Network complexity is constantly increasing. The stability of a network depends on a wide variety of actors, factors and variables, which are dynamically interdependent. This includes a steadily growing number of decentralized feeders, consumers and prosumers. A constant fine-grained examination of a network down to individual measuring points is becoming more and more necessary to ensure network security. The amount of data places new demands on forecasting tools. Autonomous forecasting is not possible without the use of self-learning artificial intelligence. ifesca.AIVA® provides the answer to this question both quantitatively and qualitatively.

The forecasts created using ifesca.AIVA® provide the basis for further consideration of local networks. Simulation calculations regarding entry and exit situations or overload situations can be created quickly, reliably and easily based on these forecasts.