USEFLEDS – Unleashing Sector-coupling Flexibility by means of an Energy Data Space

The goal of USEFLEDS is to develop a solution for the shared exchange of data in the energy sector. A co-creation workspace connects the topics of the data economy, governance, energy management, data rooms, and artificial intelligence across projects through iterative processes. Based on these project priorities, USEFLEDS is creating the foundation for shared data exchange, which will enable the development of data-driven services that benefit the energy transition and help achieve climate goals.

Salzburg Research is leading the AI Integration work package in this project: Machine learning and rule-based systems – and is thus contributing its expertise in the field of ICT for energy systems (e.g., flexibility trading, local energy communities, regional, energy- and cross-sector management systems) and in the fields of artificial intelligence and data science.

The aim of this work package is to use the developed data exchange platform in combination with highly innovative AI methods to implement the use cases defined in the co-creation process in an exemplary manner.

To achieve this, we will develop new solutions based on state-of-the-art machine learning models, such as graph neural networks and transformer architectures, using heterogeneous data sets to address typical problems in the energy sector, focusing on flexibility.

Through the implementation of these use cases, the project will demonstrate the significance, applicability, and usefulness of the developed data service ecosystem for the energy transition.