| With the increasing number of buildings in China,the resulting high energy consumption of buildings has become increasingly serious.With the requirements of carbon peaking and carbon neutrality,China pays more attention to and emphasizes the energy efficiency of public buildings.Central air conditioning systems account for a large proportion of energy consumption in the whole building operation process,and play an important role in building energy efficiency.At present,the main reasons for the high energy consumption of the central air conditioning system are: large equipment design selection,equipment running under partial load for a long time,lack of effective control of the system and other aspects.Therefore,it is very important to develop a reasonable control strategy for the central air conditioning system to reduce energy consumption.The central air conditioning system of a large shopping mall in Shenyang is the research object of this project.Based on the Sverige BECH software,the building’s basic thermal parameters,the form of the refrigeration system and the actual operation are modeled,and the time-by-time cold load during the cooling period of the building is obtained;the inertia weights and learning factors of the traditional particle swarm algorithm are improved comprehensively,and five benchmark functions are selected to test the improved adaptive weight particle The results show that the improved adaptive weight particle swarm algorithm has made significant progress in convergence speed and accuracy;the main factors and variables affecting the energy consumption of the central air-conditioning system are analyzed,and the mathematical model of energy consumption including chiller units,chilled water pumps,cooling water pumps and cooling towers is established.The optimal load rates of the refrigeration mainframe are 0.4 and0.7 respectively;combined with the actual configuration of the parallel multi-unit central air conditioning system in the shopping mall,the control strategy of system start/stop is further compared and analyzed,and the optimized start/stop control strategy has a better energy saving effect under the premise that the building load is satisfied,and the maximum energy saving rate can reach 8.5% when the load rate is 0.7.Based on the above analysis,the optimal control strategy of central air conditioning system parameters based on particle swarm algorithm is proposed.The TRNSYS module of centrifugal chiller,circulating water pump and cooling tower is established based on the mathematical model of each equipment,and the dynamic simulation platform of central air conditioning system is set up;the simulation results of variable frequency pump,variable chilled water temperature control and parameter optimization control strategy based on particle swarm algorithm are compared and analyzed,and the results show that all three control strategies can achieve the purpose of energy saving of air conditioning system,but the particle swarm based algorithm-based parameter optimization control strategy has the best energy-saving effect on the chiller unit,with the energy-saving rate reaching 8.8%,and the energy-saving rate of both chilled water pump and cooling water pump is about 74%,which can save 152,000 yuan of electricity bill during the cooling period,with significant economic benefits,and has certain demonstration significance for similar energy-saving transformation projects. |
