Research On Power Consumption Prediction Model And Energy Saving Diagnosis Of Air Conditioning System In A Terminal

China’s"One Belt,One Road"high-quality development belt lays the foundation for the development of major strategies.At the same time,the aviation industry also ushered in new opportunities.Corresponding to the ever-increasing construction scale of the terminal building is a steady stream of energy consumption issues.Among them,the air-conditioning system consumes the largest amount of electricity,about 35%.Therefore,in order to minimize the problem of energy saving,finding effective energy saving methods will be a magic weapon.This paper samples the power consumption of the air-conditioning system in a terminal in the northwest region,conducts in-depth research and tries to predict and implement energy saving.The specific content is as follows:Firstly,the influence factors of the power consumption of air conditioning system in a terminal in Northwest China are screened.The power consumption of the terminal is related to multiple factors.Therefore,the correlation analysis method is used to filter the factors that affect power consumption,and the analysis is carried out from the weather factors and the historical power consumption data.Through calculation,the relative humidity of the current time,the cooling load of the air conditioner in the previous moment and the cooling load of the air conditioner in the first two times are calculated The correlation between the outdoor air temperature at the current time,the outdoor air temperature at the previous time,the solar radiation intensity at the current time and the solar radiation intensity at the previous time is the highest.According to the influence variables analyzed,it can be used as input variable in the modeling of the subsequent prediction model,which lays a foundation for the subsequent prediction of energy consumption and energy saving diagnosis of air conditioning.Secondly,research on the power consumption prediction model of the terminal air-conditioning system.There are many variables in the power consumption model of terminal air conditioning system,and the system is very complicated..Traditional methods are difficult to predict accurately.On the basis of determining BP neural network training function and network structure,,GA-SA is used to optimize BP neural network to establish power consumption prediction model of terminal air conditioning system type.Finally,in view of the improper management of the air-conditioning system in the terminal building and the waste of energy during use,perform energy saving diagnosis.Clustering algorithm is used to screen the relative energy saving data from the historical power consumption data as the diagnosis data,and the judgment basis of three-level power consumption anomaly is established,Therefore,energy saving diagnosis research is carried out.Through the case study verifying the measure is viable.The article presents a prediction model for the power consumption of the air-conditioning system in the terminal building and an energy saving diagnosis model.By case study,the value of M_REhas reached 1.07%,and the value of R_MSPEhas reached 1.36%.and it is successful.Diagnosed the four non-energy-saving moments of the air-conditioning system in the terminal,which solves the problems of insufficient combination of energy consumption prediction and operation management,extensive operation management and so on,and provides reference for the energy-saving operation management of the terminal in China.