  The strong growth of renewable energy sources and the high volatility in
power generation of these sources, as well as the increasing amount of volatile
energy consumption is leading to major challenges in the electrical grid. In
order to ensure safety and reliability in the electricity grid, the power flow
in the grid needs to be observed to prevent overloading. Furthermore, the
energy supply and consumption need to be continuously balanced to ensure the
security of energy supply. Therefore a high quality of power flow forecasts for
the next few hours within the grid are needed. In this paper we investigate
forecasts of the vertical power flow at transformer between the medium and high
voltage grid. Forecasting the vertical power flow is challenging due to
constantly changing characteristics of the power flow at the transformer. This
is mainly a result of dynamic grid topologies, changes in the installed assets,
maintenance of the transformer itself as well as the volatile generation. In
this paper we present a novel approach to deal with these challenges. For the
multi step time series forecasts a Long-Short Term Memory (LSTM) is used. In
our presented approach an update process where the model is retrained regularly
is investigated and compared to baseline models. The model is retrained as soon
as a sufficient amount of new measurements are available. This retraining
should capture changes in the characteristic of the transformer that the model
has not yet seen in the past and therefore cannot be predicted by the model.
For the regular update process we investigate different strategies where
especially the number of used epochs are considered, but also different
learning rates are used. We show that our new approach significantly
outperforms the investigated baseline approaches.
