SOURCE-LINKED INTELLIGENCE

Battery-informed Resilient Energy Scheduling Framework for EMS-BMS Coordination in Smart Buildings

CORDIS · observation · Publication date unknown

cheduling Framework for EMS-BMS Coordination in Smart Buildings) will develop the first integrated, AI-driven EMS–BMS coordination framework for smart buildings. The approach combines: (i) multi-task deep learning forecasting of load, PV, and battery health, (ii) EMS optimisation using hybrid Model Predictive Control and Deep Reinforcement Learning with explicit battery health constraints, (iii) privacy-preserving synthetic datasets enabling reproducible and GDPR-compliant research, and (iv) validation on real-world data. By embedding battery health into EMS decisions, BRAIN will reduce household energy bills by 10–15%, extend battery lifetimes by 20%, and increase PV self-consumption by up to 20%. Scientifically, it will deliver 7 peer-reviewed journal and conference papers, 2 open FAIR datasets, and an open-source EMS–BMS toolkit. Societally and economically, the project will contribute to lower emissions, reduced e-waste, and affordable clean energy, while strengthening Europe’s leadership in the €250B global smart building market. BRAIN directly supports the European Green Deal,

Read original source ↗ Open in workspace

recordType

award

status

SIGNED

region

EU

value

247553.28

unit

EUR