Development of the Reward Function to support Model-Free Reinforcement Learning for a Heat Recovery Chiller System Optimization

Abstract

Heat recovery chiller systems have significant strategic value to reduce building greenhouse gas emissions although this potential remains unrealized in practice. Real-time optimization using model-free reinforcement learning provides a potential solution to this challenge. A full-scale case study to implement reinforcement learning in a 6,000 m2 academic laboratory is planned. This paper presents the methodology used to translate historical data correlations and expert input from operations personnel into the development of the reinforcement learning agent and associated reward function. This approach will permit a more stable and robust implementation of model-free reinforcement learning and the methodology presented will allow operator-identified constraints to be translated into reward functions more broadly, allowing for generalization to similar heat recovery chiller systems.