Research On Energy Saving Optimization Strategy For Central Air Conditioning Water System

With the rapid development of China’s economy,more and more large public buildings are being put into use.In order to meet people’s requirements for living and working comfort,central air conditioning is gradually used in public buildings such as shopping malls,office buildings,hospitals,and stations,etc.The energy saving of the central air conditioning system is of great significance to the energy saving of public buildings.The central air conditioning system has several sub-equipment,and the input and output of each equipment are interrelated and have a strong coupling.The energy saving control of the central air conditioning system cannot only control the energy consumption of a single sub-equipment to reach the optimum,but also needs to target the lowest energy consumption of the whole system,which puts forward higher requirements for the optimization strategy.The purpose of this paper is to study the energy saving optimization strategy of central air conditioning water system.First,the classification of central air conditioning systems and related technologies are analyzed,and the working process of central air conditioning water systems is studied.The working process requires the various sub-equipment of central air conditioning to cooperate with each other and perform multiple cycles in order to complete the heat exchange between indoor and outdoor.Next,the control strategies of each sub-equipment of the central air conditioning water system are explored,and the energy consumption of each sub-equipment of the central air conditioning water system is analyzed and calculated.The physical constraints of central air conditioning and the constraints between each equipment are determined,so that the energy consumption model of the central air conditioning water system is established.Based on the energy consumption model of the central air conditioning water system,the gray wolf algorithm is optimized for the disadvantages of poor population diversity and slow convergence at the later stage.A chaotic nonlinear distance weight improved gray wolf optimization algorithm is proposed,which optimizes the initial population by chaotic mapping and improves the gray wolf algorithm by using nonlinear convergence factors and distance weights.To test the effectiveness of the gray wolf optimization algorithm with chaotic nonlinear distance weights.The algorithm is compared with genetic algorithm,particle swarm algorithm and gray wolf algorithm,while the test function of international standard is solved by finding the optimal solution.The results show that the gray wolf optimization algorithm based on chaotic nonlinear distance weights has better convergence speed and effectiveness.Finally,the central air conditioning water system simulation model was established using TRNSYS software and the hourly average temperature data of a typical day in Hangzhou area was output by Meteonorm,a meteorological data analysis software.The energy saving optimization of the energy consumption of the central air conditioning water system simulation model is achieved by using MATLAB programming to realize the functions of different intelligent optimization algorithms.In this paper,four different intelligent optimization methods are used to test the optimization of the central airconditioning water system simulation model established in TRNSYS software.It is concluded that all four optimization methods can find the optimal energy consumption values for their respective algorithms.The proposed chaotic nonlinear distance-weighted gray wolf optimization algorithm found the lowest value of the four algorithms 14 times within a typical day of 16 hours of search time.System energy consumption fluctuates even less,within 14.99 KW for large temperature difference and large load,and within 0.25 KW for small temperature difference and small load.The simulation results show that the improved gray wolf optimization algorithm is more stable and has better energy saving effect.