CN108361923A - The prediction technique of central air-conditioning water returning temperature stationary value - Google Patents
The prediction technique of central air-conditioning water returning temperature stationary value Download PDFInfo
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- CN108361923A CN108361923A CN201810173519.8A CN201810173519A CN108361923A CN 108361923 A CN108361923 A CN 108361923A CN 201810173519 A CN201810173519 A CN 201810173519A CN 108361923 A CN108361923 A CN 108361923A
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
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Abstract
A kind of prediction technique of central air-conditioning water returning temperature stationary value, regard the pipeline of the air conditioner end equipment in each user room as with water supply line in parallel, to which return water temperature to be regarded as to the weighted average of each air conditioner end equipment return water temperature, the size of weight is decided by the flow of end-equipment pipeline, and the prediction model of return water temperature is established according to Newton's law of cooling.By acquiring return water temperature sample, the parameter in model is found out using steepest descent method, and thus obtain the stationary value of return water temperature.The beneficial effects of the present invention are：1. avoiding return water temperature oscillation caused by control；2. to provide foundation by flow control return water temperature；3. being adapted to different environment temperatures.
Description
Technical field
The present invention relates to a kind of prediction techniques of central air-conditioning water returning temperature stationary value.
Background technology
Central air conditioner system be using chilled water, cooling water and refrigeration machine complete whole building energy exchange, according to
Peak load is designed and selects equipment.In fact, the central air-conditioning most of the time all runs under low load condition, have
When even run the 10% or less of Design cooling load, but the energy consumed leads to the significant wastage of energy reducing therewith.
The progress of technology promotes the miniaturization and functionization of frequency converter, people start with frequency converter come control air-conditioning system water pump and
Wind turbine, to reduce the energy waste of central air conditioner system.Traditional central air-conditioner control method is by acquiring water circulation system
Pressure difference and temperature, using programmable controller to water pump carry out PID (ratio, differential, integral) adjusting control.PLC can be real
Now simple logic function can generate certain energy-saving effect, and PID control principle is simple, easy to use, and price also compares
It is relatively inexpensive, but the adjustment factor of PID regulator is adjusted by hand by veteran commissioning staff, once it is determined that being exactly a fixation
Value, cannot with the variation of controlled environment adjust automatically.And in fact, central air conditioner system is the dynamical system of a time-varying
System, operating condition are inseparable with many factors such as flow of the people in weather conditions, building material, building, are at any time
Variation.Therefore, the control method of static parameter is not suitable for the Energy Saving Control of central air conditioner system.In addition, PLC can only be real
The now simple control function of single parameter, when for controlling the such many reference amounts of central air conditioner system, nonlinear time-varying height coupling complexity
When system, system concussion is easily caused so that control temperature changes in wide range, has not only influenced the stability of system but also has dropped
The low comfort of air-conditioning system.For the deficiency of PID control method, some producers propose some and are based on artificial intelligence technology
Control method, wherein more representational is central air-conditioning energy fuzzy control method.The control method is mainly simulated
The thoughtcast of the mankind, when a skilled operator encounter operating mode variation the case where, by itself brain thinking judge,
It provides controlled quentity controlled variable and carrys out control system.Such as when worker has found that chilled water supply backwater temperature difference less than some setting value, can select to drop
The frequency of low refrigerating water pump；And when chilled water supply backwater temperature difference is more than the frequency that some setting value then selects increase refrigerating water pump.Center
Air conditioner energy saving fuzzy control method mainly simulates the thoughtcast of the mankind to control central air conditioner system, and tradition
PID approach is compared, and is more in line with the complexity, dynamic and ambiguity of central air-conditioning, can be reached and more accurately be controlled than PID
Effect processed.But fuzzy control method is the fuzzy rule built according to the abundant practical experience and thought process of expert, central hollow
The control of tune is not only related with supply backwater temperature difference, also closely related with environment temperature, even veteran expert also can not
Corresponding controlled quentity controlled variable can be provided under so much state of a control, keep control effect exactly accurate.In addition, the foundation of rule base needs
A large amount of experimental data is relied on, if these experimental datas derive from individual central air conditioning systems, it is difficult to ensure that other
Central air conditioning system is still effective, therefore these experimental datas can only be obtained from controlled central air conditioning system.
This means that central air conditioning system needs to initially set up control rule base before use, it is contemplated that rule base it is complete
Property, this method is actually unworkable.The Artificial Intelligence Control of another central air-conditioning is ANN Control side
Method matches Energy Saving Strategy by artificial neural network to the processing advantage of non-linear complex data, ensureing application performance
So that central air conditioner system is run always with optimum efficiency under the premise of demand, achievees the purpose that reduction system total energy consumption.But nerve
Network method is required for the experimental data for collecting central air conditioner system to be controlled in advance to need later as fuzzy control method
Neural network is trained according to these experimental datas.Equally, it is contemplated that the integrity demands of experimental data, to obtain in this way
One group of data and run central air conditioner system be it is infeasible, still more accordingly train neural network be even more one extremely take
Thing.A kind of key tactics of central air conditioner system intelligent control are the predictions according to the stationary value of return water temperature to flow
Implement control.
Invention content
Standard operating parameter in view of central air conditioner system handpiece Water Chilling Units is 7 DEG C, 12 DEG C of return water of water supply, Energy Saving Control
Key is to understand the increase and decrease of load in time, but since temperature control system is Correction for Large Dead Time System, becomes in the increase and decrease of load
Return water temperature is slowly changing after change, and return water temperature at this time can not reflect the increase and decrease of load, if according to reality
When return water temperature central air conditioner system is controlled, necessarily return water temperature is caused to shake.Most efficient method is exactly that basis is returned
Return water temperature stationary value after the increase and decrease of coolant-temperature gage prediction load.
Since the pipeline of the air conditioner end equipment in each user room is in parallel with water supply line, return water temperature is practical
On can regard the weighted average of each air conditioner end equipment return water temperature as, the size of weight is decided by the stream of end-equipment pipeline
Amount.And according to Newton's law of cooling, with T and ToIndicate that the leaving water temperature and environment temperature of end-equipment, λ indicate cooling system respectively
Number, thenSolution Newtonian Cooling equation can obtain T=c1e-λt+c2, wherein c1, c2For constant.It is possible thereby to
To the prediction model of return water temperature
By acquiring return water temperature sample T1, T2..., Tm, the parameter in model can be found out using steepest descent method
C0,C1,C2,…,CnAnd λ1,λ2..., λn, and thus predict the stationary value of return water temperature.
The beneficial effects of the present invention are：
1. avoiding return water temperature oscillation caused by control；
2. to provide foundation by flow control return water temperature；
3. being adapted to different environment temperatures.
Description of the drawings
Fig. 1 is the N-S flow charts that parametric technique is determined in return water temperature prediction model.
In figureExpression formula be provided below.
Specific implementation mode
7 DEG C of central air-conditioning water supply, 12 DEG C of return water are the standard operating parameters of handpiece Water Chilling Units, and the major producer in the whole world passes through nothing
Most appropriate, the most economical duty parameter that several Test Summaries goes out.In the case where supply water temperature is certain, return water temperature and flow
Related, flow is bigger, and return water temperature is lower；And flow is related with the energy consumption of central air conditioner system, flow is bigger, and energy consumption is also got over
Greatly.In order to realize the energy-saving run of central air conditioner system, flow must be as small as possible, but flow is too small, and supply backwater temperature difference increases,
It can exceed that supply backwater temperature difference standard；In addition, when supply backwater temperature difference increases, while fan coil cold declines, wind turbine
The dehumidification ability of coil pipe is also decreased obviously, and leads to the degree of comfort decreased of air-conditioned room.Therefore, the situation certain in supply water temperature
Under, the energy-saving run of central air conditioner system is exactly by adjusting flow control return water temperature, supply backwater temperature difference being made to remain same
One numerical value.Appropriate in system design, configuration, under the premise of unit is normal, the principal element for influencing supply and return water temperature is load
Increase and decrease or the variation of cooling condition, including the variation of ambient air temperature.Due to the pipe of the air conditioner end equipment in each user room
Road be it is in parallel with water supply line, therefore return water temperature can essentially regard as each air conditioner end equipment return water temperature weighting it is flat
, the size of weight is decided by that the flow of end-equipment pipeline, the open and close of a certain end-equipment lead to the increase and decrease of load, finally
Influence the variation of return water temperature.Certainly, the factor for influencing return water temperature also has the variation of ambient air temperature.It is fixed according to Newtonian Cooling
Rule, with T and ToIndicate that the leaving water temperature and environment temperature of end-equipment, λ indicate cooling ratio respectively, then
Solution Newtonian Cooling equation can obtain
T=c1e-λt+c2
Wherein c1, c2For constant.If the leaving water temperature of another end-equipment is T'=c1'e-λ't+c2', two end-equipments
Flow is respectively v and v', and the leaving water temperature after mixed flow is
The situation of two end-equipments is generalized to n, then return water temperature is represented by
Here it is the prediction models of return water temperature.
Using time Δ t as the sampling period of temperature, respectively in moment t1=Δ t, t2=2 Δ t ..., tm=m Δ t, are adopted
Collect return water temperature T1, T2..., Tm, and rememberThen obtain equation group
Wherein C0,C1,C2,…,CnAnd x1,x2..., xnWait for that rational method, value should be such that above-mentioned equation group sets up.
To find out this 2n+1 parameter, object function is constructed
In this way, the problem of seeking return water temperature prediction model parameters, which has just been changed, seeks parameter C0,C1,C2,…,CnAnd x1,x2...,
xn, object function F is made to reach the optimization problem of minimum value.Using steepest descent method, first ask F about the partial derivative of a parameter：
Given C0,C1,C2,…,CnAnd x1,x2..., xnAn initial value C0',C1',C2',…,Cn' and x1',x2' ...,
xn' and 0 ＜ ε ＜ 1 of allowable error, note parameter vector β=(C0',C1',C2',…,Cn', x1',x2' ..., xn'), it calculates F and exists
The negative normal direction at the places β '
Step-length γ is determined by linear search technique0So that
With β+γ0α is constantly repeated the above process instead of β, until F (β) ＜ ε.At this point, enabling
(C0,C1,C2,…,Cn, x1,x2..., xn)=β
It utilizesIt can obtain λi=-lnxi/ Δ t, thus just obtains the prediction model of return water temperature
WithIndicate that variable assignments symbol, the solver of return water temperature prediction model parameters are：
1) input model parameter n, temperature sampling period Δ t, temperature sampling number m, temperature samples T1, T2..., Tm, error
0 ＜ ε ＜ 1, β=(C0',C1',C2',…,Cn', x1',x2' ..., xn')；
If 2) F (β) >=ε, turn 4)；
3)
Step-length γ is determined by linear search technique0So thatTurn
2)；4) output prediction model parameters (C0,C1,C2,…,Cn, x1,x2..., xn)=β；
5) λ is calculatedi=-lnxi/ Δ t (i=1,2 ..., n)；
6) prediction model is exported
Enabling t →+∞ just obtain loads increasing or decreasing in prediction model leads to the stationary value C of return water temperature0。
Claims (2)
1. a kind of prediction technique of central air-conditioning water returning temperature stationary value, it is characterised in that：Joined with n return water temperature prediction models
Number, Δ t indicate that the sampling period of return water temperature, m indicate the number of samples of return water temperature, T1, T2..., TmIt is illustrated respectively in sampling
Moment t1=Δ t, t2=2 Δ t ..., tm=m Δs t acquires return water temperature, C0,C1,C2,…,CnAnd λ1,λ2..., λnExpression waits for
Rational method, t indicate the time, then return water temperature obtains prediction model and is
2. the prediction technique of central air-conditioning water returning temperature stationary value according to claim 1, it is characterised in that：NoteThen by sampling instant t1=Δ t, t2=2 Δ t ..., tm=m Δs t acquires to obtain return water temperature T1, T2..., TmIt can obtain
Equation group
Note
Then
Note
WithIndicate that variable assignments symbol, the solver of return water temperature prediction model parameters are：
1) input model parameter n, temperature sampling period Δ t, temperature sampling number m, temperature samples T1, T2..., Tm, 0 ＜ ε of error
＜ 1, initial value vector β=(C of given parameters0',C1',C2',…,Cn', x1',x2' ..., xn')；
If 2) F (β) >=ε, turn 4)；
3)
Step-length γ is determined by linear search technique0So thatTurn 2)；
4) output prediction model parameters (C0,C1,C2,…,Cn, x1,x2..., xn)=β；
5) λ is calculatedi=-lnxi/ Δ t (i=1,2 ..., n)；
6) prediction model is exported
Enabling t →+∞ just obtain loads increasing or decreasing in prediction model leads to the stationary value C of return water temperature0。
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| CN104898426A (en) * | 2015-05-18 | 2015-09-09 | 河海大学常州校区 | Room temperature loop control method based on gradient descent method and generalized prediction control |
| WO2016143510A1 (en) * | 2015-03-06 | 2016-09-15 | Mitsubishi Electric Corporation | Air-conditioning system and system and method for controlling an operation of an air-conditioning system |
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2018
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103322645A (en) * | 2013-06-13 | 2013-09-25 | 浙江工业大学 | Predictive control method for return water temperature of chilled water of central air-conditioner |
| WO2016143510A1 (en) * | 2015-03-06 | 2016-09-15 | Mitsubishi Electric Corporation | Air-conditioning system and system and method for controlling an operation of an air-conditioning system |
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