Research On The Management Of Building Networking Equipment And Its Energy Use Behavior Analysis

With the advance of Internet of Things(IoT),big data,artificial intelligence and other advanced technologies,building IoT has been developing rapidly.How to efficiently manage the equipment connected with the IoT and how to learn the energy use behavior of the equipment are the key to realize the energy saving and people-oriented operation in the smart building,and thus to meet the personalized needs of equipment management.Based on the building IoT platform developed by the research team,this paper mainly focuses on two parts of the IoT equipment management and analysis of equipment energy use behavior.Firstly,this paper introduces the architecture of the building equipment IoT system.Through the analysis of the composition of the architecture,the requirements of equipment access,data collection,storage and management,as well as the service and application of equipment management are studied.Secondly,by analyzing the functional flow and data flow of the IoT equipment management system,research and implement common IoT equipment classification,modeling and registration;design real-time and historical database models of system to achieve unified storage and management of equipment data and operation logs;analyze the application functions of the virtualized management of the IoT equipment space,and complete the visual display and management of the equipment.Then,based on the operation history data and operation logs of the equipment,the Markov Chain Monte Carlo(MCMC)algorithm is used to train the energy use model of the equipment,and the energy consumption behavior of the equipment is analyzed and predicted.This paper uses the operating data of office electrical equipment commonly used in desktop computers,water dispensers,lighting and air conditioning for training,and the energy use model of the equipment built by metropolis-Hastings(MH)method in MCMC algorithm is used to get the energy use behavior law of the equipment according to the results.Among them,the python platform is used for simulation,and the results are compared with the maximum likelihood estimation algorithm(MLE).According to the simulation results,the method has higher accuracy.Finally,the IoT equipment management system APP was developed,and the building IoT system developed by the team was used as the experimental platform for system deployment and experiments,which verified the reliability of the system,and analyzed the behavior of the researchers based on the energy use behavior of equipment.The research results basically meet the needs of building IoT equipment management and refined equipment energy use management.