Modeling and forecasting supply networks using functional time series and mathematical programming

In NETOPT, we will develop an innovative high-frequency forecasting model that analyses complex spatial and temporal dynamics in large-scale networks under demand and supply constraints.

Using supply networks as an example, NETOPT aims to model and forecast large-scale network flows using a Functional Time Series (TS) and Mathematical Programming (MP) approach.

The network model proposed in NETOPT can optimize decision planning and efficient scheduling by reducing financial and technical risks in transmission and distribution networks, such as gas and water networks.

To demonstrate the practical effectiveness of our new approach, we will implement models to describe and predict hourly gas flows for several days ahead in the German high-pressure gas transmission network using real-world data.

Efficient prediction and interpretation of the complex dynamics in transport and distribution networks is a crucial component of an intelligent decision support system that helps to reach the climate targets of the European Green Deal in a fair, cost-effective, and competitive way.