CN114065898A - Air conditioner energy use measurement and control method and system based on decision-making technology - Google Patents
Air conditioner energy use measurement and control method and system based on decision-making technology Download PDFInfo
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- CN114065898A CN114065898A CN202111569520.0A CN202111569520A CN114065898A CN 114065898 A CN114065898 A CN 114065898A CN 202111569520 A CN202111569520 A CN 202111569520A CN 114065898 A CN114065898 A CN 114065898A
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Abstract
The invention discloses an air conditioner energy use measurement and control method and a system based on decision-making technology, which relate to the technical field of artificial intelligence monitoring, and the method comprises the steps of obtaining the current set temperature value and humidity value of one or more than two air conditioners connected with a monitoring center and the current load ratio of each operation processing device in a strong temperature control area of each air conditioner when the monitoring center does not receive a master control message sent by a mobile user end; according to the method, the optimal combination of the predicted load ratio of each operation processing device and the predicted set temperature value and humidity value of each air conditioner is found by adopting a reinforcement learning algorithm, so that the total power consumption of all the air conditioners and the operation processing devices is minimum, the real-time intelligent on-line overall control and the global energy consumption optimization of the air conditioner are realized, the control response is fast, and the utilization rate of energy and resources can be greatly improved.
Description
Technical Field
The invention relates to the technical field of artificial intelligence monitoring, in particular to a method and a system for measuring and controlling energy use of an air conditioner based on a decision-making technology.
Background
In a room in which a plurality of arithmetic processing devices such as servers, storages, and switches are installed, in order to normally operate the arithmetic processing devices in a temperature and humidity environment within a predetermined range, it is necessary to adjust operating conditions of an air conditioner based on measurement results of a temperature and humidity sensor and the like. Since the arithmetic processing device generates heat in a concentrated manner when it is operating efficiently, the temperature of the indoor air conditioner is often set too low to avoid a high-temperature failure of the arithmetic processing device due to an increase in the indoor temperature during a failure of the air conditioner or a power failure, which results in a waste of energy resources.
In the prior art, whether the indoor temperature and humidity environment is still within a specified range after a preset time period is estimated by adopting a prediction estimation method according to the current measurement result of a temperature and humidity sensor and the like, and whether the working condition setting of the air conditioner is reasonable is evaluated, so that the working condition of the air conditioner is adjusted according to the evaluation result. However, since the general prediction estimation method often requires a long time of calculation before obtaining an evaluation result, it is impossible to realize on-line control of the air conditioner, and there is a delay in control time efficiency.
Disclosure of Invention
Therefore, in order to overcome the above defects, embodiments of the present invention provide a method and a system for measuring and controlling energy usage of an air conditioner based on a decision-making technology, which can intelligently monitor energy usage of the air conditioner in real time, thereby avoiding waste of energy resources.
Therefore, the air conditioner energy use measurement and control method based on the decision technology is applied to a monitoring center and comprises the following steps:
s101, judging whether a master control message sent by a mobile user side is received, wherein the master control message comprises air conditioner identification information and contents of master control temperature value and humidity value information, and the master control temperature value and humidity value information are working parameter values which are set for an air conditioner indicated by the air conditioner identification information and enable the air conditioner to work under the master control temperature value and humidity value;
s102, when the master control message sent by the mobile user terminal is not received, acquiring the current set temperature value and humidity value of one or more than two air conditioners connected with the monitoring center and the current load ratio of each operation processing device in the strong temperature control area of each air conditioner;
s103, according to the current set temperature value, the current set humidity value and the current load ratio value, finding the best combination between the predicted load ratio value of each operation processing device and the predicted set temperature value and humidity value of each air conditioner by adopting a reinforcement learning algorithm, and enabling the total power consumption of all the air conditioners and the operation processing devices to be minimum;
s104, optimally controlling each air conditioner to work under the respective predicted set temperature value and humidity value;
and S105, optimally controlling each operation processing device in the strong temperature control area of each air conditioner to adjust the load distribution strategy, so that each operation processing device works under the respective predicted load ratio value.
Preferably, the step of S103 includes:
s31, calculating and obtaining the predicted load ratio of each operation processing device and the predicted set temperature value and humidity value of each air conditioner when the total power consumption of all the air conditioners and the operation processing device is minimum according to a preset prediction model based on the current set temperature value and humidity value and the current load ratio value; the preset prediction model is obtained by training a neural network model by using a sample set, wherein elements of the sample set comprise a set temperature value and a humidity value of each air conditioner in a preset historical time period and a load ratio of each operation processing device in a strong temperature control area of each air conditioner when the total power consumption of all the air conditioners and the operation processing devices is minimum under the set temperature value and the humidity value.
Preferably, the neural network model is a GRU model.
Preferably, the method further comprises the following steps:
and S106, when receiving the main control message sent by the mobile user terminal, controlling the air conditioner indicated by the air conditioner identification information to work under the main control temperature value and the main control humidity value.
Preferably, the method further comprises the following steps:
s107, acquiring total power consumption of all air conditioners working under the conditions of predicting set temperature values and humidity values and predicting load occupation ratio values of all operation processing equipment in a preset time length;
s108, correcting a preset power consumption-time relation curve according to the total power consumption in the preset time length to obtain a power consumption-time relation updating curve;
and S109, calculating and obtaining the value generated after the total power consumption is reduced according to a value model based on the power consumption-time relation updating curve and the preset power price in each time period.
The embodiment of the invention provides an air conditioner energy use measurement and control method based on a decision technology, which is applied to a mobile user side and comprises the following steps:
s201, acquiring a first parameter message and a second parameter message sent by a monitoring center, wherein the first parameter message comprises identification information of all air conditioners, information of current set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the second parameter message comprises content of current load ratio of all operation processing equipment;
s202, displaying the current set temperature value and humidity value of the air conditioner indicated by the identification information of each air conditioner, and displaying the current load ratio of the operation processing equipment indicated by the identification information of all the operation processing equipment in the strong temperature control area of the air conditioner indicated by the identification information of each air conditioner.
Preferably, the method further comprises the following steps:
s203, acquiring a third parameter message and a fourth parameter message sent by a monitoring center, wherein the third parameter message comprises identification information of all air conditioners, information of predicted set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the fourth parameter message comprises content of predicted load ratio of all operation processing equipment;
s204, displaying the predicted set temperature value and humidity value of the air conditioner indicated by the identification information of each air conditioner, and displaying the predicted load ratio of the operation processing equipment indicated by the identification information of all the operation processing equipment in the strong temperature control area of the air conditioner indicated by the identification information of each air conditioner.
A computer program product of an embodiment of the invention comprises a computer program stored on a computer readable storage medium and adapted to be executed on a computer, the computer program comprising instructions adapted to perform the steps of the above-described decision-making technology-based air conditioner energy usage measurement and control method applied to a monitoring center when it is run on the computer.
A computer program product of an embodiment of the invention comprises a computer program stored on a computer readable storage medium and adapted to be executed on a computer, the computer program comprising instructions adapted to perform the steps of the above-described decision technology-based air conditioner energy usage measurement and control method applied to a mobile user terminal when the computer program is run on the computer.
The air conditioner energy use measurement and control system based on the decision technology comprises a monitoring center, one or more than two air conditioners, one or more than two operation processing devices and a mobile user side; the monitoring center is respectively communicated with each air conditioner, each operation processing device and the mobile user end;
the monitoring center is used for executing the steps of the air conditioner energy use measurement and control method based on decision technology applied to the monitoring center;
the mobile user side is used for executing the air conditioner energy use measurement and control method based on the decision technology applied to the mobile user side;
the air conditioner is used for working according to a predicted set temperature value and a predicted set humidity value under the optimized control of the monitoring center or working according to a main control temperature value and a main control humidity value under the control of a mobile user side;
the operation processing equipment is used for working according to the predicted load ratio value under the optimization control of the monitoring center.
The air conditioner energy use measurement and control method, the program product and the system based on the decision technology have the following advantages that:
1. the on-site detection and prediction estimation links of the temperature and humidity environment are omitted, so that set temperature and humidity working parameters of the air conditioner can be adjusted directly according to the load ratio condition of the current operation processing equipment, the calculation time required by prediction is greatly shortened, real-time intelligent on-line overall control and global energy consumption optimization of the air conditioner are realized, the energy consumption is reduced, energy is saved, and great contribution can be made to enterprise cost reduction. And the load proportion of the operation processing equipment can be guided to be reasonably distributed, the processing efficiency is improved, and the resources are saved.
2. The load ratio, the set temperature value and the set humidity value are predicted by adopting the bidirectional GRU model, and the method has the advantages of high training efficiency, high speed and high precision.
3. Besides automatic intelligent temperature control, each air conditioner can also carry out artificial active regulation and control, so that the control on the energy consumption of the air conditioner adapts to any emergency, and the air conditioner can work under the lowest energy consumption and the highest energy consumption.
4. The obtained reduction value is generated and embodied through the adjustment of the air conditioner and the operation processing equipment with the minimum total power consumption, the evaluation of the value obtained through the adjustment is realized, the saving amount caused by the combination of the optimal air conditioner temperature and humidity prediction value and the operation processing equipment load ratio prediction value can be intuitively felt by people, and the experience feeling is enhanced.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flowchart of a specific example of an air conditioner energy usage measurement and control method based on a decision-making technique in embodiment 1 of the present invention;
fig. 2 is a flowchart of another specific example of the air conditioner energy usage measurement and control method based on the decision technology in embodiment 1 of the present invention;
fig. 3 is a schematic block diagram of a specific example of an air conditioner energy usage measurement and control system based on a decision-making technique in embodiment 7 of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. 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.
In describing the present invention, it is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises" and/or "comprising," when used in this specification, are intended to specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The term "and/or" includes any and all combinations of one or more of the associated listed items. The terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The terms "mounted," "connected," and "connected" are to be construed broadly and may include, for example, direct connection, indirect connection via intermediate media, and communication between two elements; either a wireless or a wired connection. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Furthermore, certain drawings in this specification are flow charts illustrating methods. It will be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by computer program instructions. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions specified in the flowchart block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block or blocks. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.
Accordingly, blocks of the flowchart illustrations support combinations of means for performing the specified functions and combinations of steps for performing the specified functions. It will also be understood that each block of the flowchart illustrations, and combinations of blocks in the flowchart illustrations, can be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer instructions.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the following embodiments, the monitoring center is mainly responsible for monitoring energy usage of one or more air conditioners, one or more arithmetic processing devices, and the like in a room. Each air conditioner is provided with a strong temperature control area and a weak temperature control area, the strong temperature control area is an area closer to the air conditioner, and the weak temperature control area is an area farther from the air conditioner, and the strong temperature control area and the weak temperature control area can be specifically set according to the output power of the air conditioner.
The air conditioner mainly regulates and controls the temperature and humidity environment of one or more than two pieces of operation processing equipment and the like in a strong temperature control area, so that the air conditioner works in a specified temperature and humidity environment. The strong temperature control areas of different air conditioners are divided into non-overlapping areas, namely, one operation processing device only belongs to the strong temperature control area of one air conditioner.
The arithmetic processing device generally includes a server, a memory, a switch, and the like.
The mobile user end is mainly responsible for remotely monitoring the energy consumption conditions of one or more than two air conditioners, one or more than two operation processing devices and the like in a room, and generally comprises a mobile phone, an IPAD module, a GSM module and the like.
Example 1
The embodiment provides an air conditioner energy use measurement and control method based on a decision-making technology, which is applied to a monitoring center, and as shown in fig. 1, the method comprises the following steps:
s101, judging whether a master control message sent by a mobile user side is received, wherein the master control message comprises air conditioner identification information and contents of master control temperature value and humidity value information, and the master control temperature value and humidity value information are working parameter values which are set for an air conditioner indicated by the air conditioner identification information and enable the air conditioner to work under the master control temperature value and humidity value;
s102, when the master control message sent by the mobile user terminal is not received, acquiring the current set temperature value and humidity value of one or more than two air conditioners connected with the monitoring center and the current load ratio of each operation processing device in the strong temperature control area of each air conditioner;
s103, according to the current set temperature value, the current set humidity value and the current load ratio value, finding the best combination between the predicted load ratio value of each operation processing device and the predicted set temperature value and humidity value of each air conditioner by adopting a reinforcement learning algorithm, and enabling the total power consumption of all the air conditioners and the operation processing devices to be minimum;
s104, optimally controlling each air conditioner to work under the respective predicted set temperature value and humidity value;
and S105, optimally controlling each operation processing device in the strong temperature control area of each air conditioner to adjust the load distribution strategy, so that each operation processing device works under the respective predicted load ratio value. Under general conditions, the load occupation amount of the operation processing equipment is in direct proportion to the heat productivity of the operation processing equipment, so that the method omits a link of field detection and prediction estimation of a temperature and humidity environment, set temperature and humidity working parameters of the air conditioner can be adjusted directly according to the load occupation amount of the current operation processing equipment, the calculation time required by prediction is greatly shortened, real-time intelligent on-line overall control and global energy consumption optimization of the air conditioner are realized, the energy consumption is reduced, energy is saved, and great contribution can be made to enterprise cost reduction.
Preferably, the step of S103 includes:
s31, calculating and obtaining the predicted load ratio of each operation processing device and the predicted set temperature value and humidity value of each air conditioner when the total power consumption of all the air conditioners and the operation processing device is minimum according to a preset prediction model based on the current set temperature value and humidity value and the current load ratio value; the preset prediction model is obtained by training a neural network model by using a sample set, wherein elements of the sample set comprise a set temperature value and a humidity value of each air conditioner in a preset historical time period and a load ratio of each operation processing device in a strong temperature control area of each air conditioner when the total power consumption of all the air conditioners and the operation processing devices is minimum under the set temperature value and the humidity value. The load ratio of each operation processing device in the strong temperature control area of each air conditioner is adjusted to form a unit combination of the air conditioner and the operation processing device in the strong temperature control area thereof, so that the total power consumption of the unit generated by each unit combination is minimum, and the total power consumption of all the air conditioners and the operation processing device is minimum.
Preferably, the neural network model is a GRU (gate recovery unit) model, and the GRU model is one of Recurrent Neural Network (RNN) models, and has the advantages of high training efficiency and high speed.
In the step of S31, the step of training the neural network model using the sample set includes:
s311, acquiring the total power consumption of all corresponding air conditioners and operation processing equipment under different set temperature values and humidity values of each air conditioner and different load occupation ratios of each operation processing equipment in a strong temperature control area of each air conditioner in a preset historical time period, and determining the set temperature value and humidity value of each air conditioner and the load occupation ratio of each operation processing equipment in the corresponding strong temperature control area of each air conditioner as a sample set when the total power consumption of all the air conditioners and operation processing equipment is minimum;
and S312, training the GRU model by adopting the sample set to obtain a preset prediction model. When the air conditioner and the operation processing equipment are used, the current set temperature value and the current set humidity value are input into the trained preset prediction model, and the combination between the optimal predicted load ratio of each operation processing equipment and the predicted set temperature value and humidity value of each air conditioner, which are output by the model and can minimize the total power consumption of all the air conditioners and the operation processing equipment, can be obtained.
Preferably, the step of S312 includes:
s3121, classifying the sample set to obtain a training set and a test set; the classification mode can be random or according to a preset time node.
S3122, training the bidirectional GRU model by adopting the training set to obtain a preliminary prediction model and a preliminary training result; the bidirectional GRU model comprises a forward GRU network and a backward GRU network, and the steps specifically comprise: classifying the training set into a first training set and a second training set, wherein the classification mode can be random or performed according to a preset time node; training the forward GRU network by adopting the first training set to obtain a first training result; adopting the first training result and the second training set to jointly form a third training set; training the backward GRU network by adopting the third training set to obtain a preliminary prediction model and a preliminary training result; the load ratio, the set temperature value and the set humidity value are predicted by adopting the bidirectional GRU model, and the method has the advantages of high training efficiency, high speed and high precision.
S3123, judging whether the preliminary training result is converged by adopting the test set, and if the preliminary training result is converged, acquiring a preset test model; and if the preliminary training result is not converged, continuing training the preliminary training result until the preliminary training result is converged.
Preferably, as shown in fig. 1, the method for measuring and controlling energy usage of an air conditioner based on decision-making technology further includes the following steps:
and S106, when receiving a master control message sent by a mobile user terminal, controlling the air conditioners indicated by the identification information of the air conditioners to work under the master control temperature value and the master control humidity value, so that each air conditioner can perform automatic intelligent temperature control and also can perform artificial active regulation and control, and the control on the energy usage amount of the air conditioners is adaptive to any emergency situation, can work under the lowest energy usage amount and can work under the highest energy usage amount.
Preferably, as shown in fig. 2, the method for measuring and controlling energy usage of an air conditioner based on decision-making technology further includes the following steps:
s107, acquiring total power consumption of all air conditioners working under the conditions of predicting set temperature values and humidity values and predicting load occupation ratio values of all operation processing equipment in a preset time length;
s108, correcting a preset power consumption-time relation curve according to the total power consumption in the preset time length to obtain a power consumption-time relation updating curve; the preset power consumption-time relation curve can be obtained by summarizing according to historical data or by manually setting; preferably, the preset power consumption corresponding to the time corresponding to the preset duration is modified to be the total power consumption on the preset power consumption-time relation curve;
and S109, updating the curve and presetting the electricity price in each time interval based on the electricity consumption-time relation, and calculating according to a value model to obtain the value generated after the total electricity consumption is reduced, so that the obtained reduced value can be generated and embodied through the adjustment of the air conditioner and the operation processing equipment with the minimum total electricity consumption, the evaluation of the value obtained by the adjustment is realized, the combination of the optimal air conditioner temperature and humidity value and the operation processing equipment load ratio value can be intuitively sensed by people, and the experience feeling is enhanced.
Preferably, the calculation formula of the value model is as follows:
wherein, Δ F is a value generated after the total power consumption is reduced, ∈ is a correction function, F' (T) is a power consumption-time relationship update curve, F (T) is a preset power consumption-time relationship curve, w (T) is a preset power price in each time period, and T is a preset duration.
Preferably, the method for measuring and controlling the energy use of the air conditioner based on the decision technology further comprises the following steps:
s110, a first parameter message and a second parameter message are respectively sent to the mobile user side, the first parameter message comprises identification information of all air conditioners, information of current set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the second parameter message comprises content of current load ratio of all operation processing equipment.
Preferably, the method for measuring and controlling the energy use of the air conditioner based on the decision technology further comprises the following steps:
and S111, respectively sending a third parameter message and a fourth parameter message to the mobile user side, wherein the third parameter message comprises identification information of all air conditioners, information of predicted set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the fourth parameter message comprises content of predicted load ratio of all operation processing equipment.
Preferably, the method for measuring and controlling the energy use of the air conditioner based on the decision technology further comprises the following steps:
and S112, sending a fifth parameter message to the mobile user terminal, wherein the fifth parameter message comprises the content of the value generated after the total power consumption is reduced.
Example 2
The embodiment provides an air conditioner energy use measurement and control method based on a decision-making technology, which is applied to a mobile user side and comprises the following steps:
s201, acquiring a first parameter message and a second parameter message sent by a monitoring center, wherein the first parameter message comprises identification information of all air conditioners, information of current set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the second parameter message comprises content of current load ratio of all operation processing equipment;
s202, displaying the current set temperature value and humidity value of the air conditioner indicated by the identification information of each air conditioner, and displaying the current load ratio of the operation processing equipment indicated by the identification information of all the operation processing equipment in the strong temperature control area of the air conditioner indicated by the identification information of each air conditioner.
Preferably, the method for measuring and controlling the energy use of the air conditioner based on the decision technology further comprises the following steps:
s203, acquiring a third parameter message and a fourth parameter message sent by a monitoring center, wherein the third parameter message comprises identification information of all air conditioners, information of predicted set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the fourth parameter message comprises content of predicted load ratio of all operation processing equipment;
s204, displaying the predicted set temperature value and humidity value of the air conditioner indicated by the identification information of each air conditioner, and displaying the predicted load ratio of the operation processing equipment indicated by the identification information of all the operation processing equipment in the strong temperature control area of the air conditioner indicated by the identification information of each air conditioner.
Preferably, the method for measuring and controlling the energy use of the air conditioner based on the decision technology further comprises the following steps:
s205, sending a master control message to a monitoring center, wherein the master control message comprises the content of air conditioner identification information and master control temperature value and humidity value information, and the master control temperature value and humidity value information are working parameter values which are set by an air conditioner indicated by the air conditioner identification information and enable the air conditioner to work under the master control temperature value and humidity value.
Preferably, the method for measuring and controlling the energy use of the air conditioner based on the decision technology further comprises the following steps:
s206, acquiring and displaying a fifth parameter message sent by the monitoring center, wherein the fifth parameter message comprises content of the value generated after the total power consumption is reduced.
According to the air conditioner energy usage measurement and control method based on the decision-making technology, by setting the step of judging whether the master control message is received, each air conditioner can automatically and intelligently control the temperature and can also be artificially and actively regulated, so that the control on the energy usage of the air conditioner can adapt to any emergency situation, and the air conditioner can work under the lowest energy usage and the highest energy usage. By displaying the working parameters of the air conditioner before and after prediction and the load ratio of the operation processing equipment, monitoring personnel can visually see the data change before and after intelligent adjustment by adopting a neural network model, and the experience feeling is improved by displaying the value generated after the total power consumption is reduced.
Example 3
The embodiment provides a monitoring center, including:
one or more processors; and
a storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the decision-making technique-based air conditioner energy usage measurement and control method of embodiment 1.
Example 4
The embodiment provides a mobile user end, including:
one or more processors; and
a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the air conditioner energy usage measurement and control method of embodiment 2 based on decision-making techniques.
Example 5
The present embodiment provides a computer program product comprising a computer program stored on a computer readable storage medium and adapted to be executed on a computer, the computer program comprising instructions adapted to perform the steps of the air conditioner energy usage measurement and control method according to the decision making technique of embodiment 1 when the computer program runs on the computer.
Example 6
The present embodiment provides a computer program product comprising a computer program stored on a computer readable storage medium and adapted to be executed on a computer, the computer program comprising instructions adapted to perform the steps of the air conditioner energy usage measurement and control method according to the decision making technique of embodiment 2 when the computer program runs on the computer.
Example 7
The embodiment provides an air conditioner energy usage measurement and control system based on decision-making technology, as shown in fig. 3, including a monitoring center, one or more than two air conditioners, one or more than two operation processing devices and a mobile user end; the monitoring center is respectively communicated with each air conditioner, each operation processing device and the mobile user end;
the monitoring center is used for executing the steps of the air conditioner energy use measurement and control method based on the decision-making technology in the embodiment 1;
the mobile user side is used for executing the steps of the air conditioner energy use measurement and control method based on the decision technology in the embodiment 2;
the air conditioner is used for working according to a predicted set temperature value and a predicted set humidity value under the optimized control of the monitoring center or working according to a main control temperature value and a main control humidity value under the control of a mobile user side;
the arithmetic processing device is used for working according to the predicted load ratio value under the optimization control of the monitoring center, but not limited to.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. A method for measuring and controlling the energy use of an air conditioner based on a decision-making technology is applied to a monitoring center and is characterized by comprising the following steps:
s101, judging whether a master control message sent by a mobile user side is received, wherein the master control message comprises air conditioner identification information and contents of master control temperature value and humidity value information, and the master control temperature value and humidity value information are working parameter values which are set for an air conditioner indicated by the air conditioner identification information and enable the air conditioner to work under the master control temperature value and humidity value;
s102, when the master control message sent by the mobile user terminal is not received, acquiring the current set temperature value and humidity value of one or more than two air conditioners connected with the monitoring center and the current load ratio of each operation processing device in the strong temperature control area of each air conditioner;
s103, according to the current set temperature value, the current set humidity value and the current load ratio value, finding the best combination between the predicted load ratio value of each operation processing device and the predicted set temperature value and humidity value of each air conditioner by adopting a reinforcement learning algorithm, and enabling the total power consumption of all the air conditioners and the operation processing devices to be minimum;
s104, optimally controlling each air conditioner to work under the respective predicted set temperature value and humidity value;
and S105, optimally controlling each operation processing device in the strong temperature control area of each air conditioner to adjust the load distribution strategy, so that each operation processing device works under the respective predicted load ratio value.
2. The method of claim 1, wherein the step of S103 comprises:
s31, calculating and obtaining the predicted load ratio of each operation processing device and the predicted set temperature value and humidity value of each air conditioner when the total power consumption of all the air conditioners and the operation processing device is minimum according to a preset prediction model based on the current set temperature value and humidity value and the current load ratio value; the preset prediction model is obtained by training a neural network model by using a sample set, wherein elements of the sample set comprise a set temperature value and a humidity value of each air conditioner in a preset historical time period and a load ratio of each operation processing device in a strong temperature control area of each air conditioner when the total power consumption of all the air conditioners and the operation processing devices is minimum under the set temperature value and the humidity value.
3. The method of claim 2, wherein the neural network model is a GRU model.
4. A method according to any of claims 1-3, further comprising the step of:
and S106, when receiving the main control message sent by the mobile user terminal, controlling the air conditioner indicated by the air conditioner identification information to work under the main control temperature value and the main control humidity value.
5. The method according to any one of claims 1-4, further comprising the steps of:
s107, acquiring total power consumption of all air conditioners working under the conditions of predicting set temperature values and humidity values and predicting load occupation ratio values of all operation processing equipment in a preset time length;
s108, correcting a preset power consumption-time relation curve according to the total power consumption in the preset time length to obtain a power consumption-time relation updating curve;
and S109, calculating and obtaining the value generated after the total power consumption is reduced according to a value model based on the power consumption-time relation updating curve and the preset power price in each time period.
6. A method for measuring and controlling energy use of an air conditioner based on a decision technology is applied to a mobile user side and is characterized by comprising the following steps:
s201, acquiring a first parameter message and a second parameter message sent by a monitoring center, wherein the first parameter message comprises identification information of all air conditioners, information of current set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the second parameter message comprises content of current load ratio of all operation processing equipment;
s202, displaying the current set temperature value and humidity value of the air conditioner indicated by the identification information of each air conditioner, and displaying the current load ratio of the operation processing equipment indicated by the identification information of all the operation processing equipment in the strong temperature control area of the air conditioner indicated by the identification information of each air conditioner.
7. The method of claim 6, further comprising the steps of:
s203, acquiring a third parameter message and a fourth parameter message sent by a monitoring center, wherein the third parameter message comprises identification information of all air conditioners, information of predicted set temperature values and humidity values and identification information of operation processing equipment in a strong temperature control area, and the fourth parameter message comprises content of predicted load ratio of all operation processing equipment;
s204, displaying the predicted set temperature value and humidity value of the air conditioner indicated by the identification information of each air conditioner, and displaying the predicted load ratio of the operation processing equipment indicated by the identification information of all the operation processing equipment in the strong temperature control area of the air conditioner indicated by the identification information of each air conditioner.
8. A computer program product comprising a computer program stored on a computer readable storage medium and adapted to be executed on a computer, characterized in that the computer program comprises instructions adapted to perform the steps of the decision technology based air conditioner energy usage measurement and control method according to any of claims 1-5 when it is run on the computer.
9. A computer program product comprising a computer program stored on a computer readable storage medium and adapted to be executed on a computer, characterized in that the computer program comprises instructions adapted to perform the steps of the decision technology based air conditioner energy usage measurement and control method according to claim 6 or 7 when it is run on the computer.
10. A decision technology-based air conditioner energy use measurement and control system is characterized by comprising a monitoring center, one or more than two air conditioners, one or more than two operation processing devices and a mobile user end; the monitoring center is respectively communicated with each air conditioner, each operation processing device and the mobile user end;
the monitoring center is used for comprising the steps of executing the air conditioner energy usage measurement and control method based on the decision technology according to any one of claims 1 to 5;
the mobile user terminal is used for comprising the steps of executing the air conditioner energy usage measurement and control method based on the decision technology according to claim 6 or 7;
the air conditioner is used for working according to a predicted set temperature value and a predicted set humidity value under the optimized control of the monitoring center or working according to a main control temperature value and a main control humidity value under the control of a mobile user side;
the operation processing equipment is used for working according to the predicted load ratio value under the optimization control of the monitoring center.
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