Research On Energy Management System And Energy Consumption Prediction In Science And Technology Center

With the rapid development of China's economy,the development of industrial enterprises is changing with each passing day,accordingly,the energy consumption of industrial enterprises is also increasing,which has brought tremendous pressure on energy supply and environmental protection.Therefore,as the general headquarters of China National Heavy Duty Truck Group,the Science and Technology Center must build an energy management system which aimed at improving the energy management level of enterprises.Raising energy efficiency and reducing energy waste are important ways to achieve energy conservation,emission reduction and sustainable development.The implementation of the energy management system provides an important basis for the level of energy refinement management.This thesis first introduces the relevant theories of energy management system and energy consumption prediction.Taking the energy management of the Science and Technology Center in China National Heavy Duty Truck Group as the research object,based on the field investigation of the park,combined with the actual energy demand of the park,the data acquisition was established.The flow path of the energy management is Data collection-Centralized data-system control-data analysis and processing-providing various types of comparative assessment methods,and carry out targeted design and implementation of the park's energy management system.Using the energy consumption data of the Science and Technology Building for 3 years as a time series data sample,the first step is to predict the future energy consumption of the technology building based on the historical energy consumption of the technology building through the Winters addition and multiplication model;the second step is to technology The time series of the building energy consumption is decomposed,and then the ARIMA prediction model is established for the decomposed seasonal adjustment sequence SA.Then,the correlation between the seasonal factor and the accumulated temperature of each month is made to determine the accumulated temperature of each month in the technology building.The consumption has deeper effects,and a regression model is established to predict the energy consumption of seasonal factors,and the predicted values of the two models are added to obtain the energy consumption of the heavy-duty science and technology building based on the time series decomposition method;Model diagnosis and accuracy evaluation,and select the optimal model.the results of this thesis study are shown to be within acceptable limits.Based on the daily energy consumption data of the experimental office building which collected through the energy management system,a daily regression prediction model of the experimental office building was established.The main influencing factors of the energy consumption of the experimental office building are analyzed.Two models for predicting daily energy consumption are established based on multiple regression analysis and SVM algorithm to predict the short-term power consumption of the experimental office building.Comparing the two models,it is found that the prediction accuracy of the multiple regression analysis model is higher.In short,by establishing an energy management system and energy consumption prediction model,the future energy consumption can be predicted.So that the management can rationally allocate and utilize various types of energy,control energy consumption more accurately;tap the energy saving potential of the technology center.and combine technical energy-saving measures;to effectively reduce energy consumption.