Design And Implementation Of Central Air Conditioning Group Control System

Central air-conditioning system is widely used in daily production and life.Statistics show that the energy consumption of central air conditioning system usually accounts for 30%-50% of the total energy consumption of large buildings.Therefore,the energy saving of central air-conditioning has great economic benefits.Most of the control methods adopted by conventional central air conditioning system only stabilize the indoor climate index at a set value without considering energy saving.For some systems containing energy-saving control,local energy-saving algorithms are generally adopted.Such algorithms cannot perfectly cope with complex interference during system operation,resulting in reduced user experience and poor overall optimization effect.Therefore,it is necessary to introduce the group control algorithm.In order to design a group control system suitable for the mainstream central air-conditioning system,the following research is done in this paper:(1)There is a large lag between the control quantity input of the central airconditioning system and the cooling load it provides,and the control effect is not good when the current load is used as the basis.Load prediction is needed to get the realtime load demand as possible.Therefore,three commonly used prediction methods in air-conditioning load prediction are selected in this paper: Cubic exponential smoothing method,BP neural network and support vector regression(SVR),using Matlab procedures,inspection of these three methods under the same group data prediction effect,and the results from the quickness,accuracy,practicability,feasibility is analyzed from the four contrast,end up with: the SVR is most suitable for the central air conditioning system of short-term load forecasting method.(2)Due to the strong coupling between devices in the central air-conditioning system,the control strategy based on unit is often difficult to achieve the global optimal control effect.To solve this problem,this paper designs a group control energy saving strategy based on GA-PSO.In this paper,the optimization effect of genetic algorithm(GA)and particle swarm optimization(PSO)alone is analyzed.In this paper,the PSO algorithm is improved to optimize the discrete parameters because it can not directly deal with the discrete parameters when applied in the central air-conditioning system.After summarizing the advantages and disadvantages of the two algorithms according to the results,GA-PSO hybrid algorithm is obtained by combining the two algorithms.After optimizing the data set through Matlab program,GA-PSO is better in the speed and accuracy of the search.(3)The site conditions of the central air conditioning system are changeable and complex,and the addition of large control hosts will increase additional costs,so it is necessary to design a more universal hardware controller.In this paper,the physical simulation model of the system is built on Simulink,and the closed-loop performance of the system software is tested and verified.After that,OPC technology and CAN analyzer were used as information transmission equipment to build a closed-loop system for joint adjustment with external controller,and experiments were designed to verify the optimization effect.Finally,the total energy consumption of the original system could be reduced by 7.368%.