| Variable air volume(VAV)system is an energy saving,comfortable and regulation convenient air conditioning system,and the study on energy-saving and regulation technology of VAV system is benefit for improving its application level and has important meaning for promoting building energy efficiency.Due to building thermal inertia,diffusion and heat distribution of indoor thermal mass in different distance and control strategies,the dynamic response of indoor temperature lags behind the change of system regulation variable such as supply air volume,supply air temperature and air handle unit(AHU)valves position etc.In addition,indoor temperature response time is different for different regulation variables,which may affect the setting of control period,control strategy,control logic and control algorithm.Therefore,indoor temperature time-delay problem is one of key fundamental problems for saving-energy and efficient control of air condition system.Because single terminal control result could affect operating performance of the whole system and there is coupling relationship between indoor temperature control loop and supply air static pressure control loop,decoupling control is also an inevitable key problem for saving-energy and prediction control after solving indoor temperature time-delay control of VAV terminal.From the perspective of control theory,dynamic regulation process of VAV system is a typical nonlinear process of multi-variable,strong coupling,large time delay and large inertia,and it is hard to build its accurate mathematical model.As a result,it is difficult to slove VAV system control problem based on classical and modern control methods,but intelligent control methods such as fuzzy control and neural network have greater advantage,especially that Internet of things technology has been widely used in the building today.Therefore,study on intelligent predicition control for VAV terminal and the whole system will be achieved on the background of saving-energy and optimazion operation and reguation in this paper.Meanwhile,indoor temperature time-delay characteristic is studied and analyzed which is considered as the fundament,and neural network on-line learning technology is brought for solving corresponding intelligent prediction control problem which is considered as the goal.Detailed work is as follow.Firstly,in consideration of the deheating(or reheating)weight coefficient,this paper presents a dynamic response mathematical model of indoor temperature variation based on energy conservation principle,and corresponding time-delay reponse factors are analyzed.Then,dynamic response characteristic of indoor temperature on reglation variables is studied through experiment study.On basis of experimental result,transfer function model of indoor temperature on regulation variables is built,and the experiment theory formula for transfer function characteristic parameter is supplied according to ventilation rate variable quantity.Based on the ventilation rate variable quantity,the inverse proportional relationship formula is given between the inertia time coefficient of indoor temperature variation and the ventilation rate of air conditioning room.According to open-loop experimental results in VAV air conditioning system test platform,it can be concluded that the inertia time coefficient of indoor temperature on terminal damper position is 68?98/(ventilation rate variation),and corresponding inertia time coefficient of indoor temperature on terminal damper position is 92?176/(ventilation rate variation).The inertia time coefficient of long-distance thermal energy transfer process of indoor temperature versus supply air fan speed is greater than that of short-distance thermal energy transfer process of indoor temperature versus terminal damper position.Secondly,aiming at that it is difficult to decscribe pure time-delay response characteristic of indoor temperature on regulation variables based on classical mathematic model,Elman network structure which could satisfy indoor temperature prediction control requirement is studied and analyzed,utilizing that it is easy to identify input layer variable number and hidden layer has ability to record historical information based on internal feedback dynamically.This paper presents on-line optimization method for Elman network model parameter,and corresponding prediction model is studied by simulation study.Results indicate if the first-order derivateive of system output is brought into network input layer,Elman network multipl-step perdiction model could obtain better indoor temperature predciton result under the condition that prediction step number equals to the rate of delay time to sampling time,which could meet precision requirement of indoor temperature prediction control in engineering application.Thirdly,because it is difficult for existing building automation system to achieve Elman network prediction control study which is considered as a critical technical problem,the distributed fieldbus network architecture is designed according to function requirements of indoor temperature prediction control.Then,the development of hardware and software for VAV control system is achieved in order to satisfy requirements of experimental study and engineering application for Elman network multi-step prediction control.Fourthly,this paper presents indoor temperature prediction control method for VAV terminal based on proposed Elman network multi-step prediction model in order to improve operating performance of VAV terminal indoor temperature control loop.By comparative experimental study with tranditional cascade PI algorithm,effectiveness and availability of proposed indoor temperature prediction control method is validated.Results indicate that proposed control method could not rely on pressure-independent terminanl air velocity sensor to measure terminal actual supply air volume,and VAV terminal device is simplified.By selecting control period of indoor temperature control loop reasonably,the change time of terminal damper could be decreased 44%(in winter)or 70%(in summer)and displacement distance of terminal damper could be decreased 65%(in winter)or 42%(in summer),which will benefit for improving the stability of terminal indoor temperature control loop and the whole system.Finally,in order to decrease intercoupling influence of terminal indoor temperature control loop and supply air static pressure control loop,this paper presents a fuzzy control method for variable static pressure based on terminal prediction damper position combined with proposed indoor temperature prediction control method.By comparative experimental study,the effectiveness and energy efficiency of the proposed fuzzy control method is validated.Results indicates that if control period of indoor temperature control loop equals to pure delay time of indoor temperature on supply air volume,the proposed fuzzy control method could improve the stability of indoor temperature control loop and supply air fan control loop,which could save 10%(in winter)and 30%(in summer)energy consumption of air fan by selecting fuzzy basic domain reasonably. |
PDF Full Text Request