Optimization Research Of Public Building Energy Consumption Monitoring System Based On Internet Of Things

In recent years, with the rapid development of the national economy, excessive energy consumption phenomenon emerges in endlessly. Due to haze, dust storms and other severe weather occur frequently, the work of energy saving and emission reduction is imminent. The research of the project is based on the original building energy management platform of Xi’an University of Architecture and Technology, and the foothold is to build a public building energy consumption monitoring system. The ultimate goal is to improve the function of the system, to optimize the system structure, to increase the stabilization of the system, to tap the potential of energy-saving. So this paper carried out the following four research work.Firstly, we apply the Internet of Things to the field of building energy conservation research and on this basis we design a public building energy consumption monitoring system. The system is composed of four subsystems: data collection, data transmission, data storage and information publication. Among of the system, the data collection and transmission network is a kind of wireless sensor networks which is built by using Zig Bee and GPRS wireless communication technology. The technology optimizes the original system, and it saves a lot of manpower and material resources.Secondly, this project completes the hardware and software design of intelligent terminal collection node and coordination gateway node. And then, it sets up a small wireless collection and transmission system of energy consumption in a laboratory environment, and the power data is successfully transmitted to the upper computer.Thirdly, this thesis establishes an energy consumption prediction model based on GM-BP neural network, and verifies the reliability and error analysis of the model on the basis of the original platform historical energy consumption data. The result shows that the combined model has better accuracy and applicability than a single gray model and BP neural network model. The study of energy consumption prediction is helpful to the establishment of energy-saving planning and energy strategy, and it optimizes the functional requirement of the original energy consumption monitoring system.Finally, the MCGS configuration software is used to design the interface of public building energy consumption monitoring system, and it provides many functions, such as the real-time monitoring, information query, announcement, energy consumption publicity, data reporting, energy consumption prediction, management reporting. The monitoring interface is friendly and convenient, full-featured, easy to promote in the same system.