Dynamic load adjustment strategy for carbon dioxide refrigeration system based on hidden Markov model

Carbon dioxide refrigeration system is an indispensable equipment in modern industrial production, which plays an important role in maintaining the temperature and humidity of the production environment. Traditional air conditioning systems suffer from high energy consumption and inflexible adjustments during operation. This article is based on Hidden Markov Model (HMM) and uses HMM to monitor real-time parameters such astemperature and humidity in the production environment, and adjust the system operation status accordingly. Hidden Markov Models make predictions based on historical data when the load changes, automatically adjust the operating mode of the refrigeration system, and improve the energy efficiency and stability of the system. The response time for static load adjustment strategies under low load, medium load, and high load is all above 30 seconds, while the average response time for dynamic load adjustment strategies based on HMM is within 30 seconds. The dynamic load adjustment strategy based on HMM has a faster adjustment response speed than the static load adjustment strategy, which comes from the real-time prediction and adaptive control mechanism of the system state. This article proves that this strategy can improve the load adaptability and energy efficiency of refrigeration systems, while reducing system operating costs, and has high engineering application value.