50% reduction in energy consumption in an actual cold storage facility using a deep reinforcement learning-based control algorithm

This study presents a unique application of a temperature control algorithm, specifically modified deep deterministic policy gradient (DDPG), in an actual 2.8 m2 cold storage facility, contrasting the majority of research that leverages theoretical validations using simulation tools. The primary goal was to minimize energy consumption while maintaining the desired temperature range. To achieve this, thermocouples and a watt-hour meter were installed to collect real-time data on temperature and power consumption, subsequently transmitted to a deep-learning computing and control system for processing. Utilizing the gathered data, the algorithm was trained to simultaneously maintain the temperature and minimize power consumption. The temperature setting served as a control variable, and a deep deterministic policy gradient algorithm was used. A hyperparameter with a dominant influence on learning outcomes was optimized. Furthermore, the algorithm was exposed to various complex scenarios that occur during actual cold storage operations, such as door opening, reinforcing its practical viability. The study findings revealed that our real-world application of the DDPG algorithm significantly reduced energy consumption by 47.64% compared to conventional proportional-integral-derivative control algorithms, whilst maintaining the target temperature range.