Terminal Device Predictive Control Strategy Of Air Conditioning Systems For Office Buildings

With years of development,the use of air-conditioning systems in China's buildings has become increasingly widespread.The buildings of the last century has gradually turned into old buildings,and various technologies have been restricted,resulting in high energy consumption in buildings,which has limited the sustainable development of China's economy.In terms of energy density,the energy consumption of office buildings in China is more than four times higher than that of residential buildings.This energy consumption phenomenon runs counter to the modern concept of sustainable development.In the total energy consumption of buildings,the energy consumption of air-conditioning systems accounts for 60%.In devices of air-conditioning systems,the energy consumption of air-conditioning terminal equipment accounts for nearly 30%,so research on energy-saving optimization control strategies for air-conditioning terminals has considerable economic benefits.In recent years,domestic construction operators have paid more and more attention to the energy saving of the terminal devices of air conditioning systems.Due to the complex structure of the air conditioning system,nonlinear characteristics,and strong loop coupling,the system will be affected by many uncertain factors during the operation.The traditional PID control method can't achieve good control effect,because the PID can't achieve the solution of coupling,and can't achieve energy-saving optimization control.In addition,due to the long time constant and hysteresis characteristics of the controlled object,PID control will lead to poor dynamic characteristics of the system,resulting in uncomfortable indoors.On the other hand,although the energy efficiency of the system can be improved by energy-saving retrofit,it will lead to an increase in the investment in hardware of the air-conditioning system.Even if the energy-saving retrofit is carried out,if the control strategy is improper,the energy-saving effect will be poor due to the influence of the region and climate.Therefore,while optimizing the equipment and structure of the air conditioning system,the temperature and humidity of the air conditioning area should be adjusted with better control algorithms to achieve both the comfort requirement and the energy saving.The predictive control optimization algorithm can realize the decoupling control,and at the same time,the rolling optimization can take control measures in advance according to the system running trend,so that the delay problem can be handled.In addition,energy consumption and comfort are used as optimization performance indicators to achieve energy-saving control.Therefore,this paper studies the predictive control strategy of the air-conditioning end system of office buildings,and applies it to the energy-saving optimization control of the air-conditioning unit of an office building conference hall in Guangzhou,in order to save energy as much as possible while satisfying the comfort of indoor personnel.Predictive models,rolling optimization,and online correction are the three main components of predictive control.The controlled object was modeled by mechanism modeling method.The model is usually more complex and the calculation is too large to be easy to implement.Therefore,based on the law of conservation of energy,this paper derives the simplified mechanism model of air handling unit,collects the parameter variable data of the model,and obtains the discretization model of the air-handling unit by using the first-order backward difference method.In this paper,the optimal control strategy of the air handling unit is derived by using the variational method.The artificial neural network is used as the predictive controller,and the state quantities of the supply air temperature,the indoor temperature,and the indoor air moisture content and the set values of the three state quantities are used as the input of the controller.The air flow rate of the fan and the chilled water flow rate are used as the output of the controller.The predictive controller relies on the artificial neural network to optimize the nonlinear system,and takes the comfort of the indoor personnel and the energy consumption of the equipment as the optimization performance indicators and sets different weight coefficients,which is used as the correction target of the neural network weight and threshold.In order to achieve the rolling optimization of the system,the state of the terminal of the air conditioning system can reach the set value and reduce the energy consumption more quickly.The MATLAB is used to simulate the predictive control system of the research object.According to the state quantity and the control quantity,the optimization performance index of the system is determined,and the neural network controller is used to optimize the performance index.The different weights in the performance index are simulated and compared.For different weight coefficients,the state quantity adjustment time of the system is different,but both have faster response speed and can reach the set value of the system.It is shown through comparison with the PID control that the neural network predictive control strategy adopted in this paper can make the state of the terminal of the air-condition system keep up with the change of set value,and it can save energy consumption on air-handling units to 7.1%.This paper analysis and calculates the energy efficiency index of the air conditioning system,and obtains the energy efficiency ratio of the air conditioning system,the energy efficiency ratio of the cold machine operation,the energy efficiency ratio of the cold station operation,the chilled water transport coefficient,and the actual cooling water transport coefficient of a building in Guangzhou.The possible problems of the air conditioning system are analyzed according to the calculation of energy efficiency indicators.According to the simulation results,the energy efficiency ratio of the air conditioning unit of the air treatment unit was calculated to be 9.76,which is greater than the limit value of the energy efficiency ratio of the air conditioner terminal.The predictive control optimization algorithm adopted in this paper can realize energy-saving optimization control on the terminal of air conditioner,with short adjustment time,small overshoot,stable control effect,and energy saving of 7.1%.It has a good application prospect in the field of air conditioning system optimization control.