Maintenance And Energy-saving Management Of Mechanical And Electrical Equipments Based On BIM In Subway Station

With the rapid development of Building Information Modeling(BIM)technology,engineering practice is increasingly using modeling method to realize digital construction.The technology has been widely used in architectural design,infrastructure management,etc.In this project,for the first time the technique was applied to mechanical and electrical equipment maintenance system of subway station,combined with the equipment position spatial information and feature information,to improve the equipment maintenance efficiency on the operation stage.This method realizes the visualization and highly efficient of the equipment maintenance management,providing the necessary auxiliary support for the normal operation of the subway equipment,to realize a scientific and digital energy management and to blaze a new way for the future of energy saving management.The subway mechanical and electrical equipment maintenance has a wide variety and distribution,as well as system complexity and difficult maintenance.One of the large energy consumption of the subway station is environmental control system,and the air conditioning system in the environmental control system is the research emphasis in this paper.This project adopts the BIM technology,combined with 3 d modeling and neural genetic algorithm;through a series of calculationsto find a more energy-saving control mode in the subway station air-conditioning system.This paper firstly built environment and equipment operation and maintenance monitoring platform of Nanjing subway station based on BIM.Secondly,elaboration on this platform,mechanical and electrical control system simulation was established and FLUENT software was used for modeling analysis of the influence of related parameters on the energy consumption.Thirdly,energy-hungry air-conditioning equipment was choosed for the energy management research,and put forward the energy saving algorithm based on BP neural network and genetic algorithm.The energy saving system test obtained ideal effect in the field.