CN116241988A - Multi-parameter coupling control method and device for air conditioning system and electronic equipment - Google Patents

Multi-parameter coupling control method and device for air conditioning system and electronic equipment Download PDF

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Publication number
CN116241988A
CN116241988A CN202310444031.5A CN202310444031A CN116241988A CN 116241988 A CN116241988 A CN 116241988A CN 202310444031 A CN202310444031 A CN 202310444031A CN 116241988 A CN116241988 A CN 116241988A
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air conditioning
conditioning system
model
algorithm
energy consumption
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曹勇
崔治国
毛晓峰
王晨
丁天一
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China Academy of Building Research CABR
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China Academy of Building Research CABR
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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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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 device of an air conditioning system and electronic equipment, belonging to the technical field of control, wherein the method comprises the following steps: acquiring historical data of an air conditioning system; establishing an air conditioning system operation initial model by adopting a machine learning algorithm according to actual operation parameters of the air conditioning system; according to the historical data, performing model training and optimization on an air conditioning system operation initial model by adopting a genetic algorithm or a particle swarm algorithm so as to generate an air conditioning system operation model; according to the air conditioning system operation model, the corresponding relation between the load rate and the air conditioning system control parameter is determined by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target, so as to obtain a control strategy; acquiring a real-time load rate of an air conditioning system; determining control parameters of an air conditioning system according to the real-time load rate and the control strategy; and controlling the air conditioning system according to the control parameters. The invention solves the limitation of the current univariate feedback control of the air conditioning system, improves the control efficiency of the air conditioning system and reduces the energy consumption of the system.

Description

Multi-parameter coupling control method and device for air conditioning system and electronic equipment
Technical Field
The invention relates to the technical field of control, in particular to a multi-parameter coupling control method and device of an air conditioning system and electronic equipment.
Background
At present, in China public buildings, a heating ventilation air conditioning system is the most main energy consumption equipment, and the running energy consumption of the heating ventilation air conditioning system can account for 50% -60% of the energy consumption of the buildings. In a general air conditioning system, an air conditioning cold source system is in the core position. According to related statistics, in a typical centralized air conditioning system, the energy consumption of an air conditioning cold source system, namely equipment such as a chiller, a chilled water pump, a cooling tower and the like, in a cooling season in summer can occupy 60% -80% of the whole air conditioning system.
In the prior art, the control method of the air conditioning system mainly comprises single variable feedback control, and the aspect of cold source control mainly comprises two control modes: differential pressure control and backwater temperature control. The pressure difference control means that the pressure difference value of the water supply and return pipe of the chilled water is set, meanwhile, the pressure difference of the two ends of the water supply and return pipe of the chilled water is monitored in real time, the pressure difference is compared with a pressure difference set value, and the pressure difference of the two ends of the water supply and return pipe is close to the set value by adjusting the frequency of the water pump. The backwater temperature control means that the backwater temperature of the water chilling unit is set, meanwhile, the temperature value of a backwater pipe of the air conditioning system is monitored in real time, and compared with the set backwater temperature, the backwater temperature of the water chilling unit is adjusted, so that the backwater of the system is close to a backwater temperature set value.
The above processes in the prior art are all univariate feedback control, and there is a continuous iterative, comparison and control process, which has the inherent characteristic of response delay, so the energy-saving effect of the air conditioning system is limited, and the energy-saving range is limited.
Disclosure of Invention
In order to improve and increase the control efficiency of an 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 historical data of an air conditioning system;
according to the actual operation parameters of the air conditioning system, an initial model of the air conditioning system operation is established by adopting a machine learning algorithm;
according to the historical data, performing model training and optimizing and determining model parameters on the air conditioning system operation initial model by adopting a genetic algorithm or a particle swarm algorithm so as to generate an air conditioning system operation model;
according to the air conditioning system operation model, the corresponding relation between the load rate and the air conditioning system control parameter is determined by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target, so as to obtain a control strategy;
acquiring a real-time load rate of an air conditioning system;
determining control parameters of an air conditioning system according to the real-time load rate and the control strategy;
and controlling the air conditioning system according to the control parameters.
Optionally, the machine learning algorithm includes a generalized linear regression algorithm and a parameter identification method based on a least square method.
Optionally, according to the historical data, performing model training and optimization to determine model parameters on the air conditioning system operation initial model by adopting a genetic algorithm or a particle swarm algorithm, so as to generate an air conditioning system operation model comprises:
performing model algorithm training on the air conditioning system operation initial model by adopting part of historical data;
performing model optimization by adopting other historical data, an optimization algorithm and preset constraint conditions;
and carrying out model algorithm training and model optimization by adopting a swarm intelligent algorithm iteration to determine model parameters and generate an air conditioning system operation model.
Optionally, the history 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 relation between the energy consumption of the system and the operation parameters or the relation between the energy efficiency of the system and the operation parameters.
Optionally, the optimal objective function with the lowest energy consumption of the air conditioning system is:
min(p cold-source )＝min(p chiller +p eo-pump +p ci-pump +p tower )；
wherein p is cold-source P is the total energy consumption of the cold source system chiller For energy consumption of refrigerating unit, p eo-pump For the energy consumption of the chilled water pump, p ci-pump For cooling water pump energy consumption, p tower And the energy consumption of the cooling tower is reduced.
Optionally, the most energy efficient optimization objective function of the air conditioning system is:
max(COP cold-source )＝max(Q/(p chiller +p eo-pump +p ci-pump +p tower ))；
wherein, COP cold-source For the total energy efficiency of the air conditioning system, Q is the system load, p chiller For energy consumption of refrigerating unit, p eo-pump For the energy consumption of the chilled water pump, p ci-pump For cooling water pump energy consumption, p tower And the energy consumption of the cooling tower is reduced.
Optionally, the control parameters include: chilled water outlet temperature, cooling water inlet temperature, chilled water pump flow and cooling water pump flow.
Meanwhile, the invention also provides a multiparameter coupling control device of the air conditioning system, comprising:
a history data acquisition unit for acquiring history data of the air conditioning system;
the machine learning unit is used for establishing an air conditioning system operation initial model by adopting a machine learning algorithm according to actual operation parameters of the air conditioning system;
the training optimization unit is used for carrying out model training and optimizing and determining model parameters on the initial air conditioning system operation model by adopting a genetic algorithm or a particle swarm algorithm according to the historical data so as to generate an air conditioning system operation model;
the strategy determining unit is used for determining the corresponding relation between the load rate and the control parameters of the air conditioning system according to the air conditioning system operation model and with the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target so as to obtain a control strategy;
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 control parameters of the air conditioning system according to the real-time load rate and the control strategy;
and the control module is used for controlling the air conditioning system according to the control parameters.
Optionally, the training optimization unit includes:
the training unit is used for training a model algorithm of the air conditioning system operation initial model by adopting part of historical data;
the optimization unit is used for performing model optimization by adopting the rest historical data, an optimization algorithm and preset constraint conditions;
and the iteration processing unit is used for carrying out model algorithm training and model optimization by adopting a group intelligent algorithm iteration to determine model parameters and generate an air conditioning system operation model.
Meanwhile, the invention also provides a computer device which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the multi-parameter coupling control method of the air conditioning system when executing the computer program.
Meanwhile, the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program for executing the multi-parameter coupling control method of the air conditioning system.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention solves the limitation of the current univariate feedback control of the air conditioning system, improves and increases the control efficiency of the air conditioning system and reduces the energy consumption of the system. And the coupling control of multiple parameters of the air conditioning system at the same time is realized, and the air conditioning system is guided to regulate and control.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a multi-parameter coupling control method of an air conditioning system of the present invention;
FIG. 2 is a first flow chart of a multi-parameter coupling control method of the air conditioning system according to the embodiment;
FIG. 3 is a schematic diagram of a second flow chart of a multi-parameter coupling control method of the air conditioning system according to an embodiment;
FIG. 4 is a flow chart of a system modeling method in an embodiment of the invention;
FIG. 5 is a flowchart of a genetic algorithm in the multi-parameter optimization method disclosed in the embodiment of the present invention;
FIG. 6 is a flowchart of a particle swarm algorithm in a multi-parameter optimization method disclosed in an 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 device of an air conditioning system according to 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 following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The control method of the air conditioning system in the prior art mainly comprises single-variable feedback control, wherein the single-variable feedback control has a continuous iterative, comparison and control process and has the inherent characteristic of response delay, so that the energy-saving effect of the air conditioning system is limited, and the energy-saving range is limited.
In this regard, the present invention provides a multi-parameter coupling control method for an air conditioning system, as shown in fig. 1, the multi-parameter coupling control method includes:
step S101, acquiring a real-time load factor 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 the corresponding relation between the predetermined load rate and the control parameter of the air conditioning system.
Step S103, controlling the air conditioning system according to the determined air conditioning system control parameters.
According to the multi-parameter coupling control method of the air conditioning system, according to the load efficiency in the actual data of the air conditioning system, the control strategy which is obtained by combining the multi-parameter optimization control and is determined is aimed at the highest overall energy efficiency and the lowest energy consumption of the air conditioning cold source system in advance, and is used as the parameter input for guiding the actual operation of the air conditioning system, and the parameter input is preset into the corresponding control system, so that the system can perform self-adjustment and operation according to the real-time load rate of the air conditioning system according to the preset parameter.
The method of the invention further comprises: and presetting a control strategy according to the historical data of the air conditioning system.
In the embodiment of the invention, according to the actual operation parameters of the air conditioning system: the method comprises the steps of establishing an actual operation model of a system by adopting a machine learning algorithm and combining a physical mechanism model of the system.
In the embodiment, a mathematical relationship between the system energy consumption and the system operation parameters and a mathematical relationship between the system energy efficiency and the system operation parameters are established. And (3) according to the characteristics of group intelligence and group optimization in the biological kingdom, combining with an actual operation energy consumption/energy efficiency model of an air conditioning system, adopting a group intelligent optimization algorithm to optimize a plurality of operation parameters of the actual operation model of the air conditioning system, such as water inlet and outlet temperature of a water chilling unit, flow of a chilled water pump and a cooling water pump, fan frequency of a cooling tower and the like, so as to obtain control parameter values.
In the embodiment of the invention, the predetermined control strategy according to the historical data of the air conditioning system comprises the following steps:
and acquiring historical data of the air conditioning system.
And establishing an air conditioning system operation initial model by using a machine learning algorithm.
And performing model training and optimizing and determining model parameters on the initial air conditioning system operation model by utilizing the historical data 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 rate and the air conditioning system control parameter 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 highest energy efficiency and lowest energy consumption of the air conditioner cold source system are used as targets, and the parameter optimization result obtained after multi-parameter optimization control is combined to serve as a predetermined control strategy, and is used as parameter input for guiding the actual operation of the air conditioner system, and the parameter input is preset into a corresponding control system, so that the system performs self-adjustment and operation according to the preset parameters according to the control strategy.
The following describes the 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 of an air conditioning system, as shown in fig. 2, and the steps involved in the embodiment include:
system modeling 10, which models an air conditioning system based on actual data.
And (3) carrying out multi-parameter optimization 20, wherein a multi-parameter optimization algorithm is adopted to carry out parameter optimization on the air conditioning system.
And the coupling control 30 is used for carrying out coupling control on various operation parameters of the air conditioning system at the same moment by combining the highest energy efficiency and the lowest energy consumption of the system, so as to guide the air conditioning system to regulate and control.
In this embodiment, the air conditioning system monitoring parameters include: indoor and outdoor environment parameters, water chiller state parameters, water chiller operation data, water chiller energy consumption data, heat pump set state parameters, heat pump set operation data, heat pump set energy consumption data, air conditioner set state parameters, air conditioner set operation parameters, air conditioner set energy consumption data, water pump state parameters, water pump operation parameters, water pump energy consumption data, cooling tower state parameters, cooling tower operation parameters, cooling tower energy consumption data, user side data, wherein the user side data comprises: indoor actual temperature value, indoor temperature set value, indoor personnel number, indoor equipment number, power and other data.
As shown in fig. 3, the multi-parameter coupling control method for an air conditioning system according to the embodiment specifically includes the steps of:
the system modeling 10 specifically includes: the method comprises the steps of carrying out mechanism model analysis on each device of an air conditioning system and a system formed by the devices, analyzing the physical rule of the device, and establishing a system optimal model by using monitored system operation data in combination with a machine learning algorithm, wherein the method comprises the following steps: and determining the 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: and determining an objective function, constraint conditions and a swarm intelligence algorithm.
The method comprises the steps of determining a target (function) of system optimization according to an actual operation energy consumption model and an energy efficiency model of an air conditioning system, and combining controllable parameters of an actual control system, wherein the target is mainly water inlet and outlet temperature of a water chilling unit, water pump flow of a chilled water pump and a cooling water pump and frequency of a cooling tower fan in the embodiment.
The control parameters are optimized by adopting a swarm intelligence algorithm, and the swarm intelligence algorithm adopted in the embodiment is mainly a genetic algorithm and a particle swarm algorithm, so that the numerical value of the control parameters is obtained.
The coupling control 30 includes: the method mainly aims at the highest energy efficiency and the lowest energy consumption of the system in the implementation of the method, and generates specific numerical values of system control parameters according to a multi-parameter optimization process.
And transmitting the control parameters to an actual field control system, and presetting a centralized processing unit written into the control system 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 conditioning cold source system, a mechanism model of the air conditioning system needs to be analyzed first: the method comprises the steps of analyzing the related influence relation between the energy consumption of the air conditioning system and one or more factors of the chilled water inlet and outlet temperature, the cooling water inlet and outlet temperature, the chilled water pump flow, the cooling tower fan frequency and the like, determining the basic framework and structure of an energy consumption mathematical model of the air conditioning system, namely determining an air conditioning system empirical formula containing unknown parameters.
Taking a core device in an air conditioning system, namely a water chiller, as an example, an empirical formula for determining energy consumption (power) and influence parameters of the core device in the example of the invention is as follows:
P chiller ＝a 0 +a 1 (t ci -t eo )+a 2 (t ci -t eo ) 2 +a 3 Q ch +a 4 Q ch 2 +a 5 (t ci -t eo )Q ch (1)
wherein: p (P) chiller Refrigerating unit power, kW; q (Q) ch Load of a refrigerating unit, kW; t is t ci Cooling water inlet temperature, DEG C; t is t eo The outlet temperature of chilled water, DEG C; a, a 0 ～a 5 Preset unknown coefficients.
Because the above empirical formula (1) contains a large number of unknown key parameters, the actual operation data of the air conditioning system needs to be combined, and the specific values of the coefficients in the model are obtained through a machine learning method and a parameter identification method. In the embodiment of the invention, the generalized linear regression algorithm and the least square method parameter identification algorithm in the machine learning algorithm are used, and meanwhile, the cross verification thought is combined to carry out system modeling. The basic flow is as follows: the original data set is divided into two major categories, one category is training data used for training an algorithm model, and the other category is verification data.
Namely, in the embodiment of the invention, the determining the operation model of the air conditioning system specifically comprises the following steps:
and training a model algorithm of the air conditioning system operation initial model by utilizing a part of the historical data. And performing model optimization by using the historical data, the optimization algorithm and the preset constraint conditions of the rest parts. And carrying out the model algorithm training and model optimization iteratively to determine model parameters and generate an air conditioning system operation model.
Randomly initializing parameter values in a mathematical model (1), adopting the model (1) for samples in each training data, comparing the obtained power value with the 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 the system modeling method is shown in fig. 4.
Through the 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, the optimization of control parameters is performed 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: chilled water outlet temperature, cooling water inlet temperature, chilled water pump flow, cooling tower fan frequency, and the like. Before system optimization can be performed, objective functions and constraints must be determined.
In this embodiment, for an air conditioning cold source system, its objective function is the total energy consumption of the system; the related constraint condition setting accords with the objective constraint condition and the equipment performance constraint of actual operation, and is determined according to the system equipment parameters and the operation conditions.
The function of the air conditioner cold source system established in this embodiment is as follows:
p cold -source＝p chiller +p eo-pump +p ci-pump +p tower (2)
wherein: p is p cold-source The total energy consumption of the cold source system is unit kW; p is p chiller The energy consumption of the refrigerating unit is unit kW; p is p eo-pump The energy consumption of the chilled water pump is unit kW; p is p ci-pump The energy consumption of the cooling water pump is unit kW; p is p tower The unit kW is the energy consumption of the cooling tower.
The relation between the energy consumption of the chilled water pump, the cooling water pump and the cooling tower and the factors such as the chilled water inlet temperature, the cooling water inlet temperature, the chilled water pump flow, the cooling water pump flow and the like is a mathematical function containing unknown parameters, and is similar to the expression of the formula (1), but is not listed in the embodiment.
In a general air conditioning system, the chilled water outlet temperature can be set through a control main interface of a refrigerating unit, and the essence of the control main interface is to change the opening degree of a guide vane of a water chilling unit, so that the chilled water outlet temperature of the refrigerating unit is a controllable parameter; the cooling water inlet temperature can be realized by adjusting the frequency of a cooling fan through a cooling tower in a common cooling tower system. The flow of the chilled water pump and the flow of the cooling water pump can be regulated through the frequency converter, the motor rotation speed of the water pump is regulated, the frequency regulation flow is realized, and the frequency of the cooling tower can be regulated through the frequency converter. Therefore, in this embodiment, the determined controllable parameters of the air conditioning system are: the water outlet temperature of the chilled water, the water inlet temperature of the cooling water, the flow of the chilled water pump, the flow of the cooling water pump and the frequency of a fan of the cooling tower.
The objective function and the control parameters of the optimization control in this embodiment are determined, and the optimization range of the optimization parameters needs to be further determined, where in the actual engineering, the optimization parameter range is as follows:
5℃≤T eo ≤13℃(3-1)
20℃≤T ci ≤33℃(3-2)
0.6V 0 ≤V≤V 0 (3-3)
30Hz≤f≤50Hz(3-4)
in the above formula: t (T) eo The temperature of the outlet water of the chilled water of the refrigerating unit is lower than the temperature; t (T) ci The temperature of the cooling water outlet of the refrigerating unit is lower than the temperature of the cooling water outlet of the refrigerating unit; v (V) 0 For rated flow of water pump, m 3 /h; v is the actual flow of the water pump, m 3 /h; f is the cooling tower operating frequency, hz.
The above problems to be solved in the embodiments of the present invention belong to the multi-objective optimization problem: there is a goal to be achieved (often expressed in terms of a function) and there are several constraints that require an optimal solution to this goal under the constraints of the constraints.
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) Principle of genetic algorithm:
in nature, the natural rule of winner and winner is followed: the living environment is getting worse and resources are getting more limited, and the fight between organisms is inevitably existed, and only the living individuals winning in the fight between living can survive, and the living individuals are considered to be strong in adaptability and are the result of 'optimization' in nature.
The genetic algorithm refers to the evolution rule of the nature, namely the survival and the superior and inferior rule of the fittest, the evolution rule is blended into the algorithm, and when the problem is solved, a random search is adopted in a local solution space to find a relatively good result, so that the method is gradually expanded to a global solution space, and the globally optimal solution is found in a step-by-step and optimization mode.
FIG. 5 is a flow chart of the optimization by genetic algorithm in the multi-parameter optimization method in the implementation of the present invention.
The following formula is a mathematical model of a typical genetic algorithm SGA (Simple Genetic Algorithm, SGA):
SGA＝SGA(C,fitvalue,P 0 ,N,Φ,Γ,Ψ,T) (4)
wherein: c is the individual coding method;
fitvalue is an individual fitness function;
P 0 is an initial population;
n is the size of the population;
phi is a selection operator;
Γ is the crossover operator;
psi is a mutation operator;
t is the convergence condition of the genetic algorithm.
According to the principle of the genetic algorithm, the pseudo code of the genetic algorithm in this embodiment is as follows.
Algorithm: genetic algorithm. For solving multi-objective optimization problems
Input: optimizing a problem model; constraint conditions.
And (3) outputting: and optimizing the control parameters.
The method comprises the following steps:
(1) Initializing race;
(2)repeat；
(3) An ethnic individual fitness function;
(4) Selecting;
(5) Crossing;
(6) Variation;
(7) An until convergence condition.
(2) Principle of particle swarm algorithm:
in the motion of a bird population, there are some basic operational laws: when finding food, one individual in the group is sensitive to the food, namely, the group knows some related information of the food, and then the groups can communicate with each other and transfer information, and finally, one individual guides the whole group to find the food.
The method has the advantages that the food in the motion of the bird group is equivalent to the optimal solution in the multi-objective optimization problem, the mode of searching the food by the bird group provides a good thought for an optimization algorithm, namely heuristic search, and a global optimization technology is formed on the basis of the heuristic search, namely the principle of a particle swarm algorithm.
Fig. 6 is a flowchart of the optimization by the particle swarm algorithm in the multi-parameter optimization method according to the present invention.
The following formula is a mathematical model of a typical particle swarm algorithm PSO (ParticleSwarmOptimization, PSO):
PSO＝PSO(fitvalue,P 0 ,m,w,c 1 ,c 2 ,T)(5)
wherein: fitvalue is an fitness function; p (P) 0 Is an initial population; m is the initial population size; w is inertial weight; c 1 And c 2 Is an acceleration coefficient; t is the convergence condition of the particle swarm algorithm.
The pseudo code of the established multi-parameter optimization control parameter problem is shown below according to the principle of the particle swarm algorithm.
Algorithm: particle swarm algorithm. The method is used for solving the multi-objective optimization problem.
Input: optimizing a problem model; constraint conditions.
And (3) outputting: and optimizing the control parameters.
The method comprises the following steps:
(1) Initializing race;
(2)repeat；
(3) An ethnic individual fitness function;
(4) Updating the particle speed;
(5) Updating the particle position;
(6) An until convergence condition.
The embodiment of the invention discloses a multi-parameter coupling control method for an air conditioning system, as shown in fig. 6, which is a flow chart for implementing multi-parameter coupling control in the embodiment of the application.
In the embodiment of the invention, the multi-parameter optimization control under the condition that the energy consumption of the air conditioning system is the lowest or the energy efficiency is the highest, namely an initial model of the air conditioning system operation comprises: the relation between the energy consumption of the system and the operation parameters or the relation between the energy efficiency of the system and the operation parameters.
The lowest optimization objective function of the air conditioning system energy consumption in this embodiment becomes:
min(p cold-source )＝min(p chiller +p eo-pump +p ci-pump +p tower )(6)
or the optimal objective function with highest energy efficiency of the air conditioning system is as follows:
max(COP cold-source )＝max(Q/(p chiller +p eo-pump +p ci-pump +p tower ))(7)
wherein: p is p cold-source The total energy consumption of the cold source system is kW; p is p chiller The energy consumption of the refrigerating unit is kW; p is p eo-pump The energy consumption of the chilled water pump is kW; p is p ci-pump The energy consumption of the cooling water pump is kW; p is p tower For cooling tower energy consumption, kW; q is the system load, kW.
According to the optimization problem under the condition of the lowest energy consumption or the highest energy efficiency of the system, the air conditioner control parameter values under the specific load rate can be obtained by combining a multi-parameter optimization algorithm, as shown in the table 1.
TABLE 1 optimization results of control parameters targeting lowest energy consumption or highest energy efficiency of a System at a specific load Rate
Figure BDA0004194963420000131
Each row in the table is regarded as a control strategy, the control strategy is preset and written into the air conditioner control system, when the system meets a certain load rate, a control instruction is executed according to the preset control strategy, and control parameters are executed according to the preset parameters, so that the multi-parameter coupling control method in the embodiment of the invention is realized. The load rate in the table of the embodiment of the invention is only exemplified and not limited in sequence, and the optimization control method in the invention is suitable for any load rate. The control parameters corresponding to each load factor may be set into the control system as a control strategy.
Compared with the existing control method of the air conditioning system, the invention has the following effects:
the invention can realize modeling of the air conditioner system by utilizing the actual operation data, and can better meet the actual operation condition of the system and reflect the operation rule of the system compared with the method which depends on a physical model only. By the modeling method, the air conditioning system is regulated and controlled more stably and safely.
The invention adopts a coupling control method with multiple control parameters, reasonably optimizes the influencing variables in the system, and overcomes the delay defect compared with the traditional single-point feedback control. By the optimized control parameters and the coupling control method, the air conditioning system is controlled to save more energy.
The multi-parameter coupling control of the ground source heat pump system is used for further describing the multi-parameter coupling control of the air conditioning system.
The building area of the air conditioning area of the project is about 3 ten thousand square meters, the end of the air conditioning area is provided with a ceiling radiation and replacement fresh air system, the cold and heat sources of the system are 2 ground source heat pump units and 1 water chilling unit, and the 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 7 ℃/12 ℃ and respectively supply a fresh air unit (directly utilizing the chilled water at 7 ℃/12 ℃) and a ceiling radiation plate for replacement (18 ℃/20 ℃ chilled water replaced by a plate for the ceiling radiation system); the ground source heat pump unit in winter provides hot water at 35 ℃/30 ℃, and a fresh air unit (directly utilizes the hot water at 35 ℃/30 ℃) and a ceiling radiation plate (the ceiling radiation system utilizes the hot water at 28 ℃/26 ℃) which is replaced by the plate) are respectively supplied. The switching of the operation conditions in different seasons is realized by switching the valves. And under the working condition in summer, the open cooling tower is started as required.
Table 2 ground source heat pump system equipment parameter table
Figure BDA0004194963420000141
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 item actual run data
Sequence number P(kW) Q ch (kW) T ci (℃) T co (℃) T ei (℃) T eo (℃)
1 34.4 53.6 19.3 19.5 13.2 12.7
2 34.4 53.6 20.2 20.4 12.2 11.8
3 36.5 53.6 22 21.9 12.4 12
4 36.3 53.6 22.6 22.8 12.2 11.7
5 44.0 53.6 26.3 26.5 12.4 11.9
... ... ... ... ... ... ...
35376 96.5 524.8 28.9 33.0 13.1 8.2
35377 96.6 535.5 22.9 27.9 20.6 14.0
35378 96.7 503.4 27.4 31.8 14.9 10.2
35379 96.8 535.5 26.2 31.2 17.6 11.5
35380 98.4 535.5 25.9 30.6 20.7 14.8
Mathematical models of the refrigerating unit, the cooling water pump and the cooling water pump are obtained by using the system modeling method provided in the example of the present invention as shown in tables 4-1 to 4-3 below.
Table 4-1 mathematical model of refrigeration unit
Figure BDA0004194963420000151
TABLE 4-2 chilled water pump model
Figure BDA0004194963420000161
TABLE 4-3 Cooling Water Pump model
Figure BDA0004194963420000162
Figure BDA0004194963420000171
According to the above model, the cold source system model in this embodiment is determined as:
p cold-source ＝p chiller +p eo-pump +p ci-pump (8)
wherein: p is p cold-source The total energy consumption of the cold source system is kW; p is p chiller The energy consumption of the refrigerating unit is kW; p is p eo-pump The energy consumption of the chilled water pump is kW; p is p ci-pump For cooling water pump energy consumption, kW.
The cooling tower in the system is a fanless cooling tower, so that no energy is consumed.
Thus, the objective function of the optimization control problem of the item in this embodiment is:
p cold-source ＝p chiller +p eo-pump +p ci-pump (9)
subject to the following constraints:
5≤T eo ≤13；
20≤T ci ≤33；
63≤V eo ≤105；
75≤V ci ≤125；
wherein: t (T) eo The temperature of the outlet water of the chilled water of the refrigerating unit is lower than the temperature; t (T) ci The temperature of the cooling water outlet of the refrigerating unit is lower than the temperature of the cooling water outlet of the refrigerating unit; v (V) eo For the flow of chilled water, m 3 /h；V ci For cooling water flow, m 3 /h。
Finally, using the coupling control method of the present example: optimizing the multiple control parameters with the lowest system energy consumption as a target to form a control strategy of the air conditioning system, as shown in table 5:
table 5 multiparameter coupling control strategy
Figure BDA0004194963420000172
Figure BDA0004194963420000181
The invention can realize modeling of the air conditioner system by using the actual operation data, and can better meet the actual operation condition of the system and reflect the operation rule of the system compared with the prior art which simply 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 influencing variables in the system, and overcomes the delay defect compared with the traditional single-point feedback control. By the optimized control parameters and the coupling control method, the air conditioning system is controlled to save more energy.
The present invention also provides a multiparameter coupling control device of an air conditioning system, as shown in fig. 7, comprising:
the data acquisition module 701 is configured to acquire a real-time load factor of the air conditioning system.
The parameter determining module 702 is configured to determine an air conditioning system control parameter according to the real-time load factor and a predetermined control policy. The control strategy is the corresponding relation between the predetermined load rate and the control parameter of the air conditioning system.
And a control module 703 for controlling the air conditioning system according to the determined air conditioning system control parameter.
In an embodiment of the present invention, as shown in fig. 8, the apparatus further includes:
the policy determination module 704 is configured to determine a control policy in advance according to historical data of the air conditioning system.
Wherein the policy determination module comprises:
and the historical data acquisition unit is used for acquiring historical data of the air conditioning system.
And the machine learning unit is used for establishing an air conditioning system operation initial model by utilizing a machine learning algorithm.
And the training optimization unit is used for carrying out model training on the air conditioning system operation initial model by utilizing the historical data and optimizing and determining 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 rate and the air conditioning system control parameter by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target condition according to the generated air conditioning system operation model.
In an embodiment of the present invention, the training optimization unit includes:
and the training unit is used for training a model algorithm of the air conditioning system operation initial model by utilizing a part of the historical data.
And the optimizing unit is used for optimizing the model by utilizing the historical data, the optimizing algorithm and preset constraint conditions of the rest parts.
And the iteration processing unit is used for carrying out the model algorithm training and model optimization to determine model parameters and generate an air conditioning system operation model.
The implementation manner of the multi-parameter coupling control device of the air conditioning system according to the present invention will be clear to those skilled in the art through the description of the embodiments of the present invention, and will not be described herein.
The embodiment of the invention also provides an electronic device, which can be a desktop computer, a tablet computer, a mobile terminal and the like, and the embodiment is not limited to the desktop computer, the tablet computer, the mobile terminal and the like. In this embodiment, the electronic device may refer to the embodiments of the foregoing method and apparatus, and the content thereof is incorporated herein, and the repetition is not repeated.
Fig. 9 is a schematic block diagram of a system configuration of an electronic device 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; memory 140 is coupled to central processor 100. Notably, the diagram is exemplary; other types of structures may also be used in addition to or in place of the structures to implement telecommunications functions or other functions.
In one embodiment, the multi-parameter coupling control function of the air conditioning system may be integrated into the central processor 100. Wherein the central processor 100 may be configured to control as follows:
acquiring a real-time load rate of an air conditioning system;
determining control parameters of an 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 parameters.
In the embodiment of the invention, the method further comprises the following steps: a control strategy is predetermined according to historical data of an air conditioning system; wherein, the liquid crystal display device comprises a liquid crystal display device,
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 to determine model parameters on the initial air conditioning system operation model by utilizing the historical data 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 rate and the air conditioning system control parameter by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target condition.
In the embodiment of the present invention, performing model training and optimization on the initial model of air conditioning system operation by using the historical data to determine model parameters, so as to generate an air conditioning system operation model includes:
performing model algorithm training on the air conditioning system operation initial model by utilizing a part of the historical data;
performing model optimization by using the historical data, an optimization algorithm and preset constraint conditions of the rest parts;
and carrying out the model algorithm training and model optimization iteratively to determine model parameters and generate an air conditioning system operation model.
In the embodiment of the present invention, the history 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 initial model for running the air conditioning system comprises the following steps: the relation between the energy consumption of the system and the operation parameters or the relation between the energy efficiency of the system and the operation parameters.
In the embodiment of the invention, the control parameters of the air conditioning system include: chilled water outlet temperature, cooling water inlet temperature, chilled water pump flow and cooling water pump flow.
As shown in fig. 9, the electronic device 600 may further include: a communication module 110, an input unit 120, an audio processing unit 130, a display 160, a power supply 170. It is noted that the electronic device 600 need not include all of the components shown in fig. 9; in addition, the electronic device 600 may further include components not shown in fig. 9, to which reference is made to the related art.
As shown in fig. 9, the central processor 100, sometimes also referred to as a controller or operational control, may include a microprocessor or other processor device and/or logic device, which central processor 100 receives inputs and controls 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 about failure may be stored, and a program for executing the information may be stored. And the central processor 100 can execute the program stored in the memory 140 to realize information storage or processing, etc.
The input unit 120 provides an input to the central processor 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 for displaying display objects such as images and characters. The display may be, for example, but not limited to, an LCD display.
The memory 140 may be a solid state memory such as Read Only Memory (ROM), random Access Memory (RAM), SIM card, or the like. But also a memory which holds information even when powered down, can be selectively erased and provided with further data, an example of which is sometimes referred to as EPROM or the like. Memory 140 may also be some other type of device. Memory 140 includes a buffer memory 141 (sometimes referred to as a buffer). The memory 140 may include an application/function storage 142, the application/function storage 142 for storing application programs and function programs or a flow for executing operations of the electronic device 600 by the central processor 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 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 applications, address book applications, etc.).
The communication module 110 is a transmitter/receiver 110 that transmits and receives signals via an antenna 111. A 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, etc., 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 to receive audio input from the microphone 132 to implement usual telecommunication functions. The audio processor 130 may include any suitable buffers, decoders, amplifiers and so forth. In addition, the audio processor 130 is also coupled to the central processor 100 so that sound can be recorded locally through the microphone 132 and so that sound stored locally can be played through the speaker 131.
The embodiment of the present invention also provides a computer-readable program, wherein when the program is executed in an electronic device, the program causes a computer to execute the multiparameter coupling control method of the air conditioning system as described in the above embodiment in the electronic device.
The embodiment of the present invention also provides a storage medium storing a computer-readable program, wherein the computer-readable program causes a computer to execute the multiparameter coupling control of the air conditioning system described in the above embodiment in an electronic device.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to facilitate an understanding of the system of the present invention and its core ideas; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (10)

1. The multi-parameter coupling control method of the air conditioning system is characterized by comprising the following steps of:
acquiring historical data of an air conditioning system;
according to the actual operation parameters of the air conditioning system, an initial model of the air conditioning system operation is established by adopting a machine learning algorithm;
according to the historical data, performing model training and optimizing and determining model parameters on the air conditioning system operation initial model by adopting a genetic algorithm or a particle swarm algorithm so as to generate an air conditioning system operation model;
according to the air conditioning system operation model, the corresponding relation between the load rate and the air conditioning system control parameter is determined by taking the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target, so as to obtain a control strategy;
acquiring a real-time load rate of an air conditioning system;
determining control parameters of an air conditioning system according to the real-time load rate and the control strategy;
and controlling the air conditioning system according to the control parameters.
2. The multi-parameter coupling control method of an air conditioning system according to claim 1, wherein the machine learning algorithm includes a generalized linear regression algorithm and a parameter identification method based on a least square method.
3. The multi-parameter coupling control method of an air conditioning system according to claim 1, wherein model training and optimizing the initial model of air conditioning system operation using a genetic algorithm or a particle swarm algorithm to determine model parameters according to the historical data, to generate an air conditioning system operation model comprises:
performing model algorithm training on the air conditioning system operation initial model by adopting part of historical data;
performing model optimization by adopting other historical data, an optimization algorithm and preset constraint conditions;
and carrying out model algorithm training and model optimization by adopting genetic algorithm or particle swarm algorithm iteration, determining model parameters and generating an air conditioning system operation model.
4. The multi-parameter coupling control method of an air conditioning system according to claim 2, wherein the history 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 relation between the energy consumption of the system and the operation parameters or the relation between the energy efficiency of the system and the operation parameters.
5. The multi-parameter coupling control method of an air conditioning system according to claim 1, wherein the optimal objective function with the lowest energy consumption of the air conditioning system is:
min(p cold-source )＝min(p chiller +p eo-pump +p ci-pump +p tower )；
wherein p is cold-source P is the total energy consumption of the cold source system chiller For energy consumption of refrigerating unit, p eo-pump For the energy consumption of the chilled water pump, p ci-pump For cooling water pump energy consumption, p tower And the energy consumption of the cooling tower is reduced.
6. The multi-parameter coupling control method of an air conditioning system according to claim 1, wherein the most energy efficient optimization objective function of the air conditioning system is:
max(COP cold-source )＝max(Q/(p chiller +p eo-pump +p ci-pump +p tower ))；
wherein, COP cold-source For the total energy efficiency of the air conditioning system, Q is the system load, p chiller For energy consumption of refrigerating unit, p eo-pump For the energy consumption of the chilled water pump, p ci-pump For cooling water pump energy consumption, p tower And the energy consumption of the cooling tower is reduced.
7. The multi-parameter coupling control method of an air conditioning system according to claim 1, wherein the control parameters include: chilled water outlet temperature, cooling water inlet temperature, chilled water pump flow and cooling water pump flow.
8. A multiparameter coupling control device of an air conditioning system, characterized in that the multiparameter coupling control device of the air conditioning system comprises:
a history data acquisition unit for acquiring history data of the air conditioning system;
the machine learning unit is used for establishing an air conditioning system operation initial model by adopting a machine learning algorithm according to actual operation parameters of the air conditioning system;
the training optimization unit is used for carrying out model training and optimizing and determining model parameters on the initial air conditioning system operation model by adopting a genetic algorithm or a particle swarm algorithm according to the historical data so as to generate an air conditioning system operation model;
the strategy determining unit is used for determining the corresponding relation between the load rate and the control parameters of the air conditioning system according to the air conditioning system operation model and with the lowest energy consumption or the highest energy efficiency of the air conditioning system as a target so as to obtain a control strategy;
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 control parameters of the air conditioning system according to the real-time load rate and the control strategy;
and the control module is used for controlling the air conditioning system according to the control parameters.
9. The multi-parameter coupling control device of an air conditioning system according to claim 8, wherein the training optimizing unit includes:
the training unit is used for training a model algorithm of the air conditioning system operation initial model by adopting part of historical data;
the optimization unit is used for performing model optimization by adopting the rest historical data, an optimization algorithm and preset constraint conditions;
and the iteration processing unit is used for carrying out model algorithm training and model optimization by adopting a group intelligent algorithm iteration, determining model parameters and generating an air conditioning system operation model.
10. 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 multi-parameter coupling control method of an air conditioning system according to any one of claims 1 to 7 when executing the computer program.
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