, it has been explicitly shown
that estimated network parameters vary with GIC magnitude during an event. This
behaviour is then used to refine the parameter estimation further and allow for
real time dynamic network parameter estimation.

Space Physics