  We tune one of the most common heating, ventilation, and air conditioning
(HVAC) control loops, namely the temperature control of a room. For economical
and environmental reasons, it is of prime importance to optimize the
performance of this system. Buildings account from 20 to 40% of a country
energy consumption, and almost 50% of it comes from HVAC systems. Scenario
projections predict a 30% decrease in heating consumption by 2050 due to
efficiency increase. Advanced control techniques can improve performance;
however, the proportional-integral-derivative (PID) control is typically used
due to its simplicity and overall performance. We use Safe Contextual Bayesian
Optimization to optimize the PID parameters without human intervention. We
reduce costs by 32% compared to the current PID controller setting while
assuring safety and comfort to people in the room. The results of this work
have an immediate impact on the room control loop performances and its related
commissioning costs. Furthermore, this successful attempt paves the way for
further use at different levels of HVAC systems, with promising energy,
operational, and commissioning costs savings, and it is a practical
demonstration of the positive effects that Artificial Intelligence can have on
environmental sustainability.
