Tsaknakis, Christos (Democritus University of Thrace), Korkas, Christos (Center for Research & Technology Hellas), Vamvakas, Dimitrios (Democritus University of Thrace), Boutalis, Yiannis (Democritus University of Thrace), Kosmatopoulos, Elias (Democritus University of Thrace and CERTH, Greece)

Online and Adaptive PID Tuning for Thermal Zone Temperature Control with Bayesian Optimization

Scheduled for presentation during the Regular Session "Optimisation" (WeCD), Wednesday, June 11, 2025, 16:30−16:50,

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

This paper presents a Bayesian Optimization (BO)-based method for online tuning of a Proportional-Integral- Derivative (PID) controller in a real-world thermal zone HVAC application. Unlike conventional PID tuning methods that require manual intervention and offline calibration, the proposed approach optimizes PID gains autonomously and continuously, adapting daily to external disturbances (e.g., weather fluctuations, occupancy changes) without requiring a system model. A novel transition error mechanism accounts for occupancy patterns, enabling the controller to anticipate heating/cooling demands by preemptively adjusting set-points, thereby improving thermal comfort and energy efficiency. Through a 90-day real-world experiment under varying winter conditions (January–March), the BO-optimized PID controller is evaluated against a traditional on-off dead-band rule-based controller (RBC), demonstrating 5–7% energy savings despite lacking direct energy consumption feedback during tuning, a 45% reduction in temperature deviations from set-points, and faster convergence to target temperatures during occupancy transitions. These findings validate BO as a robust, model-free solution for real-time PID tuning in dynamic environments, balancing energy efficiency with occupant preferences.