| With the development of society and economy,the demand for electricity and energy is rising,whereas the problem of low energy coefficient of utilization is becoming more and more prominent.It is an unavoidable and important research topic for power system and energy-consuming enterprises,which need to strengthen energy saving and emission reduction and increasing efficiency.To help enterprises to improve the scientific and intelligent use of energy,based on the enterprises' energy data collection,statistics,analysis and digging to forecast the enterprises' future energy consumption accurately,and accordingly to this forecast to optimize the enterprises' energy use,which has a more far-reaching practical significance.This paper first to overall outlines the background and significance of electric power demand side optimization of energy consumption Decision Support System,and the research status of the system is summarized.Meanwhile,analyses the existing problems of energy consumption optimization of demand side.Then,the related technologies of data mining and decision support are introduced,and emphatically discussed SVM and SVR algorithms.Then,the characteristics of typical industry energy consumption and the architecture and technology used in energy consumption data collection subsystem are analyzed.Through analyzing and processing energy consumption data of a steel enterprise and selecting typical input features,using SVR algorithm model to conduct the energy consumption forecast and the demand response energy consumption forecast respectively,the feasibility of the model is verified by simulation results;then,the process of DR is presented.Finally,based on the above research,expound the design philosophy of this system,and the system's architecture was designed and functions were implemented.This system has played the role of decision support for energy consumption optimization of the demand side users,which has obtained good results in the test and initial application. |
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