Neural network-based model predictive control for energy efficient HVAC systems for CERN accelerators

In times of concern over the environmental impact of high-energy physics organizations, our research in CERN's Cooling and Ventilation group in the Engineering department investigates energy-saving strategies for heating, ventilation, and air conditioning (HVAC) systems. Widely used in both residential and industrial settings, HVAC systems contribute up to 40% of residential and 70% of industrial consumption, making their optimization a global concern. At CERN, cooling and ventilation systems are used to ensure appropriate temperature and humidity conditions in the accelerator complex. Together with general water-cooling systems, these systems account for up to 12% of total electricity consumption of CERN’s flagship accelerator. Despite their energy intensity, these systems are typically managed by classical controllers, which are reliable for temperature and humidity regulation but not optimal in terms of energy efficiency. This study aims to quantify the potential energy savings in HVAC systems using model predictive control (MPC), an advanced control strategy that incorporates behaviour prediction and external data, such as weather forecast. In a quest for a reproducible solution that can be easily adapted to different HVAC plants, the digital twin is built using physics-informed neural networks, following recent advances in academic research.