“Simplifying decision-making in the model-based co-design of building energy systems through automatically generated optimal controllers”
This study explores how automatically generated optimal controls can boost energy efficiency while simplifying the design and decision-making process in building energy systems. The case study focuses on a historic neighborhood in Bruges, Belgium, where the energy supply system was redesigned entirely with renewable and residual energy sources (R2ES), including air-source and ground-source heat pumps, photovoltaic thermal (PVT), and photovoltaic (PV) panels.
Two design strategies were compared:
- Rule-Based Control (RBC) – manually generated controls.
- Optimal Control (OC) – automatically generated using the Toolchain for Automated Control and Optimization (TACO).
Key insights:
🔹 Optimally controlled systems can reduce electricity consumption by around 30% compared to rule-based controls.
🔹 Automatically generated optimal control simplifies decision-making in model-based co-design processes and acts as an effective system integrator.
🔹 Integrating OC into the engineering workflow streamlines the design process while enabling high-performing building operation.
🔹 Advanced control strategies are crucial for the fast and cost-efficient energy transition of the built environment, even in complex or historic settings.
The study demonstrates that combining energy-efficient technologies with intelligent control systems allows cities to achieve sustainable energy solutions while optimizing both performance and design efficiency.
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