Research On Diagnosis Method Of Existing Public Buildings Based On Multi-objective Optimization

In recent years,the development trend of public buildings in our country is that the grade of public buildings is getting higher and higher,the functions are more and more complicated,and the proportion of high-energy-consumption buildings in new buildings is getting higher and higher.The energy consumption per unit area of public buildings in our country increased from 52.2kWh/m~2 in 2001 to nearly 75.3kWh/m~2 in2011.As the largest proportion of building energy consumption,there isn't effective regulation methods in HVAC system's actual operation in public buildings.In the process of energy-saving diagnosis,the energy consumption of HVAC system or a certain equipment is regarded as the single objective.Therefore,this paper aims at the multi-objective diagnosis and optimization of HVAC system in public buildings,and puts forward reasonable suggestions for the follow-up of system operation adaptation and energy-saving renovation.In this paper,firstly,the diagnostic principle of‘separation of demand side and load side,from equipment to system'is put forward by summarizing the research of building energy-saving diagnosis methods at home and abroad.Then,from the perspective of reducing the energy consumption of the HVAC system,improving the system energy efficiency and improving the indoor thermal comfort,a multi-objective diagnostic system for existing public buildings is established.Through summarizing the methods of solving multi-objective problems at present,it is determined that the multi-objective optimization problem is solved by the combination of particle swarm optimization and linear weighting sum method.The diagnostic system has been established is applied to the actual diagnosis of typical buildings,the actual operating status of HVAC system is diagnosed in winter and summer conditions.Finally,the particle swarm optimization algorithm is used to optimize the operating parameters of the key equipment in the system.The optimization results are compared with the measured data to test the reliability of the optimization results.