RACE - Realtids AI Computing i Energisektoren

Real-time grid data, cloud computing, Machine Learning and AI Control enable realization of the next gen-eration of district heating systems that have been much anticipated the recent years. Knowing how to apply these technologies in a flexible, secure and thus scalable way will be key to export district heating (as well as cooling solutions) as modern and future-proofed energy solutions to new markets.

To achieve these optimizations, it requires a digitization of the district heating sector by having real-time input from across the grid and a cross-sector integration to different energy and data sources. This increases the amount of data that must be processed, stored, and analyzed and thus imposes requirements of openness, flexibility and security to the data storage.

We develop a sensor solution for real-time measuring of pressure, temperature and flow in grid valves. The measured pressure from the grid is used to set accurate pressures on main and booster pumps. We develop an open and flexible data platform for acquiring and structuring machine and time-series data that enables open sharing of data between systems. We develop solutions for decision support for systemwide planning using AI algorithms trained on Machine Learning generated predictive digital twins learned from datasets spanning whole seasons. We utilize general solutions for integration to typical district heating OT-systems, the solutions are based on open and modern industry standards. We develop a solution for controlling district heating valves from SCADA based on AI control technology.