Design And Research With University’s Energy Monitoring System

In recent years, high energy consumption is one of the most serious problemsfacing our country, and energy-saving measures are urgently needed. At present, wecan easily find scientific effective energy saving measures to reduce unnecessaryenergy consumption using the energy monitoring systems. While the university is thecradle of culture community college talent, it accounts for a large proportion of theenergy consumed. Therefore, it not only saves energy consumption, but also plays avital role in the construction of environment-friendly society of the establishment ofuniversity energy consumption monitoring system.Based on the related technology, this paper builds a university energyconsumption monitoring system, in which Fourier analysis and clustering algorithmare combined to predict the energy consumption values and achieve early warningsystem functions by comparison with the actual values. The main work is summarizedas follows: Firstly introduces research background energy monitoring system, currentresearch, and the significance of university energy consumption monitoring system,which provides a theoretical foundation for the later research of universities energymonitoring system, and makes a detailed description of energy-related technologiesand methods for monitoring system involved; Secondly, it demonstrates about thedesign process of university energy consumption monitoring system, including thesystem architecture, functionality, etc. Finally, this paper puts forward an energyconsumption warning methods to predict energy consumption buildings using someprobability situation cycle energy values, and apply it to the actual warning module.The main innovation of this paper is to use the method combining clustering anddiscrete Fourier analysis to predict the energy consumption of buildings: Firstly,identifying the energy consumption patterns using clustering algorithms with the historical data collected and computing the consumption cycle in the pattern throughDiscreet Fourier Transformation. Then obtaining the distribution function accordingto the statistics of the cycle frequency energy value of discrete points within thedistribution period, which is achieved by SPSS analytical tools to determine theprobability of the situation within the period of discrete points of energy consumptionvalues. At last, comparing with the actual data to determine the value of buildingenergy consumption. In case the actual value exceeds the predictive value of a certainrange, an alarm to alert the administrator about the abnormal energy consumption data,so as the administrator to find the cause of the exception and solve these problems,and reduce energy waste and save energy.