# Safe Contextual Bayesian Optimization for Sustainable Room Temperature PID Control Tuning

## TL;DR

This paper presents a safe Bayesian optimization method to tune PID controllers for room temperature, significantly reducing costs and improving safety, with implications for sustainable HVAC system management.

## Contribution

It introduces a novel safe contextual Bayesian optimization approach for automatic PID tuning in HVAC systems, enhancing efficiency and safety without human intervention.

## Key findings

- Reduced tuning costs by 32%
- Ensured safety and comfort during optimization
- Demonstrated potential for broader HVAC system improvements

## Abstract

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.