A large amount of energy is consumed to create comfort and productive built environments for people in high-density cities, which is associated with large amount of carbon emissions. To increase the use of renewable energy in high density cities, new methods and tools are needed. As energy systems in buildings, districts and cities are complex, energy saving and energy flexibility provision must reflect system physics to avoid sub-optimal energy management. Thus, SEM4Cities integrates system physics in a hierarchical spatial and temporal framework, including models for energy aggregation at all levels, combing machine learning with mathematical models of known physical processes. SEM4Cities brings universities, living labs and companies in with complementary expertise and technologies together to accelerate the green transition.

SEM4Cities Goals

SEM4Cities aims to develop innovative smart energy management technologies and solutions for buildings and districts in high-density cities by effectively leveraging advanced big data and AI technologies. These include:

1. Data driven predictive models for energy dynamics characterization and energy flexibility prediction for buildings and districts;
2. AI-powered model predictive control and optimization methods for distributed energy systems;
3. Game theory-based methods for optimization and coordination of buildings and distributed energy systems.