Physics-consistent input convex neural network-driven reinforcement learning control for multi-zone radiant ceiling heating and cooling systems: An experimental study

Abstract

Radiant ceiling heating and cooling system is a technology used for space heating and cooling. Owing to the variable weather conditions, occupant behavior, and thermal lag of the system, it is challenging to design a control strategy to reduce air-conditioning energy consumption while maintaining the thermal environment. This study is the first pilot implementation of a physics-consistent input convex neural network (PCICNN)-driven reinforcement learning (RL) approach for real-world multi-zone radiant ceiling heating and cooling systems. A multi-zone PCICNN based on a graph neural network (GNN) was developed to simulate the zone temperature. The radiant panel load was simulated using the physics-based ε-NTU method. The PCICNN-driven RL agent was based on the soft actor-critic algorithm and trained in an environment model comprising the PCICNN and ε-NTU models. The proposed controller was deployed in real-time on one floor of an office building for one month. The real-world implementation showed that the proposed PCICNN-driven RL control can reduce the radiant panel cooling load by up to 33% compared with the inherited baseline control strategy under similar weather conditions. This study provides a comprehensive demonstration of real-world data-driven building controls and leverages future research on advanced building control.