Research On Optimization Of Central Air Conditioning Air Handling System Based On Improved Multi Objective Particle Swarm Optimization

With the improvement of people’s living and working environment quality requirements,central air conditioning is widely used in large buildings.When the central air conditioning system is running,it will produce a lot of energy consumption.Because it includes many interrelated equipment,parameters are coupled with each other,and there are many heat exchange links,how to create a comfortable environment and reduce the total consumption of air conditioning under different loads is of great significance.For this reason,the main work of this paper is as follows:Firstly,this thesis introduces the composition and working principle of the central air conditioning air handling system in detail,and analyzes six factors that affect the thermal comfort.The energy consumption model of chilled water pump and fan of air conditioning unit is established.Secondly,according to the average voting index of thermal environment prediction,the comfort of users in the air-conditioned room is characterized,a thermal comfort model was established by using the percentage of dissatisfied thermal environment prediction as an index.Then,the improved multi-objective particle swarm optimization algorithm is used to solve the multi-objective optimization problem of central air conditioning air handling system.In order to solve the problems of insufficient search ability and poor diversity in the standard multi-objective particle swarm optimization algorithm,the worst position of individual particles is proposed as the reflection part,the particle velocity update method is changed,and the method of nonlinear adjustment of inertia weight is proposed to ensure that the inertia weight decreases nonlinearly with the increase of iteration times,it makes the large-scale search at the beginning of the iteration and the small-scale accurate search at the end of the iteration.The test function is used to verify the pareto frontier of the improved MOPSO algorithm,and the average and standard deviation of the inverse generation distance are used to compare the performance indexes of the three algorithms,experiments show that the improved multi-objective particle swarm optimization algorithm has better performance.Finally,a multi-objective optimization model of central air conditioning air handling system is established by minimizing the total energy consumption and the percentage of unsatisfied thermal environment prediction,the constraint conditions of various process parameters are defined.The multi-objective problem of the central air conditioning air handling system is verified by the experimental platform.Under different process parameters,the standard multi-objective particle swarm optimization algorithm and the improved multiobjective particle swarm optimization algorithm are respectively applied for comparative verification.The multi-objective optimization of central air conditioning air handling system is carried out through the experimental platform.Nondominated sorting genetic algorithm Ⅱ algorithm,multi-objective immune algorithm,standard multi-objective particle swarm optimization algorithm and improved multi-objective particle swarm optimization algorithm are applied to compare and verify under different process parameters.The experiment shows that the improved multi-objective particle swarm optimization algorithm can meet the high requirements of users’ thermal comfort and reduce the energy consumption of the system.