Research On Energy Saving Optimization Of Ventilation And Air-conditioning System In Public Area Of Subway Station

With the aggravation of traffic problems,subways have become the first choice for people to travel,and subway stations are mostly underground stations,which are tightly sealed and have a large passenger flow.Therefore,the requirements for ventilation and air-conditioning systems are increasing day by day,and with it the increase in energy consumption.Optimal control of them can save energy while meeting environmental requirements.Taking a subway station in Xi’an as an example,this article puts forward a new air control method for the ventilation and air-conditioning system of the subway station based on passenger flow prediction through the study of the ventilation and air-conditioning system of the subway station.Using deep learning algorithms to predict short-term passenger flow in subway stations,dynamically adjust the size of the fresh air volume according to changes in passenger flow,and realize variable air volume control of the fresh air system,thereby achieving the purpose of energy saving.The main research contents of the article are as follows:（1）Research on subway ventilation and air-conditioning system.Carry out theoretical research on subway ventilation and air-conditioning system,analyze its composition and classification,analyze the load composition of the screen door system,analyze the influence of different factors on the load and the influence of different types of loads on the system,so as to determine the research object of the article as the passenger flow With the fresh air system,take passenger flow as the input to carry out energy-saving optimization research on the fresh air system,laying a solid foundation for subsequent experimental research.（2）Forecast of short-term passenger flow in subway stations.Aiming at the problem of slow optimization of LSTM model parameters,construct a passenger flow prediction model based on improved bat algorithm（IBA）optimized long short-term memory（LSTM）network,and introduce Opposition-Based Learning,Dynamic Adaptive Inertial Weighting and Lagrangian Interpolation to improve the operating speed and accuracy of the model,using IBA to optimize the parameters of the LSTM model,to achieve accurate prediction of passenger flow in subway stations,and to provide data input for the control system.（3）Research on control strategy of ventilation and air-conditioning system.Research on existing control strategies,and propose a new air control strategy for subway public areas based on passenger flow prediction based on the new air optimization control plan,and use building pollution and CO₂concentration to calculate new air volume settings Value to enhance the effectiveness of the control strategy.（4）Modeling and simulation of ventilation and air conditioning systems.Design a fuzzy PID controller,use Simulink modeling and simulation to verify the fresh air dynamic control and energy-saving effects of the predictive model and control strategy.The results show that the model constructed in the article can dynamically adjust the fresh air volume input according to the changes in passenger flow.The traditional control method is compared with the load of one day,and the results verify that the method in the article can effectively reduce the energy consumption of the system.