**6. Discussion**

In comparison to other approaches of grid state forecasts, the presented software application includes a few more use cases, as for example the missing of previous measurements. Other approaches, like they are shown in [6,7,23], obligatorily need measurements to calculate future grid states. This approach allows it to handle new situations in the grid without the necessity to restart the whole system. For example, this would be the case with a neural network, because it would have to train the system once again when a new generation system is integrated or the topology is changed. In addition, neural networks rely on a big database and a long executing time, which was already analyzed in [14].

The presented bottom-up approach instead needs less data (as shown in Section 2) and does not have to be trained at first. Due to these facts, a substitution of the shown grid state forecast with all its functionalities by a neural network is not possible, but some single forecast modules could be supplemented by other forecast models, like neural networks for example. Therefore, the possibility is given to combine this framework with other forecast methods.

The modular bottom-up approach allows changing single parts like the photovoltaic power forecast for example. The innovation consists therefore of the framework around all these single modules, which decides which module has to be used depending on the actual database.

The presented grid state forecast is realized in a standalone application and is very robust against external effects. In addition, it allows the exchange of forecast information between different voltage levels.
