## 1 Introduction  
To improve energy efficiency and enhance the reliability of heating, ventilation, and air-conditioning (HVAC) systems, artificial intelligence (AI) technology for refrigeration, heat pumps, and air conditioners has emerged in recent decades [1, 2]. [PERSON], [PERSON] et al. [3]and [PERSON], [PERSON] et al. [4] proposed a flexible PID controller that employs a deep reinforcement learning (DRL) algorithm to optimize its parameters. [PERSON], [PERSON] et al. [5] investigated the accuracy of machine learning routines in building optimization controlling and the ability to learn controlled and optimized data for different systems.  
To improve the performance of constant-speed air-cooled water chiller (CAWC) air-conditioning systems, [PERSON], [PERSON] et al. [6] developed an optimal control method to reset the temperature of CAWCs in real time. [PERSON], [PERSON] et al. [7] proposed a smart control model for HVAC systems in manufacturing systems. The research results of [PERSON], [PERSON] et al. [8], [PERSON], [PERSON] et al. [9], [PERSON], [PERSON], et al. [10], [PERSON], [PERSON] et al. [11], [PERSON], [PERSON] et al. [12], [PERSON], [PERSON] et al. [13], [PERSON], [PERSON] et al. [14], and [PERSON] et al. [15] showed that the neural network technology could achieve good control and reduce energy; meanwhile, most of the existing studies on HVAC systems only highlighted the optimization of individual devices, but did not fully consider global optimization.  
According to the analysis of domestic and international research status, the improvement of energy efficiency is an urgent need for energy conservation in refrigeration station equipment; at the same time, it can also meet the project and investment market demand for energy-saving transformation of refrigeration plant rooms. It is also found that AI technology has broad application prospects in the HVAC field. However, the major limitations of the application of AI technology in HVAC systems include exploding and/or vanishing gradients, interpretability, training saturation, etc.  
This paper will first analyze the key issues affecting the energy-saving performance of a refrigeration station and set the optimization goals for the control system. Secondly, the focus of this paper will study the retrofitting of refrigeration station equipment (bidirectional variable flow technology) and the optimization of control systems based on the reinforcement learning capability of fuzzy neural network and its control algorithm. Finally, the application verification analysis shows that the research results are significant for improving the energy efficiency and reliable operation of refrigeration station equipment.  
The paper structure is as follows: Firstly, Section 2 analyzes the main problems affecting the energy-saving effect of refrigeration stations.  
Secondly, in Section 3, the bidirectional variable flow technology of primary and secondary chilled water pumps is presented, and the feasibility of a fuzzy neural network in bidirectional variable flow systems and its energy-saving effect are studied.  
Thirdly, Section 4 presented the practical feasibility of fuzzy neural networks in bidirectional variable flow systems and their energy-saving effect.  
Finally, in Section 5, the obtained research achievements are given, and the limits of the proposed approach are also highlighted.  
Analysis of Main Problems Affecting Energy-Saving Effect of Refrigeration Station and Optimization Principle of the Control System