CN109882995B - Equipment and energy-saving control method thereof - Google Patents
Equipment and energy-saving control method thereof Download PDFInfo
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- CN109882995B CN109882995B CN201910038357.1A CN201910038357A CN109882995B CN 109882995 B CN109882995 B CN 109882995B CN 201910038357 A CN201910038357 A CN 201910038357A CN 109882995 B CN109882995 B CN 109882995B
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- 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 discloses equipment and an energy-saving control method thereof, relates to the technical field of equipment energy conservation, and aims to solve the problems that in the prior art, an energy-saving control strategy is used for controlling the energy-saving operation of intelligent equipment, the control is difficult to be accurately controlled, and the operation control of the equipment capable of achieving the energy-saving effect is not accurate enough, wherein the method comprises the following steps: the method comprises the steps of obtaining the current running state of the equipment, determining a group of optimal running parameters corresponding to the current running state by using a particle swarm algorithm according to the mapping relation between the preset running state and the running parameters of the equipment, and controlling the equipment to run by using the optimal running parameters in the current running state.
Description
Technical Field
The invention relates to the technical field of equipment energy conservation, in particular to equipment and an energy-saving control method thereof.
Background
With the continuous advance of the modernization level, the air conditioner becomes a necessary electrical appliance for a family, but the air conditioner is also a household electrical appliance which consumes the most energy when being used by a common family, but the energy consumption of the air conditioner is larger due to the higher energy consumption of the components of the fan outside the air conditioner, so that the problem that consumers are more headache is caused. Certainly, each large air conditioner manufacturer also spends a large amount of time and resources to break through the air conditioner energy-saving technology, and optimizes the air conditioner components to the air conditioner control method to obtain good effects, but the existing air conditioner control method is not accurate enough, the law of the energy-saving control strategy is not well mastered, and a great progress space exists.
In conclusion, the existing energy-saving control strategy is not easy to control accurately, and is not accurate enough for controlling the operation of some air conditioners capable of achieving the energy-saving effect.
Disclosure of Invention
The invention provides an energy-saving control method and equipment, which are used for solving the problems that in the prior art, an energy-saving control strategy is used for controlling energy-saving operation of intelligent equipment, accurate control is difficult, and the operation control of the equipment capable of achieving an energy-saving effect is not accurate enough.
In a first aspect, the present invention provides an energy saving control method, including:
acquiring the current running state of the equipment;
determining a group of optimal operation parameters corresponding to the current operation state by utilizing a particle swarm algorithm according to the mapping relation between the preset operation state and the operation parameters of the equipment;
and controlling the equipment to operate by utilizing the optimal operation parameters in the current operation state.
In the method, the optimal operation parameters of the current operation state of the equipment are calculated by utilizing a particle swarm algorithm, so that the optimal energy-saving operation parameters of the equipment can be accurately determined.
In a possible implementation manner, in the mapping relationship between the preset operation state of the device and the operation parameters, one operation state corresponds to multiple sets of operation parameters.
In a possible implementation manner, a plurality of groups of operation parameters corresponding to the current operation state of the equipment are used as a parameter group, and each group of operation parameters is used as an individual in the parameter group;
and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter group.
In a possible implementation manner, the fitness of each individual in the parameter group is determined through a preset fitness function;
determining the individual with the best fitness in the parameter population as a local optimal individual;
and determining a group of optimal operation parameters corresponding to the current operation state of the equipment according to the determined local optimal individuals.
In one possible implementation manner, calculating the device operation power consumption and operation benefit corresponding to each individual of the parameter group;
and calculating the fitness of each individual according to the equipment operation power consumption and the operation benefit of each individual by presetting a fitness function.
In a possible implementation manner, when the fitness of the local optimal individual meets a preset fitness limit and the population iteration meets a preset iteration number, determining a group of operation parameters corresponding to the current local optimal individual as a group of optimal operation parameters corresponding to the operation state;
when the fitness of the local optimal individual does not meet a preset fitness limit or the population iteration does not meet a preset iteration number, performing population iteration on the current parameter population, and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter population after iteration; the population iteration is to bias all individuals of the current parameter population toward the locally optimal individual.
In one possible implementation, the method for determining the fitness of the locally optimal individual satisfies a preset fitness limit, including:
and the difference value of the fitness corresponding to the currently updated local optimal individual and the fitness corresponding to the latest updated local optimal individual is smaller than a preset fitness threshold.
According to the method, the equipment is preferentially regulated and controlled by utilizing the particle swarm algorithm of biological evolution, so that low-energy-consumption operation of the equipment is realized, and the accuracy, the comfort and the energy conservation of equipment control are improved.
In a possible implementation manner, when the equipment is an air conditioner, the operation state of the equipment is a cooling state, a heating state and a constant temperature state, and the operation parameter is the rotating speed of an external fan of the air conditioner.
According to the method, the rotating speed of the outer fan of the air conditioner is preferentially regulated and controlled through the particle swarm algorithm, low-energy-consumption operation of the outer fan part of the air conditioner is achieved, and the accuracy, comfort and energy conservation of air conditioner control are improved.
In a second aspect, the present invention provides an apparatus for energy-saving control, the apparatus comprising: a processor and a memory:
the processor: the device is used for acquiring the current running state of the equipment;
determining a group of optimal operation parameters corresponding to the current operation state by utilizing a particle swarm algorithm according to the mapping relation between the preset operation state and the operation parameters of the equipment;
and controlling the equipment to operate by utilizing the optimal operation parameters in the current operation state.
In a third aspect, the present application also provides a computer storage medium having a computer program stored thereon, which when executed by a processing unit, performs the steps of the method of the first aspect.
In addition, for technical effects brought by any one implementation manner of the second aspect and the third aspect, reference may be made to technical effects brought by different implementation manners of the first aspect, and details are not described here.
Has the advantages that:
according to the invention, the rotating speed of the external fan is preferentially regulated and controlled by utilizing the particle swarm algorithm of biological evolution, so that low-energy-consumption operation of the external fan component of the air conditioner is realized, and the accuracy, comfort and energy conservation of air conditioner control are improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic diagram of a method for controlling energy saving of equipment according to an embodiment of the present invention;
fig. 2 is a flowchart of a specific method for controlling energy saving of a device according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an energy-saving control apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic diagram of an energy-saving control apparatus according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Some of the words that appear in the text are explained below:
in the embodiment of the present invention, the term "device" refers to a device having different sets of operating parameters in the same state, where the different operating parameters affect the operating power consumption, such as an air conditioner, a refrigerator, and the like, and may also refer to a component of any one of the above devices, such as an external fan, a compressor, and the like.
The application scenario described in the embodiment of the present invention is for more clearly illustrating the technical solution of the embodiment of the present invention, and does not form a limitation on the technical solution provided in the embodiment of the present invention, and it can be known by a person skilled in the art that with the occurrence of a new application scenario, the technical solution provided in the embodiment of the present invention is also applicable to similar technical problems. In the description of the present invention, the term "plurality" means two or more unless otherwise specified.
With the continuous advance of the modernization level, air conditioners, refrigerators and the like become essential household appliances, but the air conditioners are household appliances which consume the most energy when being used by common families, and the problem that consumers are very headache due to the large energy consumption of the air conditioners is solved. Certainly, each large air conditioner manufacturer also spends a large amount of time and resources to break through the air conditioner energy-saving technology, and optimizes the air conditioner components to the air conditioner control method to obtain good effects, but the existing air conditioner control method is not accurate enough, the law of the energy-saving control strategy is not well mastered, and a great progress space exists.
Therefore, an embodiment of the present invention provides an apparatus and a method for energy saving control thereof, so as to solve the problems occurring in the above scenarios.
In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
With respect to the above scenario, the following describes an embodiment of the present invention in further detail with reference to the drawings of the specification.
The first embodiment is as follows:
as shown in fig. 1, the present embodiment provides a method for controlling energy saving of a device, which specifically includes the following steps:
in this embodiment, when the apparatus is an air conditioner, the operation state of the apparatus may be, but is not limited to, a cooling state, a heating state, a constant temperature state, and the like, and when the apparatus is a refrigerator, the operation state of the apparatus may be, but is not limited to, a refrigerating state, a refreshing state, and the like. 102, determining a group of optimal operation parameters corresponding to the current operation state by using a particle swarm algorithm according to the mapping relation between the preset operation state and the operation parameters of the equipment;
the particle swarm optimization, also called particle swarm optimization or bird foraging optimization pso (particle swarm optimization), is a biological evolution algorithm ea (evolution algorithm), which also finds the optimal solution through iteration starting from a random solution.
And presetting an operation state and operation parameters as input parameters of the particle swarm algorithm, and searching an optimal solution through iteration by using the particle swarm algorithm to obtain the corresponding optimal operation parameters in the current state.
And 103, controlling the equipment to operate by using the optimal operation parameters under the current operation state.
In the method, the optimal operation parameters of the current operation state of the equipment are calculated by utilizing a particle swarm algorithm, so that the optimal energy-saving operation parameters of the equipment can be accurately determined.
In a possible implementation manner of the step 102, in the mapping relationship between the preset operation state and the operation parameter of the equipment, one operation state corresponds to multiple sets of operation parameters, and the mapping relationship between the preset operation state and the operation parameter of the equipment may be set in advance by a person skilled in the art, and may be, but is not limited to, as shown in the following table 1:
table 1:
| operating state A1 | B1 set of operating parameters | B2 set of operating parameters | B3 set of operating parameters | … |
| Operating state A2 | C1 group of operating parameters | C2 group of operating parameters | C3 group of operating parameters | … |
| Operating state A3 | D1 set of operating parameters | D2 set of operating parameters | D3 set of operating parameters | … |
| … | … | … | … | … |
In table 1, the operation parameters corresponding to different operation states of the device may be the same or different, and are not limited to this, and those skilled in the art can set the operation parameters according to the actual working conditions of the device.
In this embodiment, the device is an air conditioner, and the operation state of the device may be, but is not limited to, a cooling state, a heating state, and a constant temperature state; the above operation parameters may be, but are not limited to, the rotating speed parameter of the external fan of the air conditioner; the mapping relationship between the preset operation state and the operation parameter of the air conditioner may be, but is not limited to, as shown in table 2 below:
table 2:
| refrigerating state | Outer fan speed parameter B1 | Outer fan speed parameter B2 | Outer fan speed parameter B3 | … |
| Heating state | Outer fan speed parameter C1 | Outer fan speed parameter C2 | Outer fan speed parameter C3 | |
| Constant temperature state | Outer fan rotating speed parameter D1 | Outer fan rotating speed parameter D2 | Outer fan rotating speed parameter D3 |
In a possible implementation manner of the step 102, a plurality of sets of operation parameters corresponding to the current operation state of the device are used as a parameter group, and each set of operation parameters is used as an individual in the parameter group;
and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter group.
In this embodiment, a group consisting of all the external fan rotation speeds corresponding to the current operation state of the air conditioner is used as a parameter group, and each external fan rotation speed is used as one individual in the parameter group;
the particle swarm optimization, also called particle swarm optimization or bird foraging optimization pso (particle swarm optimization), is a biological evolution algorithm ea (evolution algorithm), which also starts from a random solution, finds an optimal solution through iteration, evaluates the quality of the solution through fitness, and finds global optimization by following the optimal value currently searched.
In a possible implementation manner of the step 102, the fitness of each individual in the parameter group is determined by presetting a fitness function, the individual with the best fitness in the parameter group is a locally optimal individual, and a set of optimal operation parameters corresponding to the current operation state of the device is determined according to the determined locally optimal individual.
The fitness of each individual in the above-mentioned parameter population may be determined, but is not limited to, by:
calculating the equipment operation power consumption and operation benefit corresponding to each individual of the parameter group;
and calculating the fitness of each individual according to the equipment operation power consumption and the operation benefit of each individual by presetting a fitness function.
The preset fitness function is not limited too much, and a person skilled in the art can set the fitness function according to actual requirements, for example, in this embodiment, the device is an air conditioner, and when the operation parameter is the rotating speed of the external fan, the fitness of each individual of the parameter group can be calculated by referring to the corresponding power consumption when the air conditioner operates at different rotating speeds of the external fan and the ambient temperature created by the operation of the air conditioner in the current operation state.
It should be understood that, in this embodiment, when the device is an air conditioner, and the operation parameter is an external fan rotation speed, and when the air conditioner is operated at different external fan rotation speeds, an individual corresponding to the external fan rotation speed at which the overall index of the external fan power consumption and the ambient temperature created by the air conditioner operation is optimal is the locally optimal individual.
In a possible implementation manner of the step 102, when the fitness of the locally optimal individual meets a preset fitness limit or the population iteration meets a preset iteration number, determining a set of operation parameters corresponding to the current locally optimal individual as a set of optimal operation parameters corresponding to the current operation state;
when the fitness of the local optimal individual does not meet a preset fitness limit or the population iteration does not meet a preset population iteration number, performing population iteration on the current parameter population, and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter population after iteration; the population iteration is to shift all individuals of the current parameter population towards the locally optimal individual.
The preset fitness limit is not excessively limited, and a person skilled in the art can set according to actual requirements, and in a possible implementation mode, when the difference value of the fitness corresponding to the currently updated local optimal individual and the fitness corresponding to the latest updated local optimal individual is smaller than a preset fitness threshold value, the fitness of the local optimal individual is determined to meet the preset fitness limit;
in this embodiment, when the preset fitness limit is 0.5, that is, when the difference between the fitness corresponding to the currently updated locally optimal individual and the fitness corresponding to the recently updated locally optimal individual is smaller than 0.5, it is determined that the fitness of the locally optimal individual meets the preset fitness limit.
It should be understood that, when it is determined for the first time whether the fitness of the locally optimal individual meets the preset fitness limit, the fitness corresponding to the recently updated locally optimal individual is defaulted to 0.
The preset value of the preset population iteration times is not limited too much, and a person skilled in the art can set the preset population iteration times according to the effect and power consumption brought by the operation of equipment, for example, in the embodiment, when the equipment is an air conditioner, the environment comfort brought by the operation of the air conditioner and the power consumption of an external fan are considered;
according to the method, the equipment is preferentially regulated and controlled by utilizing the particle swarm algorithm of biological evolution, so that low-energy-consumption operation of the equipment is realized, and the accuracy, the comfort and the energy conservation of equipment control are improved.
In a possible implementation manner, when the equipment is an air conditioner, the operation state of the equipment is a cooling state, a heating state and a constant temperature state, and the operation parameter is the rotating speed of an external fan of the air conditioner.
According to the method, the rotating speed of the outer fan of the air conditioner is preferentially regulated and controlled through the particle swarm algorithm, low-energy-consumption operation of the outer fan part of the air conditioner is achieved, and the accuracy, comfort and energy conservation of air conditioner control are improved.
As shown in fig. 2, a specific method for controlling energy saving of a device is provided as follows:
step 1) obtaining the current running state of equipment;
step 2) determining a parameter group corresponding to the current operation state according to the mapping relation between the preset operation state and the operation parameters of the equipment;
step 3) calculating the fitness of each individual of the current parameter group;
step 4) determining the local optimal individual of the current parameter group according to the fitness of all the individuals;
step 5) judging whether the fitness of the local optimal individual meets a preset fitness limit or whether the population iteration meets a preset iteration number, if not, entering step 6), and if not, entering step 7);
step 6) carrying out population iteration on the current parameter population according to the current locally optimal individual to obtain a new parameter population, and entering step 3);
and shifting all individuals in the current parameter population to the local optimal individual to obtain a new parameter population.
Step 7) determining a group of operation parameters corresponding to the current locally optimal individual as a group of optimal operation parameters corresponding to the current operation state;
and 8) controlling the equipment to operate by using the determined optimal operation parameters.
It should be noted that the manner of an air conditioning energy saving control method recited in the embodiment of the present invention is merely an example, and any manner that can perform the energy saving control of the device by using the above method is applicable to the embodiment of the present invention.
Example two:
as shown in fig. 3, based on the same inventive concept, the present embodiment provides a device for energy-saving control, which includes a processor 301 and a memory 302, wherein the processor is configured to:
acquiring the current running state of the equipment;
determining a group of optimal operation parameters corresponding to the current operation state by utilizing a particle swarm algorithm according to the mapping relation between the preset operation state and the operation parameters of the equipment;
and under the current running state, controlling the equipment to run by using the optimal running parameters.
In the mapping relationship between the preset operation states and the operation parameters of the equipment, one operation state corresponds to a plurality of groups of operation parameters.
The processor is specifically configured to use multiple sets of operating parameters corresponding to the current operating state of the device as a parameter group, and use each set of operating parameters as an individual in the parameter group;
and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter group.
The processor is specifically configured to determine the fitness of each individual in the parameter group by presetting a fitness function;
determining the individual with the best fitness in the parameter group as a local optimal individual;
and determining a group of optimal operation parameters corresponding to the current operation state of the equipment according to the determined local optimal individuals.
The processor is specifically configured to calculate an operation power consumption and an operation benefit of the device corresponding to each individual of the parameter group;
and calculating the fitness of each individual according to the equipment operation power consumption and the operation benefit of each individual by presetting a fitness function.
The processor is specifically configured to determine, when the fitness of the locally optimal individual meets a preset fitness limit or the population iteration meets a preset iteration number, that a set of operation parameters corresponding to the current locally optimal individual is a set of optimal operation parameters corresponding to the current operation state;
when the fitness of the local optimal individual does not meet a preset fitness limit and the population iteration does not meet a preset population iteration number, performing population iteration on the current parameter population, and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter population after iteration; the population iteration is to shift all individuals of the current parameter population towards the locally optimal individual.
And when the difference value of the fitness corresponding to the currently updated local optimal individual and the fitness corresponding to the latest updated local optimal individual is smaller than a preset fitness threshold value, determining that the fitness of the local optimal individual meets a preset fitness limit.
When the equipment is an air conditioner, the running power consumption of the equipment corresponding to the local optimal individual meets the preset power consumption limit, the running states of the equipment are a refrigerating state, a heating state and a constant temperature state, and the running parameter is the rotating speed of an external fan of the air conditioner.
As shown in fig. 4, based on the same inventive concept, the present embodiment provides an apparatus for energy saving control, the apparatus including:
an operation state obtaining unit 401, configured to obtain a current operation state of the device;
an optimal operation parameter determining unit 402, configured to determine, according to a mapping relationship between a preset operation state of the device and an operation parameter, a set of optimal operation parameters corresponding to the current operation state by using a particle swarm algorithm;
and a control device operation unit 403, configured to control the device to operate in the current operation state by using the optimal operation parameter.
In the mapping relationship between the preset operation states and the operation parameters of the equipment, one operation state corresponds to a plurality of groups of operation parameters.
The optimal operation parameter determining unit is configured to use multiple sets of operation parameters corresponding to the current operation state of the device as a parameter group, and use each set of operation parameters as an individual in the parameter group;
and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter group.
The optimal operation parameter determining unit is used for determining the fitness of each individual in the parameter group through a preset fitness function;
determining the individual with the best fitness in the parameter group as a local optimal individual;
and determining a group of optimal operation parameters corresponding to the current operation state of the equipment according to the determined local optimal individuals.
The optimal operation parameter determining unit is used for calculating the operation power consumption and the operation benefit of the equipment corresponding to each individual of the parameter group;
and calculating the fitness of each individual according to the equipment operation power consumption and the operation benefit of each individual by presetting a fitness function.
The optimal operation parameter determining unit is used for determining a group of operation parameters corresponding to the current local optimal individual as a group of optimal operation parameters corresponding to the current operation state when the fitness of the local optimal individual meets a preset fitness limit or the population iteration meets a preset iteration number;
when the fitness of the local optimal individual does not meet a preset fitness limit and the population iteration does not meet a preset population iteration number, performing population iteration on the current parameter population, and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter population after iteration; the population iteration is to shift all individuals of the current parameter population towards the locally optimal individual.
And when the difference value of the fitness corresponding to the currently updated local optimal individual and the fitness corresponding to the latest updated local optimal individual is smaller than a preset fitness threshold value, determining that the fitness of the local optimal individual meets a preset fitness limit.
When the equipment is an air conditioner, the running power consumption of the equipment corresponding to the local optimal individual meets the preset power consumption limit, the running states of the equipment are a refrigerating state, a heating state and a constant temperature state, and the running parameter is the rotating speed of an external fan of the air conditioner.
Example three:
the embodiment of the present invention further provides a computer-readable non-volatile storage medium, which includes a program code, and when the program code runs on a computing terminal, the program code is configured to enable the computing terminal to execute the steps of the method according to the first embodiment of the present invention.
The present application is described above with reference to block diagrams and/or flowchart illustrations of methods, apparatus (systems) and/or computer program products according to embodiments of the application. It will be understood that one block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, and/or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer and/or other programmable data processing apparatus, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks.
Accordingly, the subject application may also be embodied in hardware and/or in software (including firmware, resident software, micro-code, etc.). Furthermore, the present application may take the form of a computer program product on a computer-usable or computer-readable storage medium having computer-usable or computer-readable program code embodied in the medium for use by or in connection with an instruction execution system. In the context of this application, a computer-usable or computer-readable medium may be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method for energy-efficient control of a device, the method comprising:
acquiring the current running state of the equipment;
determining a group of optimal operation parameters corresponding to the current operation state by utilizing a particle swarm algorithm according to the mapping relation between the preset operation state and the operation parameters of the equipment; in the mapping relation between the preset operation state and the operation parameters of the equipment, one operation state corresponds to a plurality of groups of operation parameters;
and controlling the equipment to operate by utilizing the optimal operation parameters in the current operation state.
2. The method of claim 1, wherein determining a set of optimal operating parameters corresponding to the current operating state using a particle swarm algorithm comprises:
taking a plurality of groups of operation parameters corresponding to the current operation state of the equipment as parameter groups, and taking each group of operation parameters as individuals in the parameter groups;
and determining a group of optimal operation parameters corresponding to the equipment in the current operation state according to the parameter group.
3. The method of claim 2, wherein determining a corresponding set of optimal operating parameters of the device in the current operating state based on the parameter population comprises:
determining the fitness of each individual in the parameter group through a preset fitness function;
determining the individual with the best fitness in the parameter population as a local optimal individual;
and determining a group of optimal operation parameters corresponding to the current operation state of the equipment according to the determined local optimal individuals.
4. The method of claim 3, wherein determining the fitness of each individual of the parameter population by a preset fitness function comprises:
calculating the running power consumption and running benefit of equipment corresponding to each individual of the parameter group;
and calculating the fitness of each individual according to the equipment operation power consumption and the operation benefit of each individual by presetting a fitness function.
5. The method of claim 3, wherein determining a set of optimal operating parameters corresponding to the current operating state of the device based on the determined locally optimal individuals comprises:
when the fitness of the local optimal individual meets a preset fitness limit or the population iteration meets a preset iteration number, determining a group of operation parameters corresponding to the current local optimal individual as a group of optimal operation parameters corresponding to the current operation state;
when the fitness of the local optimal individual does not meet a preset fitness limit and the population iteration does not meet a preset population iteration number, performing population iteration on the current parameter population, and determining a corresponding group of optimal operation parameters of the equipment in the current operation state according to the parameter population after iteration; the population iteration is to bias all individuals of the current parameter population toward the locally optimal individual.
6. The method of claim 5, wherein the fitness of the locally optimal individual satisfies a preset fitness limit, comprising:
and the difference value of the fitness corresponding to the currently updated local optimal individual and the fitness corresponding to the latest updated local optimal individual is smaller than a preset fitness threshold.
7. The method of claim 1, wherein when the device is an air conditioner, the operation state of the device is a cooling state, a heating state, and a constant temperature state, and the operation parameter is an external fan rotation speed of the air conditioner.
8. An apparatus for energy-efficient control, the apparatus comprising a processor and a memory, wherein the processor is configured to:
acquiring the current running state of the equipment;
determining a group of optimal operation parameters corresponding to the current operation state by utilizing a particle swarm algorithm according to the mapping relation between the preset operation state and the operation parameters of the equipment; in the mapping relation between the preset operation state and the operation parameters of the equipment, one operation state corresponds to a plurality of groups of operation parameters;
and controlling the equipment to operate by utilizing the optimal operation parameters in the current operation state.
9. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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|---|---|---|---|---|
| CN102980272A (en) * | 2012-12-08 | 2013-03-20 | 珠海派诺科技股份有限公司 | Air conditioner system energy saving optimization method based on load prediction |
| CN104566797A (en) * | 2014-12-18 | 2015-04-29 | 珠海格力电器股份有限公司 | Central air conditioner cooling tower fan frequency control method |
| CN105676657A (en) * | 2015-12-31 | 2016-06-15 | 天津大学 | Intelligent household electrical appliance electrical load group control method |
| WO2018182022A1 (en) * | 2017-03-31 | 2018-10-04 | ダイキン工業株式会社 | Air conditioning system |
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2019
- 2019-01-16 CN CN201910038357.1A patent/CN109882995B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102980272A (en) * | 2012-12-08 | 2013-03-20 | 珠海派诺科技股份有限公司 | Air conditioner system energy saving optimization method based on load prediction |
| CN104566797A (en) * | 2014-12-18 | 2015-04-29 | 珠海格力电器股份有限公司 | Central air conditioner cooling tower fan frequency control method |
| CN105676657A (en) * | 2015-12-31 | 2016-06-15 | 天津大学 | Intelligent household electrical appliance electrical load group control method |
| WO2018182022A1 (en) * | 2017-03-31 | 2018-10-04 | ダイキン工業株式会社 | Air conditioning system |
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