Energy Consumption Prediction And Energy Conversation Of The HVAC System In Subway Station

In recent years,with the advent of the subway,not only can it bring convenience to people's travel,but also relieve traffic pressure.The subway has become an indispensable product of the continuous development of big cities.At present,many cities in China are still under construction or expansion.However,it is a huge energy consumer for the entire subway system.Since the subway is usually built underground and better serves passengers,the requirements for the operation of the subway are relatively high,requiring a large number of instruments with high precision.Equipment to protect the safety of passengers and provide passengers with a comfortable ride environment.The power consumption of the subway in this process is huge.Among them,the venti lation and air conditioning system of the subway station accounts for nearly 1/3 of the energy consumption.Therefore,how to effectively reduce the energy consumption of the ventilation and air conditioning system of the subway station becomes more import ant.At present,the main reason is to analyze the heat load in the subway station,it is difficult to accurately predict the energy consumption of the subway ventilation and air conditioning system,and the energy saving has been achieved.Therefore,it i s of practical and important significance to find a method to predict the energy consumption of the ventilation and air conditioning system of the station.Based on the research of a subway station in Guangzhou,this paper proposes a BP neural network base d energy consumption prediction model for the ventilation and air conditioning system of the metro station,predicts the energy consumption of the station ventilation and air conditioning system,and finally analyzes the energy saving potential.Firstly,the paper expounds the necessity and purpose of the ventilation and air conditioning system of the subway station,introduces the classification of the ventilation and air conditioning system of the subway station,and analyzes the research status of the energy saving of the ventilation and air conditioning system of the subway station.This paper introduces the basic situation of a metro station in Guangzhou based on this paper.Then,through the analysis of the influencing factors of the energy consumption of the ventilation and air conditioning system of the subway station,the influencing factors are determined.The data acquisition and preprocessing of each influencing factor provides theoretical basis and data support for BP neural network metro station ventilation and air conditioning system energy consumption prediction model establishment and prediction.Secondly,according to the BP neural network,the energy consumption prediction model of the ventilation and air conditioning system of the subway st ation is established,which considers the temperature inside the station,the humidity inside the station,the temperature outside the station,the humidity outside the station,the passenger flow,the power of the new fan,the train number,the equipment and the utilization rate of the lighting.The input layer of the neural network model,the energy consumption of the ventilation and air conditioning system of the metro station as the output layer.The samples collected are trained and the prediction data is simulated and predicted.Through the error analysis,the accuracy of the prediction model is judged.Finally,based on the data of a certain day,the energy saving potential of the station ventilation and air conditioning system is analyzed.