AI techniques for energy optimization of buildings integrated into smart cities and interoperability with Data Spaces (T7)

Research challenges / Novelty / Innovation
• Development of AI techniques to predict electricity consumption and production of buildings
• Development of AI techniques for the coordination and optimal aggregation of flexibility
• Design of a demonstrator for efficient energy management of buildings in a smart city
• Development of prediction and aggregation services
• Testing and validation in a pilot

Research results:
• Reduction of prediction error compared to current methods
• Increase in the amount of flexible energy used by buildings
• Increase in the utilization rate of green energy (if available)
• Reduction of buildings’ carbon footprint

Inovation:
• Development of advanced AI models (LLM, Transformer, and RL) for predicting energy consumption and production, and for aggregation
• The demonstrator can be replicated and extended at national and international level, facilitating the transition to sustainable smart cities.
• Energy Data Spaces ensure secure and interoperable data exchange, enabling near real-time coordination of flexibility.
• Pilot (TRL 6)