A Deep Reinforcement Learning Approach For Load Shifting in Industrial Refrigeration Facilities

Abstract

Industrial refrigeration facilities are among the most energy-intensive industrial systems and operate under strict temperature constraints to ensure product safety and quality. Optimizing energy consumption can therefore lead to substantial cost savings. The allowable temperature flexibility of stored products effectively provides thermal storage, which can be leveraged for intelligent control. However, in facilities with multiple rooms coupled through a centralized cooling unit, commonly employed heuristic strategies are often insufficient for meaningful cost reduction. More comprehensive model-based control approaches, e.g., model predictive control, also suffer from uncertainty in thermal parameters, external disturbances, and product characteristics. This paper explores the potential of model-free control in industrial refrigeration. We propose a reinforcement learning–based control strategy that learns an energy-efficient cooling policy through interaction with the system, without requiring explicit knowledge of the dynamics. Simulation results on a simplified facility demonstrate that the proposed approach exploits both thermal storage and inter-room coupling to reduce power consumption while maintaining temperature constraints and outperforming heuristic controllers. These positive results suggest that further study is warranted.