Research On Enterprise Electric Energy Efficiency Management Method Based On Iot Technonlogy

Electric energy efficiency management,a key measure for enterprises to save energy and reduce consumption,reduce operating costs,and improve competitiveness,has become a hot issue of all-social enterprises.However,due to the lack and poor level of enterprise electric energy efficiency management systems and electric energy efficiency management methods,there are always the problems of high energy consumption and low profit in enterprises.Therefore,in order to reduce the energy consumption of enterprises and improve the energy efficiency level,the methods of enterprise electric energy efficiency management were studied in this thesis.The main research contents are as follows:The enterprise electric energy efficiency management collection terminal based on DSP(Digital Signal Processing)and ARM(Advanced RISC Machines)was designed.Aiming at the current issues of single acquisition,low real-time and poor accuracy in electric energy efficiency management collection terminal,a dual-core design based on DSP and ARM was proposed.The collection terminal could complete high-speed and real-time collection of environmental parameters such as temperature,condensation,smoke and so on,as well as electrical parameters such as voltage,cur,ent,power,etc.At the same time,it realized long-distance transmission of parameters through GPRS.With the advantages of high stability,comprehensive functions and real-time efficiency,it satisfied the requirements of real-time data and accuracy of electric energy efficiency management.The enterprise electric energy efficiency evaluation indicator system was established.Combined with the expert and enterprise opinions while considering the characteristics of enterprise electricity consumption,the initial energy efficiency evaluation indicator system was built to pre-process and normalize the indicators,and the Principal Component Analysis(PCA)and Correlation Analysis(CA)were used to calculate the constituent coefficients of the indicators as well as related other coefficients.With analyzing the coefficient ratio and the correlation degree among the indicators,the indicators were screened,and finally a scientific,reasonable,and highly representative electric energy efficiency evaluation indicator system were formed.The fuzzy and comprehensive evaluation model for enterprise electric energy efficiency was constructed.The indicator weights at all levels was obtained by using the hierarchical weighting model.The subjective weighting method,G1 method,was used to determine the weight of the first-level indicator,and the objective weighting method,Improved Entropy Weight,was used to determine the weight of the second-level indicator.Then,the final evaluation result of enterprise electrical efficiency was obtained and quantified through the fuzzy comprehensive evaluation method.Finally,the scores of the overall and each subsystem of energy efficiency of enterprises were obtained and the characteristics of enterprises power consumption were found and the optimization objectives were determinedThe enterprise electric energy efficiency optimization model was built.Based on the maximization of enterprise investment benefit and the results of enterprise power efficiency assessment,and combined with the weights of each indicator and the applicability of optimization technology,the candidate schemes were sorted by calculating the single objective optimization cost-effectiveness and multi-objective optimization cost-effectiveness.Finally,an efficient and reasonable enterprise optimization scheme was selected,which considered not only the energy saving but also the investment efficiency of the enterprise,and improved the enthusiasm of the enterprise for effective power optimizationThe prototype system for enterprise electric energy efficiency management was developed.The system function and work flow were analyzed,and JavaScript language and MySQL database were used to develop a prototype system,which verified the effectiveness of the proposed methods and model.