Research On Energy Consumption Management System And Energy Conservation Strategy Of Public Buildings Based On Internet Of Things

The Internet of things and cloud data technology provide an effective way for the monitoring,management and feedback of building energy consumption.With traditional methods focus on building energy saving,intelligent electrical appliances,energy-saving equipment such as direct different energy saving method,comprehensive energy consumption of an intelligent information management system should not only realize the collection,management and analysis of energy consumption data,but also for building interior environment monitoring data,thus more effective and more reliable ways to manage the real energy consumption situation and the internal environment quality and display,and through the intelligent algorithm for rule between comfort and building energy consumption prediction and analysis,the excavation of the object construction energy conservation potential,find the balance between the degree of indoor comfort and energy consumption,and to achieve energy conservation and emissions reduction and the environment is comfortable and pleasant.This paper focuses on energy consumption collection,management and analysis of large public buildings.1)The front-end hardware part of the energy consumption management system is designed and built,mainly including the design of data acquisition node and data collection terminal.The design of data acquisition node includes its DSP controller,wireless communication module,data acquisition module selection,and the design of main circuit and peripheral circuit.The design of the data-set end includes the selection of its high-performance processor and the porting of the Linux operating system.2)Of the energy management system to establish information platform architecture and WEB software parts,mainly including software platform architecture planning and design of network architecture,the establishment of the database,the choice of cloud server and connection,and the design of man-machine interface,realize the energy management system and a software part of the operation.3)Based on NAR artificial neural network algorithm,the prediction of building energy consumption was realized.Through the study of the improvement of the traditional BP artificial neural network to get the NAR neural network,and establish the NAR mathematical model and simulation model of neural network,and by processing the measured data input model and completes the parameters and the sample training,implementation is based on the measured data data to predict the future,and according to the results and error evaluation of its validity and reliability is verified.4)According to the two main objectives of building energy saving,comfort and energy saving and emission reduction,a multi-objective optimization strategy is proposed.Based on fuzzy algorithm and pareto multi-objective optimization method,and has set up a about thermal comfort and energy consumption level of multi-objective optimization model,the simulation and analysis of the results,obtained the building thermal comfort and energy consumption level of the optimal interval,which meet the temperature and humidity of the environment is comfortable and reduce energy consumption at the same time interval,only through the adjustment of the central air conditioning and heating equipment are keeping building environment temperature and humidity range comfort,energy saving can be realized.