CN112984919A - Refrigerating system energy efficiency optimization method and device, electronic equipment and storage medium - Google Patents
Refrigerating system energy efficiency optimization method and device, electronic equipment and storage medium Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D15/00—Devices not covered by group F25D11/00 or F25D13/00, e.g. non-self-contained movable devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D29/00—Arrangement or mounting of control or safety devices
- F25D29/005—Mounting of control devices
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D2500/00—Problems to be solved
- F25D2500/04—Calculation of parameters
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25D—REFRIGERATORS; COLD ROOMS; ICE-BOXES; COOLING OR FREEZING APPARATUS NOT OTHERWISE PROVIDED FOR
- F25D2600/00—Control issues
- F25D2600/06—Controlling according to a predetermined profile
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Abstract
The application provides a method and a device for optimizing energy efficiency of a refrigeration system, electronic equipment and a storage medium, wherein the method for optimizing the energy efficiency of the refrigeration system comprises the following steps: solving to obtain the gradient of a control variable based on a preset cold start linear model; obtaining a target control parameter based on the control variable, the gradient of the control variable and a Monte Carlo method, and updating the model parameter of the preset cold start linear model, wherein the target control parameter is used as a control parameter for the next control of the refrigeration system; and repeating the steps until the target control parameter is converged and the state is stable. According to the refrigeration system energy efficiency optimization method, the refrigeration system energy efficiency optimization device, the electronic equipment and the storage medium, the accuracy of the calculated control parameters of the refrigeration system is high, and the effect of refrigeration system energy efficiency optimization is improved.
Description
Technical Field
The application relates to the technical field of refrigeration systems, in particular to a refrigeration system energy efficiency optimization method and device, electronic equipment and a storage medium.
Background
The energy efficiency optimization of the refrigeration system is an optimization problem for minimizing energy consumption, and can reduce the energy consumption and improve the energy utilization rate. At present, a strategy optimization method is mainly adopted for energy efficiency optimization of a refrigeration system, the strategy optimization method is controlled according to condition rules summarized by experience and is generally model-free, but the strategy optimization method is the condition rules summarized by experience and is not objective enough, so that the calculation accuracy of control parameters of the refrigeration system is low, and the effect of energy efficiency optimization of the refrigeration system is influenced.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method and an apparatus for optimizing energy efficiency of a refrigeration system, an electronic device, and a storage medium, which can enable accuracy of a calculated control parameter of the refrigeration system to be higher, and improve an effect of optimizing energy efficiency of the refrigeration system.
In a first aspect, an embodiment of the present application provides a method for optimizing energy efficiency of a refrigeration system, including:
solving to obtain the gradient of a control variable based on a preset cold start linear model;
obtaining a target control parameter based on the control variable, the gradient of the control variable and a Monte Carlo method, and updating the model parameter of the preset cold start linear model, wherein the target control parameter is used as a control parameter for the next control of the refrigeration system;
and repeating the steps until the target control parameter is converged and the state is stable.
In the implementation process, the refrigeration system energy efficiency optimization method according to the embodiment of the application obtains the gradient of the control variable through the preset cold start linear model, obtains the target control parameter based on the control variable, the gradient of the control variable and the monte carlo method, updates the model parameter of the preset cold start linear model, and repeatedly executes the steps until the target control parameter is converged and the state is stable.
Further, the obtaining a target control parameter based on the control variable, the gradient of the control variable, and the monte carlo method, and updating the model parameter of the preset cold start linear model includes:
randomly selecting an initial control variable from the control variables, the random probability obeying a softmax distribution established by the magnitude of the gradient of the control variable;
calculating to obtain target control parameters according to the initial control variables;
performing the next control of the refrigeration system according to the target control parameter, and acquiring the operation data of the refrigeration system;
and updating the model parameters of the preset cold start linear model according to the operation data.
In the implementation process, the Monte Carlo method is utilized by the method, so that the accuracy of the calculated control parameters of the refrigeration system is higher, and the effect of optimizing the energy efficiency of the refrigeration system is better improved.
Further, the calculating a target control parameter according to the initial control variable includes:
obtaining a random control variable based on bounded Gaussian distribution through the initial control variable and a random number generator;
and calculating to obtain a target control parameter according to the initial control variable, the gradient of the control variable and the random control variable.
In the implementation process, the method obtains a random control variable based on bounded Gaussian distribution through the initial control variable and the random number generator, and then obtains the target control parameter through calculation according to the initial control variable, the gradient of the control variable and the random control variable, so that the target control parameter can be accurately obtained through calculation, and the calculated control parameter of the refrigeration system is accurate.
Further, the calculating a target control parameter according to the initial control variable, the gradient of the control variable, and the random control variable includes:
calculating the product of the gradient of the control variable and the learning rate of the preset cold start linear model;
and adding the product, the initial control variable and the random control variable, and calculating to obtain a target control parameter.
In the implementation process, the method adds the product of the gradient of the control variable and the preset learning rate of the cold start linear model, the initial control variable and the random control variable, and can calculate the target control parameter more accurately.
Further, the updating the model parameters of the preset cold start linear model according to the operation data includes:
and updating the preset model parameters of the cold start linear model by using a least square method, a gradient descent method, a Newton method or a covariance estimation method according to the operation data.
In the implementation process, the method can better update the preset model parameters of the cold-start linear model according to the operation data.
Further, the preset cold start linear model is obtained by the following steps:
obtaining historical operating data of the refrigeration system;
and carrying out fuzzy modeling on the basis of the historical operating data through a preset linear model to obtain the preset cold start linear model.
In the implementation process, the method performs fuzzy modeling on the basis of the acquired historical operating data of the refrigeration system through the preset linear model to obtain the preset cold start linear model, so that the preset cold start linear model is more suitable for the refrigeration system energy efficiency optimization method in the embodiment of the application, the calculated refrigeration system control parameter has higher precision, and the refrigeration system energy efficiency optimization effect is better improved.
Further, the preset linear model is a field-aware linear model.
In the implementation process, the preset linear model adopts a field-aware linear model, so that the historical operating data of the refrigeration system required by fuzzy modeling can be greatly reduced, the field-aware linear model has higher stability and efficiency, and meanwhile, the nonlinear relation between the control parameters and the energy consumption can be captured, so that the calculated control parameters of the refrigeration system have higher precision, and the energy efficiency optimization effect of the refrigeration system is better improved.
In a second aspect, an embodiment of the present application provides a refrigeration system energy efficiency optimization device, including:
the gradient solving module is used for solving to obtain the gradient of the control variable based on a preset cold start linear model;
and the processing module is used for obtaining a target control parameter based on the control variable, the gradient of the control variable and the Monte Carlo method, and updating the preset model parameter of the cold start linear model, wherein the target control parameter is used as a control parameter for the next control of the refrigeration system.
In the implementation process, the refrigeration system energy efficiency optimization device according to the embodiment of the application obtains the gradient of the control variable through the preset cold start linear model, obtains the target control parameter based on the control variable, the gradient of the control variable and the monte carlo method, updates the model parameter of the preset cold start linear model, and repeatedly executes the operation until the target control parameter converges and the state is stable.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to make the electronic device execute the above-mentioned method for optimizing the energy efficiency of the refrigeration system.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium storing a computer program, where the computer program is executed by a processor to implement the method for optimizing energy efficiency of a refrigeration system.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and that those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of a method for optimizing energy efficiency of a refrigeration system according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of obtaining a preset cold start linear model according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of step S120 according to a first embodiment of the present application;
fig. 4 is a block diagram of a refrigeration system energy efficiency optimization device according to a second embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
At present, a strategy optimization method is mainly adopted for energy efficiency optimization of a refrigeration system, the strategy optimization method is controlled according to condition rules summarized by experience and is generally model-free, but the strategy optimization method is the condition rules summarized by experience and is not objective enough, so that the calculation accuracy of control parameters of the refrigeration system is low, and the effect of energy efficiency optimization of the refrigeration system is influenced.
In view of the above problems in the prior art, the present application provides a method and an apparatus for optimizing energy efficiency of a refrigeration system, an electronic device, and a storage medium, which can make the precision of a calculated control parameter of the refrigeration system higher, and improve the effect of optimizing energy efficiency of the refrigeration system.
Example one
In a refrigeration system, a water chilling unit is a main device of the refrigeration system, the refrigeration system usually adopts the water chilling unit to prepare cold water, the cold water is driven by a freezing water pump to flow in a water pipe and is distributed into an air conditioning unit, cold air and indoor hot air are driven by an air conditioning fan to carry out heat exchange for cooling a room, in the process, heat obtained by the water chilling unit from the cold water also needs to be dissipated into the air through devices such as a cooling tower, most of the heat exchange process adopts water as a medium, and the device for driving the medium to flow is a cooling water pump.
In a refrigeration system, a water chilling unit, a freezing water pump, an air conditioning fan and a cooling tower form main components of energy consumption of the refrigeration system. Refrigeration system energy efficiency optimization is an optimization problem that minimizes energy consumption.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for optimizing energy efficiency of a refrigeration system according to an embodiment of the present application. The energy efficiency optimization method of the refrigeration system, which is described in the embodiment of the application, can be applied to a controller of the refrigeration system.
The energy efficiency optimization method for the refrigeration system comprises the following steps:
and step S110, solving to obtain the gradient of the control variable based on a preset cold start linear model.
In this embodiment, the preset cold start linear model is a pre-established and set linear model.
The control variable is the control variable of the refrigeration system, under the preset cold start linear model, the control variable of the refrigeration system and the energy consumption are in an approximate convex function relationship, and the control variable of the refrigeration system and the energy consumption have a corresponding mapping relationship.
In this embodiment, the control variables of the refrigeration system may include the frequency of the chilled water pump, the frequency of the cooling tower, and other variables; correspondingly, the gradient of the control variable may include a gradient of a frequency of the chilled water pump, a gradient of a frequency of the cooling tower, and the like.
And step S120, obtaining target control parameters based on the control variables, the gradient of the control variables and the Monte Carlo method, and updating the preset model parameters of the cold start linear model, wherein the target control parameters are used as control parameters for the next control of the refrigeration system.
The monte carlo method is a statistical simulation method, a very important numerical calculation method guided by probability statistics theory, and refers to a method for solving many calculation problems by using random numbers (or more commonly pseudo-random numbers).
In this embodiment, the refrigeration system energy efficiency optimization method according to the embodiment of the present application will repeat steps S110 and S120 until the target control parameter is converged and the state is stable; it can be understood that, when the target control parameter converges and the state is stable, the target control parameter is the optimal control parameter.
According to the refrigeration system energy efficiency optimization method, the gradient of the control variable is obtained through the preset cold start linear model, the target control parameter is obtained based on the control variable, the gradient of the control variable and the Monte Carlo method, the model parameter of the preset cold start linear model is updated, and the steps are repeatedly executed until the target control parameter is converged and the state is stable.
Referring to fig. 2, fig. 2 is a schematic flowchart of obtaining a preset cold start linear model according to an embodiment of the present application.
In some embodiments of the present application, the preset cold start linear model in the refrigeration system energy efficiency optimization method according to the embodiment of the present application may be obtained by:
step S150, obtaining historical operating data of the refrigeration system;
and step S160, carrying out fuzzy modeling through a preset linear model based on historical operation data to obtain a preset cold start linear model.
The preset linear model is a preset linear model.
In the process, the method can enable the preset cold start linear model to be more suitable for the refrigeration system energy efficiency optimization method in the embodiment of the application, enables the calculated refrigeration system control parameter to be higher in precision, and well improves the refrigeration system energy efficiency optimization effect.
Optionally, the preset linear model is a field-aware linear model.
In the process, the preset linear model adopts a field-aware linear model, so that the historical operating data of the refrigeration system required by fuzzy modeling can be greatly reduced, the field-aware linear model has higher stability and efficiency, and the nonlinear relation between the control parameters and the energy consumption can be captured, so that the calculated control parameters of the refrigeration system have higher precision, and the energy efficiency optimization effect of the refrigeration system is better improved.
Referring to fig. 3, fig. 3 is a schematic flowchart of step S120 provided in the embodiment of the present application.
In some embodiments of the present application, the method for optimizing energy efficiency of a refrigeration system according to the embodiment of the present application, in step S120, obtaining a target control parameter based on a control variable, a gradient of the control variable, and a monte carlo method, and updating a preset model parameter of a cold start linear model, may include the following steps:
step S121, randomly selecting an initial control variable from the control variables, wherein the random probability obeys softmax distribution established by the gradient of the control variables;
step S122, calculating to obtain target control parameters according to the initial control variables;
step S123, performing the next control of the refrigeration system according to the target control parameter, and acquiring the operation data of the refrigeration system;
and step S124, updating the preset model parameters of the cold start linear model according to the operation data.
Wherein, assuming that the control variables of the refrigeration system include the frequency of the chilled water pump and the frequency of the cooling tower, one initial control variable randomly selected from the control variables may be the frequency of the chilled water pump or the frequency of the cooling tower.
In the process, the Monte Carlo method is utilized by the method, so that the accuracy of the calculated control parameters of the refrigeration system is higher, and the effect of optimizing the energy efficiency of the refrigeration system is better improved.
Optionally, in step S122, calculating a target control parameter according to the initial control variable, which may include:
obtaining a random control variable based on bounded Gaussian distribution through an initial control variable and a random number generator;
and calculating to obtain target control parameters according to the initial control variable, the gradient of the control variable and the random control variable.
It will be appreciated that the random control variables based on the bounded gaussian distribution are generated by the random number generator based on the initial control variables.
In the process, the method can accurately calculate the target control parameter, so that the calculated control parameter of the refrigeration system is accurate.
Optionally, when the target control parameter is calculated according to the initial control variable, the gradient of the control variable, and the random control variable, the following steps may be performed:
calculating the product of the gradient of the control variable and the learning rate of a preset cold start linear model;
and adding the product, the initial control variable and the random control variable, and calculating to obtain a target control parameter.
In the process, the method adds the product of the gradient of the control variable and the preset learning rate of the cold start linear model, the initial control variable and the random control variable, and can calculate the target control parameter more accurately.
Alternatively, in step S124, when the preset model parameters of the cold-start linear model are updated according to the operation data, the following steps may be performed:
and updating the preset model parameters of the cold start linear model by using a least square method, a gradient descent method, a Newton method or a covariance estimation method according to the operation data.
In the process, the method can better update the preset model parameters of the cold-start linear model according to the operation data.
Example two
In order to implement the corresponding method of the above embodiments to achieve the corresponding functions and technical effects, a refrigeration system energy efficiency optimization device is provided below.
Referring to fig. 4, fig. 4 is a block diagram illustrating a configuration of an energy efficiency optimizing apparatus for a refrigeration system according to an embodiment of the present application.
The energy efficiency optimization device for the refrigeration system comprises:
the gradient solving module 210 is configured to solve a gradient of the obtained control variable based on a preset cold start linear model;
and the processing module 220 is configured to obtain a target control parameter based on the control variable, the gradient of the control variable, and the monte carlo method, and update a preset model parameter of the cold start linear model, where the target control parameter is used as a control parameter for controlling the refrigeration system next time.
The refrigeration system energy efficiency optimization device provided by the embodiment of the application obtains the gradient of the control variable through the preset cold start linear model, obtains the target control parameter based on the control variable, the gradient of the control variable and the Monte Carlo method, updates the model parameter of the preset cold start linear model, and repeatedly executes the operation until the target control parameter is converged and the state is stable.
As an optional implementation manner, the processing module 220 may specifically be configured to:
randomly selecting an initial control variable from the control variables, the random probability obeying a softmax distribution established by the magnitude of the gradient of the control variables;
calculating to obtain target control parameters according to the initial control variables;
performing the next control of the refrigeration system according to the target control parameter, and acquiring the operation data of the refrigeration system;
and updating the preset model parameters of the cold start linear model according to the operation data.
Optionally, when the processing module 220 calculates the target control parameter according to the initial control variable, it may:
obtaining a random control variable based on bounded Gaussian distribution through an initial control variable and a random number generator;
and calculating to obtain target control parameters according to the initial control variable, the gradient of the control variable and the random control variable.
Optionally, when the processing module 220 calculates the target control parameter according to the initial control variable, the gradient of the control variable, and the random control variable, it may:
calculating the product of the gradient of the control variable and the learning rate of a preset cold start linear model;
and adding the product, the initial control variable and the random control variable, and calculating to obtain a target control parameter.
Optionally, when the processing module 220 updates the preset model parameters of the cold-start linear model according to the operation data, it may:
and updating the preset model parameters of the cold start linear model by using a least square method, a gradient descent method, a Newton method or a covariance estimation method according to the operation data.
As an optional implementation manner, the refrigeration system energy efficiency optimization device according to the embodiment of the present application may further include:
the acquisition module is used for acquiring historical operating data of the refrigeration system;
and the modeling module is used for carrying out fuzzy modeling on the basis of historical operating data through a preset linear model to obtain a preset cold start linear model.
The refrigeration system energy efficiency optimization device can implement the refrigeration system energy efficiency optimization method of the first embodiment. The alternatives in the first embodiment are also applicable to the present embodiment, and are not described in detail here.
The rest of the embodiments of the present application may refer to the contents of the first embodiment, and in this embodiment, details are not repeated.
EXAMPLE III
An embodiment of the present application provides an electronic device, which includes a memory and a processor, where the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above method for optimizing energy efficiency of a refrigeration system.
Alternatively, the electronic device may be a controller of a refrigeration system.
In addition, an embodiment of the present application further provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the method for optimizing energy efficiency of a refrigeration system as described above.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only an example of the present application and is not intended to limit the scope of the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Claims (10)
1. A method for optimizing energy efficiency of a refrigeration system, comprising:
solving to obtain the gradient of a control variable based on a preset cold start linear model;
obtaining a target control parameter based on the control variable, the gradient of the control variable and a Monte Carlo method, and updating the model parameter of the preset cold start linear model, wherein the target control parameter is used as a control parameter for the next control of the refrigeration system;
and repeating the steps until the target control parameter is converged and the state is stable.
2. The refrigerant system energy efficiency optimization method according to claim 1, wherein the obtaining target control parameters based on the control variables, the gradient of the control variables and the monte carlo method, and updating the model parameters of the preset cold start linear model comprises:
randomly selecting an initial control variable from the control variables, the random probability obeying a softmax distribution established by the magnitude of the gradient of the control variable;
calculating to obtain target control parameters according to the initial control variables;
performing the next control of the refrigeration system according to the target control parameter, and acquiring the operation data of the refrigeration system;
and updating the model parameters of the preset cold start linear model according to the operation data.
3. The refrigerant system energy efficiency optimization method according to claim 2, wherein the calculating a target control parameter according to the initial control variable comprises:
obtaining a random control variable based on bounded Gaussian distribution through the initial control variable and a random number generator;
and calculating to obtain a target control parameter according to the initial control variable, the gradient of the control variable and the random control variable.
4. The method for optimizing energy efficiency of a refrigeration system according to claim 3, wherein the calculating a target control parameter according to the initial control variable, the gradient of the control variable and the random control variable comprises:
calculating the product of the gradient of the control variable and the learning rate of the preset cold start linear model;
and adding the product, the initial control variable and the random control variable, and calculating to obtain a target control parameter.
5. The refrigerant system energy efficiency optimization method according to claim 2, wherein the updating the model parameters of the preset cold start linear model according to the operation data comprises:
and updating the preset model parameters of the cold start linear model by using a least square method, a gradient descent method, a Newton method or a covariance estimation method according to the operation data.
6. The refrigerant system energy efficiency optimization method according to claim 1, wherein the preset cold start linear model is obtained by the following steps:
obtaining historical operating data of the refrigeration system;
and carrying out fuzzy modeling on the basis of the historical operating data through a preset linear model to obtain the preset cold start linear model.
7. The refrigerant system energy efficiency optimization method according to claim 6, wherein the preset linear model is a field-aware linear model.
8. A refrigeration system energy efficiency optimization device, comprising:
the gradient solving module is used for solving to obtain the gradient of the control variable based on a preset cold start linear model;
and the processing module is used for obtaining a target control parameter based on the control variable, the gradient of the control variable and the Monte Carlo method, and updating the preset model parameter of the cold start linear model, wherein the target control parameter is used as a control parameter for the next control of the refrigeration system.
9. An electronic device, comprising a memory for storing a computer program and a processor for executing the computer program to cause the electronic device to perform the method for optimizing energy efficiency of a refrigeration system according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that it stores a computer program which, when executed by a processor, implements a method for optimizing energy efficiency of a refrigeration system according to any one of claims 1 to 7.
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