Research On Energy Consumption Prediction And Energy Saving Strategy Of An Airport Terminal Based On Insect Intelligence

The "One Belt and One Road" strategy has promoted the friendly exchanges between China and countries along the routes.The complex airport routes play a vital role as the hub,and a large number of terminals have been completed one after another.Due to the large building volume,the high energy consumption brought by the terminal building has become a prominent problem in urban construction.It is of great practical significance to study the energy-saving and optimal operation of the airport terminal building.From the perspective of energy conservation of building control system,the traditional distributed control system is complex in networking,high in installation cost,time-consuming and easy to cause information "island",which has certain disadvantages in energy management,and can not fully meet the new requirements of modern airport terminal building gradually transforming to "intelligent and energy saving".In order to reduce terminal energy consumption and improve operation efficiency,this paper studies the energy consumption monitoring and energy saving optimization strategies of key problems existing in terminals based on Insect Intelligence architecture.The specific contents are as follows:Firstly,this paper studies the energy consumption monitoring system of an airport terminal based on Insect Intelligence architecture,designs a terminal CPN topology structure based on Insect Intelligence,and obtains the time-series energy consumption data from the standard information set of spatial units and electromechanical equipment through the spanning tree,and B spline curve method is used to check the data to improve the accuracy of the data.Secondly,the characteristics of energy consumption of an airport terminal are analyzed to determine that it has chaotic characteristics.the combination of chaos theory and support vector machine(SVM)model is established for the terminal energy consumption to forecast future time,using the Markov chain correction error,the experimental results show that the predicted results of the root mean square error is0.4743,the average absolute percentage error is 0.1979,Compared with Chaoa-SVR,RMSE value decreased by 0.3413 and MAPE value decreased by 0.1692,indicating that the prediction accuracy was significantly improved.Finally,based on the energy consumption forecast results in the previous chapter,a distributed collaborative scheduling strategy based on Insect Intelligence is proposed to optimize the operation mode of the air-conditioning system,adjust the lighting brightness and the running speed of baggage checking system to save electric energy.The energy saving effect of the terminal was verified with the power consumption intensity of the terminal as the evaluation index.The energy consumption monitoring system based on Insect Intelligence proposed in this paper provides an effective method for solving the problems of information "islands" in the terminal and reducing network costs.The predicted results of the combined model provide a reliable data source for formulating energy-saving optimization strategies and avoid unnecessary energy waste.In this paper,a CPN topology structure for energy consumption monitoring of an airport terminal building based on Insect Intelligence is built.By taking full advantage of the characteristics of Insect Intelligence nodes’ simple networking and the fact that information "island" is not easy to exist,more comprehensive and accurate energy consumption data of the airport terminal is obtained.By analyzing the disorder,non-linearity and other characteristics of its energy consumption data,the internal Chaos factor was mining,and the slope of the least square line was fitted to get the maximum Lyapunov index of 0.0029.The Chaos-SVR prediction model was established to judge the existence of Chaos characteristics,and the error was corrected by Markov chain.RMSE and MAPE decreased by 0.3413 and 0.1692,respectively.The accurate prediction results provide a reliable data source for the development of energy saving optimization strategy.The power consumption of air conditioning,lighting and baggage transfer systems in the terminal is optimized by the distributed collaborative Insect Intelligence algorithm,and the energy saving effect is verified by the power consumption intensity of the terminal.