CN112944599A - Multi-parameter coupling control method and device of air conditioning system - Google Patents
Multi-parameter coupling control method and device of air conditioning system Download PDFInfo
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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
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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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
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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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
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
- F24F11/85—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using variable-flow pumps
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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
- F24F11/88—Electrical aspects, e.g. circuits
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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
- F24F2140/00—Control inputs relating to system states
- F24F2140/20—Heat-exchange fluid temperature
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
Abstract
The invention provides a multi-parameter coupling control method and a multi-parameter coupling control device for an air conditioning system, wherein the method comprises the following steps: acquiring the real-time load rate of the air conditioning system; determining control parameters of the air conditioning system according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system; and controlling the air conditioning system according to the determined air conditioning system control parameter. The invention solves the limitation of the single variable feedback control of the current air conditioning system, improves and improves the control efficiency of the air conditioning system, reduces the energy consumption of the system, realizes the coupling control of various parameters of the air conditioning system and guides the air conditioning system to regulate and control.
Description
Technical Field
The invention relates to a control technology, in particular to a multi-parameter coupling control method and a multi-parameter coupling control device for an air conditioning system.
Background
At present, in public buildings in China, a heating, ventilating and air conditioning system is the most important energy consumption equipment, and the operation energy consumption of the heating, ventilating and air conditioning system can account for 50% -60% of the energy consumption of the buildings. In a general air conditioning system, an air conditioning cooling source system is in a central position. According to relevant statistics, in a typical centralized air-conditioning system, the energy consumption of an air-conditioning cold source system, namely a refrigerator, a chilled water pump, a cooling tower and other equipment, can occupy 60% -80% of the whole air-conditioning system in summer cooling seasons.
In the prior art, the control method of the air conditioning system mainly adopts univariate feedback control as a main part, and mainly adopts two control modes in the aspect of cold source control: differential pressure control and return water temperature control. The pressure difference control means setting a pressure difference value of a water supply and return pipe of the chilled water, simultaneously monitoring the pressure difference of two ends of the chilled water supply and return water in real time, comparing the pressure difference with a pressure difference set value, and enabling the pressure difference of two ends of the water supply and return pipe to be close to the set value by adjusting the frequency of a water pump. And the backwater temperature control means setting the backwater temperature of the water chilling unit, simultaneously monitoring the temperature value of a backwater pipe of the air conditioning system in real time, comparing the temperature value with the set backwater temperature, and enabling the backwater of the system to be close to the backwater temperature set value by adjusting the outlet water temperature of the water chilling unit.
The processes in the prior art are all feedback control of single variable, and a process of continuous iteration, comparison and control exists, and the process has the inherent characteristic of response delay, so that the energy-saving effect of an air conditioning system is limited, and the energy-saving range is limited.
Disclosure of Invention
In order to improve and improve the control efficiency of the air conditioning system and reduce the energy consumption of the system, the invention provides a multi-parameter coupling control method of the air conditioning system, which comprises the following steps:
acquiring the real-time load rate of the air conditioning system;
determining control parameters of the air conditioning system according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system;
and controlling the air conditioning system according to the determined air conditioning system control parameter.
In the embodiment of the present invention, the method further includes: predetermining a control strategy according to historical data of the air conditioning system; wherein the content of the first and second substances,
acquiring historical data of an air conditioning system;
establishing an air conditioning system operation initial model by using a machine learning algorithm;
performing model training and optimizing on the air conditioning system operation initial model by using the historical data to determine model parameters so as to generate an air conditioning system operation model;
and according to the generated air-conditioning system operation model, determining the corresponding relation between the load factor and the control parameters of the air-conditioning system as a determined control strategy by taking the lowest energy consumption or the highest energy efficiency of the air-conditioning system as a target condition.
In an embodiment of the present invention, the performing model training and optimizing on the initial model of the air conditioning system operation by using the historical data to determine model parameters to generate the model of the air conditioning system operation includes:
performing model algorithm training on the initial model of the air conditioning system operation by using a part of the historical data;
performing model optimization by using the historical data, the optimization algorithm and preset constraint conditions of the rest parts;
and iteratively performing model algorithm training and model optimization to determine model parameters and generate an air conditioning system operation model.
In the embodiment of the present invention, the historical data includes: historical energy consumption data of the air conditioning system, historical load data of the air conditioning system and historical control parameters of the air conditioning system;
the air conditioning system operation initial model comprises the following steps: the relationship between the energy consumption of the system and the operation parameters or the relationship between the energy efficiency of the system and the operation parameters.
In the embodiment of the present invention, the air conditioning system control parameters include: the outlet water temperature of the chilled water, the inlet water temperature of the cooling water, the flow rate of the chilled water pump and the flow rate of the cooling water pump.
Meanwhile, the invention also provides a multi-parameter coupling control device of the air conditioning system, which comprises the following components:
the data acquisition module is used for acquiring the real-time load rate of the air conditioning system;
the parameter determining module is used for determining the control parameters of the air conditioning system according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system;
and the control module is used for controlling the air conditioning system according to the determined air conditioning system control parameters.
In the embodiment of the present invention, the apparatus further includes: the strategy determining module is used for determining a control strategy in advance according to historical data of the air conditioning system; wherein the policy determination module comprises:
the historical data acquisition unit is used for acquiring historical data of the air conditioning system;
the machine learning unit is used for establishing an air conditioning system operation initial model by utilizing a machine learning algorithm;
the training optimization unit is used for performing model training and optimizing the initial model of the air conditioning system operation by using the historical data to determine model parameters so as to generate an air conditioning system operation model;
and the strategy determining unit is used for determining the corresponding relation between the load factor and the control parameter of the air conditioning system according to the generated air conditioning system operation model by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target condition.
In an embodiment of the present invention, the training optimization unit includes:
the training unit is used for carrying out model algorithm training on the air conditioning system operation initial model by using a part of historical data;
the optimization unit is used for optimizing a model by using the historical data, the optimization algorithm and the preset constraint conditions of the rest parts;
and the iteration processing unit is used for iteratively carrying out model algorithm training and model optimization to determine model parameters and generate an air-conditioning system operation model.
Meanwhile, the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the method when executing the computer program.
Meanwhile, the invention also provides a computer readable storage medium, and a computer program for executing the method is stored in the computer readable storage medium.
The multi-parameter coupling control method and the multi-parameter coupling control device for the air conditioning system solve the limitation of single-variable feedback control of the current air conditioning system, improve and improve the control efficiency of the air conditioning system and reduce the energy consumption of the system. And coupling control of various parameters of the air conditioning system at the same time is realized, and the air conditioning system is guided to regulate and control.
In order to make the aforementioned and other objects, features and advantages of the invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a multi-parameter coupling control method for an air conditioning system according to the present invention;
FIG. 2 is a schematic flow chart of an embodiment of the present invention;
FIG. 3 is a schematic flow chart of an embodiment of the present invention;
FIG. 4 is a flow chart of a system modeling method in an embodiment of the invention;
FIG. 5 is a flow chart of a genetic algorithm in the multi-parameter optimization method disclosed in an embodiment of the present invention;
FIG. 6 is a flow chart of a particle swarm algorithm in the multi-parameter optimization method disclosed in the embodiment of the present invention;
FIG. 7 is a block diagram of a multi-parameter coupling control device of an air conditioning system according to the present invention;
fig. 8 is a block diagram of a multi-parameter coupling control apparatus of an air conditioning system provided in an embodiment of the present invention;
fig. 9 is a schematic diagram of an embodiment of an electronic device provided in an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The control method of the air conditioning system in the prior art is mainly single-variable feedback control, and the single-variable feedback control has a process of continuous iteration, comparison and control and has the inherent characteristic of response delay, so that the air conditioning system has limited energy-saving effect and limited energy-saving amplitude.
To this end, the present invention provides a multi-parameter coupling control method for an air conditioning system, as shown in fig. 1, the method of the present invention includes:
step S101, acquiring a real-time load rate of an air conditioning system;
step S102, determining control parameters of the air conditioning system according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system;
and step S103, controlling the air conditioning system according to the determined air conditioning system control parameter.
The multi-parameter coupling control method of the air conditioning system, disclosed by the invention, is characterized in that according to the load efficiency in the actual data of the air conditioning system, the aim of the highest overall energy efficiency and the lowest energy consumption of the air conditioning cold source system is fulfilled in advance, and the obtained control strategy is determined by combining multi-parameter optimization control and is used as the parameter input for guiding the actual operation of the air conditioning system, and is preset in the corresponding control system, so that the system can be self-regulated and operated according to the preset parameters and the real-time load rate of the air conditioning system.
The method of the present invention further comprises: predetermining a control strategy according to historical data of the air conditioning system; i.e. according to
In the embodiment of the invention, according to the actual operation parameters of the air conditioning system: indoor and outdoor environmental parameters and running parameters of cold source systems (such as a water chilling unit, a freezing water pump, a cooling water pump and a cooling tower) are combined with a physical mechanism model of the system by adopting a machine learning algorithm to establish an actual running model of the system.
In this embodiment, a mathematical relationship between system energy consumption and system operating parameters and a mathematical relationship between system energy efficiency and system operating parameters are established. According to the characteristics of group intelligence and group optimization of the biological world, a group intelligence optimization algorithm is adopted by combining an actual operation energy consumption/energy efficiency model of the air conditioning system, and a plurality of operation parameters of the actual operation model of the air conditioning system, such as the inlet and outlet water temperature of a water chilling unit, the flow of a freezing water pump and a cooling water pump, the frequency of a cooling tower fan and the like, are optimized to obtain control parameter values.
In the embodiment of the invention, the predetermining the control strategy according to the historical data of the air conditioning system comprises the following steps:
acquiring historical data of an air conditioning system;
establishing an air conditioning system operation initial model by using a machine learning algorithm;
performing model training and optimizing on the air conditioning system operation initial model by using the historical data to determine model parameters so as to generate an air conditioning system operation model;
and determining the corresponding relation between the load factor and the control parameter of the air conditioning system according to the generated air conditioning system operation model by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target condition.
In this embodiment, the overall energy efficiency of the air-conditioning cold source system is highest, and the energy consumption is lowest, and the parameter optimization result obtained after the multi-parameter optimization control is combined is used as a predetermined control strategy, and is input as a parameter for guiding the actual operation of the air-conditioning system, and is preset in the corresponding control system, so that the system performs self-regulation and operation according to the predetermined parameter according to the control strategy.
The following describes a scheme of the present invention in detail with reference to a specific embodiment of the present invention, where the embodiment provides a multi-parameter coupling control method for an air conditioning system, as shown in fig. 2, the steps involved in the embodiment include:
a system modeling 10 for modeling the air conditioning system based on actual data;
and the coupling control 30 is used for coupling control of various operation parameters of the air conditioning system at the same time by combining the highest energy efficiency and the lowest energy consumption of the system, and guiding the air conditioning system to regulate and control.
In this embodiment, the air conditioning system monitoring parameters include: indoor and outdoor environmental parameters, water chilling unit state parameter, water chilling unit operation data, water chilling unit energy consumption data, heat pump unit state parameter, heat pump unit operation data, heat pump unit energy consumption data, air conditioning unit state parameter, air conditioning unit operation parameter, air conditioning unit energy consumption data, water pump state parameter, water pump operation parameter, water pump energy consumption data, cooling tower state parameter, cooling tower operation parameter, cooling tower energy consumption data, user side data include: indoor actual temperature value, indoor temperature set value, indoor personnel quantity, indoor equipment quantity, power and other data.
As shown in fig. 3, the multi-parameter coupling control method for an air conditioning system provided in this embodiment specifically includes the following steps:
the system modeling 10 specifically includes: analyzing mechanism models of each device of the air conditioning system and a system formed by the devices, analyzing physical laws of the devices, combining a machine learning algorithm, and establishing a system optimal model by using monitored system operation data, wherein the method comprises the following steps: and determining multi-control parameter output of the system by using the model with the lowest energy consumption and the highest energy efficiency.
The machine learning algorithm used for system modeling in the embodiment is a generalized linear regression algorithm and a parameter identification method based on a least square method.
The multi-parameter optimization 20 includes: determining a target function, a constraint condition and a group intelligent algorithm;
the method comprises the steps of determining an optimized target (function) of the system according to an actual operation energy consumption model and an energy efficiency model of the air conditioning system, and simultaneously combining controllable parameters of an actual control system, wherein the controllable parameters mainly comprise the inlet and outlet water temperature of a water chilling unit, the flow rates of a freezing water pump and a cooling water pump, and the frequency of a cooling tower fan;
the algorithm optimization is carried out on the control parameters by adopting a swarm intelligence algorithm, and the swarm intelligence algorithm adopted in the embodiment mainly comprises a genetic algorithm and a particle swarm algorithm, so that the numerical values of the control parameters are obtained.
The coupling control 30 includes: determining the overall control target of the air conditioning system, wherein the specific implementation of the invention mainly takes the highest system energy efficiency and the lowest energy consumption as the targets, and the specific numerical values of the system control parameters are generated by combining the multi-parameter optimization process;
and transmitting the control parameters to a control system on the actual site, and presetting the control parameters to be written into a centralized processing unit of the control system to be used as a control instruction of the actual control system.
Fig. 4 is a system modeling flowchart of a multi-parameter coupling control method for an air conditioning system according to the present embodiment.
For a general air conditioner cooling source system, a mechanism model of the air conditioner system needs to be analyzed firstly: the method comprises the steps of analyzing the correlation influence relationship between the energy consumption of the air conditioning system and one or more of the factors such as the inlet and outlet water temperature of chilled water, the inlet and outlet water temperature of the chilled water, the flow of a chilled water pump, the flow of a cooling water pump, the fan frequency of a cooling tower and the like, determining the basic framework and the structure of an energy consumption mathematical model of the air conditioning system, and determining an empirical formula of the air conditioning system containing unknown parameters.
Taking a core device in an air conditioning system, namely a water chilling unit as an example, an empirical formula for determining energy consumption (power) and influence parameters in the embodiment of the invention is as follows:
Pchiller＝a0+a1(tci-teo)+a2(tci-teo)2+a3Qch+a4Qch 2+a5(tci-teo)Qch (1)
in the formula:
Pchillerpower of the refrigerating unit, kW;
Qchload of the refrigerating unit, kW;
tcithe inlet temperature of cooling water is in DEG C;
teothe temperature of the outlet water of the chilled water is lower than DEG C;
a0～a5and a preset unknown coefficient.
Since the empirical formula (1) contains a large number of unknown key parameters, the specific values of the coefficients in the model are obtained by a machine learning method and a parameter identification method in combination with actual operation data of the air conditioning system. According to the embodiment of the invention, the generalized linear regression algorithm and the least square parameter identification algorithm in the machine learning algorithm are used, and the cross validation thought is combined at the same time to carry out system modeling. The basic process is as follows: the original data set is divided into two categories, one category is training data used for training an algorithm model, and the other category is verification data.
That is, in the embodiment of the present invention, determining an operation model of an air conditioning system specifically includes:
performing model algorithm training on the initial model of the air conditioning system operation by using a part of the historical data;
performing model optimization by using the historical data, the optimization algorithm and preset constraint conditions of the rest parts;
and iteratively performing model algorithm training and model optimization to determine model parameters and generate an air conditioning system operation model.
Randomly initializing parameter values in the mathematical model (1), adopting the model (1) for samples in each training data, comparing the obtained power value with an actual power value, and calculating an error; and updating the model parameters by adopting a least square method aiming at the errors. And continuously iterating samples in the training data to finish data training, performing model verification on the verification data, and obtaining a real model of the system if the verification error is within an allowable range.
In this embodiment, a flowchart of a system modeling method is shown in fig. 4.
Through a system modeling process, an actual mathematical model of energy consumption and energy efficiency of the air conditioning system is established, and based on the model, control parameters are optimized in the next step.
In this embodiment, from the actual operation analysis of the air conditioning system, the main controllable parameters of the air conditioning system are mainly as follows: the outlet water temperature of the chilled water, the inlet water temperature of the cooling water, the flow rate of a chilled water pump, the flow rate of a cooling water pump, the fan frequency of a cooling tower and the like. Before system optimization, an objective function and a constraint condition must be determined.
In this embodiment, for the air conditioner cold source system, the objective function is the total energy consumption of the system; and the related constraint condition setting is determined according to the system equipment parameters and the operation conditions, and the objective limit condition and the equipment performance constraint which are required to meet the actual operation are met.
The functions of the air conditioner cold source system established in this embodiment are as follows:
pcold-source＝pchiller+peo-pump+pci-pump+ptower (2)
in the formula:
pcold-sourcethe total energy consumption of a cold source system is in kW;
pchillerthe unit kW is the energy consumption of the refrigerating unit;
peo-pumpthe energy consumption of a freezing water pump is kW;
pci-pumpthe energy consumption of a cooling water pump is in kW unit;
ptoweris the energy consumption of a cooling tower in kW.
The relation between the energy consumption of the chilled water pump, the cooling water pump and the cooling tower and the inlet water temperature of the chilled water, the flow rate of the chilled water pump, the flow rate of the cooling water pump and other factors is also a mathematical function containing unknown parameters, and the mathematical function is similar to the expression of the formula (1), and is not listed in the embodiment one by one.
In a general air conditioning system, the outlet water temperature of chilled water can be set through a control main interface of a refrigerating unit, and the essence of the system is to change the opening degree of a guide vane of the refrigerating unit, so that the outlet water temperature of the chilled water of the refrigerating unit is a controllable parameter; the inlet water temperature of the cooling water can be realized by adjusting the frequency of a cooling fan through a cooling tower in a general cooling tower system. The flow of the chilled water pump and the flow of the cooling water pump can be adjusted through the frequency converter, the rotating speed of a motor of the water pump is adjusted, the flow of the cooling tower can be adjusted through the frequency converter, and the frequency of the cooling tower can be adjusted through the frequency converter. Therefore, in this embodiment, the determined controllable parameters of the air conditioning system are as follows: the flow rate of the chilled water pump, the flow rate of the cooling water pump and the frequency of the cooling tower fan.
The objective function and the control parameter of the optimization control in this embodiment are determined, and the optimization range of the optimization parameter needs to be further determined below, in the actual engineering, the range of the optimization parameter is as follows:
5℃≤Teo≤13℃ (3-1)
20℃≤Tci≤33℃ (3-2)
0.6V0≤V≤V0 (3-3)
30Hz≤f≤50Hz (3-4)
in the above formula:
Teothe outlet water temperature of the chilled water of the refrigerating unit is DEG C;
Tcithe outlet water temperature of the cooling water of the refrigerating unit is DEG C;
V0is rated flow of water pump, m3H; v is the actual flow of the water pump, m3/h；
f is the cooling tower operating frequency, Hz.
The problems to be solved in the embodiments of the present invention belong to a multi-objective optimization problem: there is an objective to be achieved (often expressed functionally) and there are several constraints under which an optimal solution of this objective is required.
In order to solve the multi-objective optimization problem of optimization control, a genetic algorithm and a particle swarm algorithm are used as main algorithms for optimization solution in the embodiment of the invention.
(1) The genetic algorithm principle is as follows:
in nature, the natural law of excellence and disadvantage is followed: the living environment is worse and more limited, the resources are more and more limited, the struggle between organisms inevitably exists, only the organism individuals winning in the struggle for survival can survive, and the organism individuals are considered to be high in adaptability and are the result of the natural optimization.
The genetic algorithm references the evolution law in the natural world, namely the survival of the suitable person and the selection-elimination law, the evolution law is integrated into the genetic algorithm, and when the problem is solved, random search is adopted in a local solution space to search for a relatively good result, so that the global solution space is gradually expanded, and the global optimal solution is found out in a step-by-step optimization manner.
Fig. 5 is a flow chart of optimization using genetic algorithm in the multi-parameter optimization method in the embodiment of the present invention.
The following formula is a mathematical model of the typical Genetic Algorithm SGA (Simple Genetic Algorithm, SGA):
SGA＝SGA(C,fitvalue,P0,N,Φ,Γ,Ψ,T) (4)
in the formula: c is an individual encoding method;
the fitvalue is an individual fitness function;
P0is an initial population;
n is the size of the population;
phi is a selection operator;
gamma is a crossover operator;
Ψ is a mutation operator;
and T is a genetic algorithm convergence condition.
The pseudo code of the genetic algorithm in the present embodiment is as follows according to the principle of the genetic algorithm.
(2) The principle of particle swarm algorithm:
in the sport of bird populations, there are some basic rules of operation: when looking for food, one individual in the group feels sensitive to food, namely, some related information of the food is mastered, so that the groups can communicate with each other and transmit information, and finally, one individual guides the whole group to find the food.
The food in the bird group movement is equivalent to the optimal solution in the multi-objective optimization problem, the food searching mode of the bird group provides a good idea for the optimization algorithm, namely heuristic search, and a global optimization technology is formed on the basis of the heuristic search, namely the principle of the particle swarm algorithm.
Fig. 6 is a flowchart illustrating optimization by using a particle swarm optimization in the multi-parameter optimization method in the embodiment of the present invention.
The following equation is a mathematical model of a typical Particle Swarm Optimization (PSO):
PSO＝PSO(fitvalue,P0,m,w,c1,c2,T) (5)
in the formula: the fitvalue is a fitness function;
P0is an initial population;
m is the initial population scale;
w is the inertial weight;
c1and c2Is the acceleration coefficient;
and T is a particle swarm algorithm convergence condition.
According to the particle swarm optimization principle, the pseudo code of the established multi-parameter optimization control parameter problem is shown as follows.
The embodiment of the invention discloses a multi-parameter coupling control method for an air conditioning system, which is a flow chart for implementing multi-parameter coupling control according to the embodiment of the application as shown in fig. 6.
In the example of the present invention, the multi-parameter optimization control under the condition of lowest energy consumption or highest energy efficiency of the air conditioning system is considered, that is, the initial model for the operation of the air conditioning system includes: the relationship between the energy consumption of the system and the operation parameters or the relationship between the energy efficiency of the system and the operation parameters.
The energy consumption minimum optimization objective function of the air conditioning system in the embodiment is changed into:
min(pcold-source)＝min(pchiller+peo-pump+pci-pump+ptower) (6)
or the optimal objective function with the highest energy efficiency of the air conditioning system is as follows:
max(COPcold-source)＝max(Q/(pchiller+peo-pump+pci-pump+ptower)) (7)
in the formula:
pcold-sourcethe total energy consumption of a cold source system is kW;
pchillerenergy consumption of a refrigerating unit is kW;
peo-pumpenergy consumption of a freezing water pump is kW;
pci-pumpthe energy consumption of a cooling water pump is kW;
ptowerthe energy consumption of the cooling tower is kW;
q is the system load, kW.
According to the optimization problem under the condition of lowest energy consumption or highest energy efficiency of the system, combining a multi-parameter optimization algorithm, the air conditioner control parameter value under a specific load rate can be obtained, as shown in the following table 1:
TABLE 1 optimization results of control parameters targeting minimum energy consumption or maximum energy efficiency of the system at specific load rates
Each row in the table is regarded as a control strategy, the control strategy is preset and written into an air conditioner control system, and when the system meets a certain load rate, a control command is executed according to the preset control strategy, so that the control parameters are executed according to the preset parameters, and the multi-parameter coupling control method in the embodiment of the invention is realized. The load rates in the table of the embodiment of the present invention are only examples and are not limited in sequence, and the optimization control method of the present invention is suitable for any load rate. The control parameters corresponding to each load rate may be set into the control system as a control strategy.
Compared with the existing air conditioning system control method, the method has the following effects:
by utilizing the technical invention, the modeling of the air conditioning system by utilizing the actual operation data can be realized, and compared with the method which only depends on a physical model, the method can better fit the actual operation condition of the system and can better reflect the operation rule of the system. By the modeling method, the air conditioning system is regulated and controlled more stably and safely.
The invention reasonably optimizes the influencing variables in the system by adopting a coupling control method with multiple control parameters, and overcomes the delay defect compared with the traditional single-point feedback control. By the optimal control parameter and the coupling control method, the air conditioning system is more energy-saving in regulation and control.
The multi-parameter coupling control of the ground source heat pump system is used for further explaining the multi-parameter coupling control of the air conditioning system.
The area of the project air-conditioning area is about 3 ten thousand square meters, the tail end of the project air-conditioning area is provided with a ceiling radiation and fresh air replacement system, cold and heat sources of the system are 2 ground source heat pump units and 1 water chilling unit, and main equipment parameters of the project are shown in table 2. In the ground source heat pump composite system, a ground source heat pump unit and a water chilling unit in summer provide chilled water at the temperature of 7 ℃/12 ℃ and respectively supply the chilled water to a fresh air unit (the chilled water at the temperature of 7 ℃/12 ℃ is directly utilized) and a ceiling radiation plate replacement (the chilled water at the temperature of 18 ℃/20 ℃ is replaced by the ceiling radiation system by the plate replacement); the ground source heat pump unit in winter provides hot water at 35 ℃/30 ℃ and supplies the hot water to a fresh air unit (directly using the hot water at 35 ℃/30 ℃) and a ceiling radiation plate exchanger (the ceiling radiation system uses the hot water at 28 ℃/26 ℃ replaced by the plate exchanger) respectively. The switching of the operating conditions in different seasons is realized by switching the valves. And under the working condition of summer, the open cooling tower is opened as required.
TABLE 2 ground source heat pump system equipment parameter table
The operation data of the air conditioning system is monitored and stored in real time, and the project data part is shown in the following table 3.
Table 3 actual operating data
By using the system modeling method provided in the embodiment of the invention, mathematical models of the refrigerating unit, the cooling water pump and the cooling water pump are obtained as shown in tables 4-1 to 4-3 below.
TABLE 4-1 mathematical model of refrigerating unit
TABLE 4-2 model of chilled water pump
TABLE 4-3 Cooling Water Pump model
According to the above model, the cold source system model in this embodiment is determined as follows:
pcold-source＝pchiller+peo-pump+pci-pump (8)
in the formula:
pcold-sourceas a cold source systemTotal energy consumption, kW;
pchillerenergy consumption of a refrigerating unit is kW;
peo-pumpenergy consumption of a freezing water pump is kW;
pci-pumpenergy consumption of a cooling water pump is kW.
Wherein, the cooling tower in the system is a fanless cooling tower, so that no energy consumption is caused.
Thus, the objective function of the optimization control problem of the project in this embodiment is:
pcold-source＝pchiller+peo-pump+pci-pump (9)
subject to the following constraints:
in the formula: t iseoThe outlet water temperature of the chilled water of the refrigerating unit is DEG C;
Tcithe outlet water temperature of the cooling water of the refrigerating unit is DEG C;
Veois the flow rate of chilled water, m3/h；
VciFor cooling water flow, m3/h。
Finally, with the coupling control method of the present embodiment: with the lowest system energy consumption as a target, optimizing multiple control parameters to form a control strategy of the air conditioning system, as shown in table 5:
TABLE 5 Multi-parameter coupling control strategy
The invention can realize the modeling of the air conditioning system by using the actual operation data, and can better fit the actual operation condition of the system and reflect the operation rule of the system compared with the prior art which only depends on a physical model. By the modeling method, the air conditioning system is regulated and controlled more stably and safely. The method adopts a coupling control method with multiple control parameters, reasonably optimizes the influence variables in the system, and overcomes the delay defect of the method compared with the traditional single-point feedback control. By the optimal control parameter and the coupling control method, the air conditioning system is more energy-saving in regulation and control.
The present invention also provides a multi-parameter coupling control device of an air conditioning system, as shown in fig. 7, which includes:
a data obtaining module 701, configured to obtain a real-time load rate of the air conditioning system;
a parameter determining module 702, configured to determine an air conditioning system control parameter according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system;
and the control module 703 is configured to control the air conditioning system according to the determined air conditioning system control parameter.
In the embodiment of the present invention, as shown in fig. 8, the apparatus further includes:
a strategy determination module 704, configured to determine a control strategy in advance according to historical data of the air conditioning system; wherein the policy determination module comprises:
the historical data acquisition unit is used for acquiring historical data of the air conditioning system;
the machine learning unit is used for establishing an air conditioning system operation initial model by utilizing a machine learning algorithm;
the training optimization unit is used for performing model training and optimizing the initial model of the air conditioning system operation by using the historical data to determine model parameters so as to generate an air conditioning system operation model;
and the strategy determining unit is used for determining the corresponding relation between the load factor and the control parameter of the air conditioning system according to the generated air conditioning system operation model by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target condition.
In an embodiment of the present invention, the training optimization unit includes:
the training unit is used for carrying out model algorithm training on the air conditioning system operation initial model by using a part of historical data;
the optimization unit is used for optimizing a model by using the historical data, the optimization algorithm and the preset constraint conditions of the rest parts;
and the iteration processing unit is used for iteratively carrying out model algorithm training and model optimization to determine model parameters and generate an air-conditioning system operation model.
For those skilled in the art, the implementation of the multi-parameter coupling control device of the air conditioning system of the present invention can be clearly understood through the description of the embodiments of the present invention, and will not be described herein again.
The embodiment of the invention also provides electronic equipment which can be a desktop computer, a tablet computer, a mobile terminal and the like, and the embodiment is not limited thereto. In this embodiment, the electronic device may refer to the embodiments of the method and the apparatus, and the contents thereof are incorporated herein, and repeated descriptions are omitted.
Fig. 9 is a schematic block diagram of a system configuration of an electronic apparatus 600 according to an embodiment of the present invention. As shown in fig. 9, the electronic device 600 may include a central processor 100 and a memory 140; the memory 140 is coupled to the central processor 100. Notably, this diagram is exemplary; other types of structures may also be used in addition to or in place of the structure to implement telecommunications or other functions.
In one embodiment, the multi-parameter coupling control function of the air conditioning system may be integrated into the cpu 100. The central processor 100 may be configured to control as follows:
acquiring the real-time load rate of the air conditioning system;
determining control parameters of the air conditioning system according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system;
and controlling the air conditioning system according to the determined air conditioning system control parameter.
In the embodiment of the present invention, the method further includes: predetermining a control strategy according to historical data of the air conditioning system; wherein the content of the first and second substances,
acquiring historical data of an air conditioning system;
establishing an air conditioning system operation initial model by using a machine learning algorithm;
performing model training and optimizing on the air conditioning system operation initial model by using the historical data to determine model parameters so as to generate an air conditioning system operation model;
and determining the corresponding relation between the load factor and the control parameter of the air conditioning system according to the generated air conditioning system operation model by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target condition.
In an embodiment of the present invention, the performing model training and optimizing on the initial model of the air conditioning system operation by using the historical data to determine model parameters to generate the model of the air conditioning system operation includes:
performing model algorithm training on the initial model of the air conditioning system operation by using a part of the historical data;
performing model optimization by using the historical data, the optimization algorithm and preset constraint conditions of the rest parts;
and iteratively performing model algorithm training and model optimization to determine model parameters and generate an air conditioning system operation model.
In the embodiment of the present invention, the historical data includes: historical energy consumption data of the air conditioning system, historical load data of the air conditioning system and historical control parameters of the air conditioning system;
the air conditioning system operation initial model comprises the following steps: the relationship between the energy consumption of the system and the operation parameters or the relationship between the energy efficiency of the system and the operation parameters.
In the embodiment of the present invention, the air conditioning system control parameters include: the outlet water temperature of the chilled water, the inlet water temperature of the cooling water, the flow rate of the chilled water pump and the flow rate of the cooling water pump.
As shown in fig. 9, the electronic device 600 may further include: communication module 110, input unit 120, audio processing unit 130, display 160, power supply 170. It is noted that the electronic device 600 does not necessarily include all of the components shown in FIG. 9; furthermore, the electronic device 600 may also comprise components not shown in fig. 9, which may be referred to in the prior art.
As shown in fig. 9, the central processor 100, sometimes referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, the central processor 100 receiving input and controlling the operation of the various components of the electronic device 600.
The memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable media, a volatile memory, a non-volatile memory, or other suitable device. The information relating to the failure may be stored, and a program for executing the information may be stored. And the central processing unit 100 may execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides input to the cpu 100. The input unit 120 is, for example, a key or a touch input device. The power supply 170 is used to provide power to the electronic device 600. The display 160 is used to display an object to be displayed, such as an image or a character. The display may be, for example, an LCD display, but is not limited thereto.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), Random Access Memory (RAM), a SIM card, or the like. There may also be a memory that holds information even when power is off, can be selectively erased, and is provided with more data, an example of which is sometimes called an EPROM or the like. The memory 140 may also be some other type of device. Memory 140 includes buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage section 142, and the application/function storage section 142 is used to store application programs and function programs or a flow for executing the operation of the electronic device 600 by the central processing unit 100.
The memory 140 may also include a data store 143, the data store 143 for storing data, such as contacts, digital data, pictures, sounds, and/or any other data used by the electronic device. The driver storage portion 144 of the memory 140 may include various drivers of the electronic device for communication functions and/or for performing other functions of the electronic device (e.g., messaging application, address book application, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. The communication module (transmitter/receiver) 110 is coupled to the central processor 100 to provide an input signal and receive an output signal, which may be the same as in the case of a conventional mobile communication terminal.
Based on different communication technologies, a plurality of communication modules 110, such as a cellular network module, a bluetooth module, and/or a wireless local area network module, may be provided in the same electronic device. The communication module (transmitter/receiver) 110 is also coupled to a speaker 131 and a microphone 132 via an audio processor 130 to provide audio output via the speaker 131 and receive audio input from the microphone 132 to implement general telecommunications functions. Audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, an audio processor 130 is also coupled to the central processor 100, so that recording on the local can be enabled through a microphone 132, and so that sound stored on the local can be played through a speaker 131.
Embodiments of the present invention also provide a computer-readable program, where when the program is executed in an electronic device, the program causes a computer to execute the multi-parameter coupling control method of an air conditioning system in the electronic device according to the above embodiments.
Embodiments of the present invention also provide a storage medium storing a computer-readable program, where the computer-readable program enables a computer to execute the multi-parameter coupling control of the air conditioning system in the electronic device according to the above embodiments.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings. The many features and advantages of the embodiments are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the embodiments that fall within the true spirit and scope thereof. Further, since numerous modifications and changes will readily occur to those skilled in the art, it is not desired to limit the embodiments of the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope thereof.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The principle and the implementation mode of the invention are explained by applying specific embodiments in the invention, and the description of the embodiments is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A multi-parameter coupling control method of an air conditioning system is characterized by comprising the following steps:
acquiring the real-time load rate of the air conditioning system;
determining control parameters of the air conditioning system according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system;
and controlling the air conditioning system according to the determined air conditioning system control parameter.
2. The multi-parameter coupling control method of an air conditioning system as claimed in claim 1, wherein the method further comprises: predetermining a control strategy according to historical data of the air conditioning system; wherein the content of the first and second substances,
acquiring historical data of an air conditioning system;
establishing an air conditioning system operation initial model by using a machine learning algorithm;
performing model training and optimizing on the air conditioning system operation initial model by using the historical data to determine model parameters so as to generate an air conditioning system operation model;
and according to the generated air-conditioning system operation model, determining the corresponding relation between the load factor and the control parameters of the air-conditioning system as a determined control strategy by taking the lowest energy consumption or the highest energy efficiency of the air-conditioning system as a target condition.
3. The multi-parameter coupling control method of the air conditioning system as claimed in claim 2, wherein the performing model training and optimization on the initial model of the air conditioning system operation by using the historical data to determine model parameters to generate the model of the air conditioning system operation comprises:
performing model algorithm training on the initial model of the air conditioning system operation by using a part of the historical data;
performing model optimization by using the historical data, the optimization algorithm and preset constraint conditions of the rest parts;
and iteratively performing model algorithm training and model optimization to determine model parameters and generate an air conditioning system operation model.
4. The multi-parameter coupling control method of an air conditioning system as claimed in claim 2, wherein the history data comprises: historical energy consumption data of the air conditioning system, historical load data of the air conditioning system and historical control parameters of the air conditioning system;
the air conditioning system operation initial model comprises the following steps: the relationship between the energy consumption of the system and the operation parameters or the relationship between the energy efficiency of the system and the operation parameters.
5. The multi-parameter coupling control method of the air conditioning system according to any one of claims 1 or 2, wherein the air conditioning system control parameters comprise: the outlet water temperature of the chilled water, the inlet water temperature of the cooling water, the flow rate of the chilled water pump and the flow rate of the cooling water pump.
6. A multi-parameter coupling control device for an air conditioning system, said device comprising:
the data acquisition module is used for acquiring the real-time load rate of the air conditioning system;
the parameter determining module is used for determining the control parameters of the air conditioning system according to the real-time load rate and a predetermined control strategy; the control strategy is a corresponding relation between a predetermined load rate and control parameters of the air conditioning system;
and the control module is used for controlling the air conditioning system according to the determined air conditioning system control parameters.
7. The multi-parameter coupling control device of air conditioning system as claimed in claim 6, wherein said device further comprises: the strategy determining module is used for determining a control strategy in advance according to historical data of the air conditioning system; wherein the policy determination module comprises:
the historical data acquisition unit is used for acquiring historical data of the air conditioning system;
the machine learning unit is used for establishing an air conditioning system operation initial model by utilizing a machine learning algorithm;
the training optimization unit is used for performing model training and optimizing the initial model of the air conditioning system operation by using the historical data to determine model parameters so as to generate an air conditioning system operation model;
and the strategy determining unit is used for determining the corresponding relation between the load factor and the control parameters of the air conditioning system as a determined control strategy according to the generated air conditioning system operation model by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target condition.
8. The multi-parameter coupling control device of air conditioning system as claimed in claim 7, wherein said training optimization unit comprises:
the training unit is used for carrying out model algorithm training on the air conditioning system operation initial model by using a part of historical data;
the optimization unit is used for optimizing a model by using the historical data, the optimization algorithm and the preset constraint conditions of the rest parts;
and the iteration processing unit is used for iteratively carrying out model algorithm training and model optimization to determine model parameters and generate an air-conditioning system operation model.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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