CN114198881A - Air conditioner control method and device and air conditioner - Google Patents
Air conditioner control method and device and air conditioner Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
- F24F2110/12—Temperature of the outside air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2140/00—Control inputs relating to system states
- F24F2140/50—Load
Abstract
Provided are an air conditioner control method, an air conditioner control device and an air conditioner, wherein the method comprises the following steps: s1: when the air conditioner enters a constant temperature mode, acquiring current environmental parameters and operation parameters of the air conditioner; s2: inputting the environmental parameter and the operation parameter into a first neural network model of the air conditioner to obtain a current operation capacity and power of the air conditioner, and inputting the environmental parameter and the operation parameter into an air conditioner load model to obtain a current load of the air conditioner, S3: correcting the operation capacity of the air conditioner according to the load variation of the air conditioner and the current operation capacity of the air conditioner; s4: and inputting the corrected air conditioner operation capacity and the current environment parameter of the air conditioner into a second neural network model to obtain the optimal operation parameter of the air conditioner. According to the method, the prediction of the output capacity of the current air conditioner and the optimization of the optimal energy efficiency control strategy are carried out by means of a prediction neural network model and a strategy optimization model of an air conditioning system; the air conditioner realizes accurate temperature control on the basis of the existing air conditioner control, and reduces unnecessary waste of energy consumption of the air conditioner.
Description
Technical Field
The invention relates to the field of intelligent control, in particular to an air conditioner control method and device and an air conditioner.
Background
With the improvement of living standard, the quality requirement of people on living environment is higher and higher. The air conditioner is used as an important device for indoor temperature and humidity adjustment and has become a necessity in the life of people. At present, the air conditioner adopts a traditional control strategy, the control target is only indoor temperature, the room temperature control process is influenced by factors such as indoor humidity, an enclosure structure, outdoor environment and the like, constant temperature control is difficult to realize through the existing control, and energy consumption waste is avoided.
Therefore, there is a need in the art for an effective control scheme for air conditioning.
The above information disclosed in the background section is only for further understanding of the background of the invention and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
The invention provides an air conditioner control method and device and an air conditioner. The scheme of the invention can solve the problem that the cold input of the air conditioner and the room load are not matched in the constant temperature stage of the air conditioner; the problem that heat leakage affects a control strategy in the constant temperature stage of the existing air conditioner can be solved.
A first aspect of the present invention provides an air conditioner control method, including: s1: when the air conditioner enters a constant temperature mode, acquiring current environmental parameters and operation parameters of the air conditioner; s2: inputting the environmental parameters and the operation parameters into a first neural network model of the air conditioner to obtain the current operation capacity and power of the air conditioner, and inputting the environmental parameters and the operation parameters into an air conditioner load model to obtain the current load of the air conditioner; s3: correcting the operation capacity of the air conditioner according to the load variation of the air conditioner and the current operation capacity of the air conditioner; s4: and inputting the corrected air conditioner operation capacity and the current environment parameter of the air conditioner into a second neural network model to obtain the optimal operation parameter of the air conditioner.
According to one embodiment of the invention, wherein: the load of the air conditioner includes an outdoor environmental load, a maintenance structure load, a latent heat load, and a sensible heat load.
According to an embodiment of the present invention, wherein the step S3 includes: the corrected air conditioner operation capacity Q is Qn+ΔQw+ΔQq+ΔQL+ΔQsWherein Q is the corrected air conditioner operation capacity, QnFor the current operation capability of the air conditioner, Δ Qw is the outdoor environmental load variation, Δ QqTo maintain structural load variation; delta QsFor sensible load variation, Δ QLIs the latent heat load variation.
According to an embodiment of the present invention, wherein the step S4 includes: obtaining the optimal operation parameter when the air conditioner operation power is minimum according to the corrected air conditioner operation capacity and the current environment parameter of the air conditioner, wherein the current environment parameter of the air conditioner comprises an outdoor environment parameter and an indoor environment parameter, the outdoor environment parameter comprises an outdoor temperature, and the indoor environment parameter comprises: indoor temperature, indoor humidity.
According to an embodiment of the present invention, the operation parameters of the air conditioner are an operation frequency of the air conditioner, an operation rotation speed of an internal fan of the air conditioner, a rotation speed of an external fan of the air conditioner, and an opening degree of an electronic expansion valve of the air conditioner.
According to an embodiment of the invention, wherein the method further comprises: when the air conditioner is in the constant temperature control stage in the (n-1) th cycle operation period, namely | Td-TsWhen | ≦ epsilon, after the optimal operation parameter with the minimum operation power is obtained through the calculation of the step S4 in the nth operation period, the air conditioner executes the following operation after executing the optimal operation parameter: if the indoor environment temperature greatly deviates from the set temperature in the nth operation period, namely | Td-TsIf the | is more than epsilon, replacing the operation parameter calculated in the n +1 th period with the operation parameter of the original n-1 period; if the ambient temperature in the chamber in the nth operation period is still within the range of Td-Ts | ≦ epsilon, the operation parameters executed in the nth period are continuously executed in the n +1 th period, wherein TdFor the air-conditioning dry bulb temperature, TsEpsilon is a preset threshold value for the temperature set by the user.
According to an embodiment of the invention, according to the claimsThe method of claim 1, wherein the current environmental parameters include: indoor temperature Tq，Indoor humidity DwAir temperature T of air conditionerdOutdoor temperature TwWherein the indoor temperature TqThe calculation method is as follows: t isq＝a*TInner, 0*(b*t2+c)-d(e*t3+f*t2+ g × t + h)/μ, wherein a, b, c, d, e, f and h are correction coefficients; the air-conditioning operation time is set after the room temperature reaches the set temperature; t is the air-conditioning running time after the set temperature is reached; t isInner, 0The room temperature changes at the moment of starting up; mu is the wall mean heat capacity, wherein Td、Dw、TwObtained by temperature and humidity sensors of the air conditioner.
According to an embodiment of the present invention, wherein the outdoor environmental load variation Δ Q of the current operation period nwAccording to the outdoor ambient temperature TwThe estimation calculation yields: delta Qw＝f(Td)＝α1·(Tw,n-Tw,n-1)+β1(ii) a Maintaining structural load variation Δ QqAccording to wall temperature TqThe estimation calculation yields: delta Qq＝f(Tq)＝α2·(Tq,n-Tq,n-1)+γ2·(Tq,n 4-Tq,n-1 4)+β2；
Sensible and latent load Δ Qs+ΔQLAccording to the indoor air temperature TdAnd calculating an air humidity D estimate to obtain: delta QL+ΔQs＝f(Td,D)＝α3*(Td,n-Td,n-1)+γ3*Dn*(1+β3*Td,n)*[m1+m2*(Td,n)4+m3*lg(Td,n)]-γ3*Dn-1*(1+β3*Td,n-1)*[m1+m2*lg(Td,n-1)4+m3*lg(Td,n-1)]In which α is1，β1，α2，β2，γ2，α3，γ3，β3，，m1，m2，m3，If the parameter is a coefficient dimension of 1, if the parameter is a constant term, the dimension is J or kJ, lg represents a logarithmic function with a base 10, and n-1 represent the nth and nth-1 periods of the air conditioner operation.
A second aspect of the present invention provides an air conditioning control apparatus including a memory storing a computer program and a processor for: when the computer program is executed, the above-described air conditioning control method is implemented.
A third aspect of the present invention provides an air conditioner that employs the air conditioner control method described above, or includes the air conditioner control apparatus described above.
According to the influence of a room maintenance structure, outdoor environmental load and indoor temperature and humidity on target control temperature, a related load variation estimation model is established, the control target load is corrected, and the matching between the real-time room load and the air conditioner output capacity is realized. And optimizing the current air conditioner output capacity prediction and the optimal energy efficiency control strategy by means of an air conditioner system prediction neural network model and a strategy optimization model. The invention realizes accurate temperature control on the basis of the existing air conditioner control, and simultaneously reduces unnecessary waste of air conditioner energy consumption.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are 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 that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic view of an air conditioner constant temperature phase affecting load change according to an exemplary embodiment of the present invention.
Fig. 2 is a flowchart of an air conditioner control method according to an exemplary embodiment of the present invention.
Fig. 3 is a schematic diagram of an air conditioning system predictive neural network model according to an exemplary embodiment of the present invention.
Fig. 4 is an air conditioner control strategy optimizing neural network model according to an exemplary embodiment of the present invention.
FIG. 5 is an exemplary optimization control flow diagram at constant temperature load according to the present invention.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
As used herein, the terms "first," "second," and the like may be used to describe elements of exemplary embodiments of the invention. These terms are only used to distinguish one element from another element, and the inherent features or order of the corresponding elements and the like are not limited by the terms. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms, such as those defined in commonly used dictionaries, are to be interpreted as having a meaning that is consistent with their context in the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Those skilled in the art will understand that the devices and methods of the present invention described herein and illustrated in the accompanying drawings are non-limiting exemplary embodiments and that the scope of the present invention is defined solely by the claims. Features illustrated or described in connection with one exemplary embodiment may be combined with features of other embodiments. Such modifications and variations are intended to be included within the scope of the present invention.
Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings, a detailed description of related known functions or configurations is omitted to avoid unnecessarily obscuring the technical points of the present invention. In addition, the same reference numerals refer to the same circuits, modules or units throughout the description, and repeated descriptions of the same circuits, modules or units are omitted for brevity.
Further, it should be understood that one or more of the following methods or aspects thereof may be performed by at least one control unit or controller. The terms "control unit," "controller," "control module," or "master module" may refer to a hardware device that includes a memory and a processor. The memory or computer-readable storage medium is configured to store program instructions, while the processor is specifically configured to execute the program instructions to perform one or more processes that will be described further below. Moreover, it is to be appreciated that the following methods may be performed by including a processor in conjunction with one or more other components, as will be appreciated by one of ordinary skill in the art.
The invention provides an air conditioner constant temperature control method, which can accurately balance sensible heat load and latent heat load and maintain the relation between structural load and air conditioner output capacity by detecting the wall temperature and the room humidity of a room, so that the room temperature control is more accurate, and an air conditioner system can run more energy-saving.
Fig. 1 is a schematic view of an air conditioner constant temperature phase affecting load change according to an exemplary embodiment of the present invention.
As shown in FIG. 1, the cooling capacity of the air conditioning cooling process is used for reducing the temperature T of the air dry bulb in addition to the building maintenance structure and the existence of the ambient air humiditydReduction of (sensible heat load change Qs) and also for reducing room humidity (wet bulb temperature T)wTemperature T of dry bulbdChange in latent heat load QL) And room wall surface temperature Tq(envelope load Q)q) And the heat exchange quantity Q between the outdoor penetrating window and the indoor airw. Because the air heat capacity is much less than the wall heat capacity, the wall temperature changes more slowly. And after the temperature of the room dry bulb reaches the temperature set by the user, the temperature of the wall is still higher than the temperature of the room. Along with the continuous output of the air conditioner cold energy, the load of the maintenance structure is continuously reduced. Meanwhile, under the working condition of high humidity, the dehumidification process of the air conditioner exists in the constant temperature stage, and the latent heat load is reduced accordingly. The existing air conditioner mainly controls the temperature according to a dry-bulb temperature target, cannot accurately balance the structural load, latent heat load and air conditioner output capacity in a constant temperature stage, causes temperature fluctuation, influences the comfort of a room and can also cause waste of air conditioner output cold quantity.
According to one or more embodiments of the invention, QwRepresenting the heat exchange load between indoor and outdoor environments, and noting the outdoor side load, the temperature difference between the outdoor temperature and the indoor dry bulb temperature is in positive correlation; qqMaintenance structure for representing wall body of room and indoor air roomThe heat exchange load of the heat exchanger is mainly in positive correlation with the temperature difference between the wall of a room and the temperature of an indoor dry bulb; qLThe heat exchange quantity in the dehumidification process of the air conditioner is represented, and is in positive correlation with the temperature difference of the indoor dry bulb and wet bulb temperatures. Fig. 1 shows the law of the effect of the above-mentioned various loads on the room temperature, such as the need of the air conditioner to overcome these load effects to maintain the room dry bulb temperature stable, i.e. constant temperature.
The room wall temperature Tq can be obtained by detecting a temperature sensor arranged on the room wall, and if the wall temperature sensor is not available or the sensor fails, the Tq can be estimated by the following formula;
Tq＝a*Tinner, 0*(b*t2+c)-d*(e*t3+f*t2+ g t + h)/μ equation (1)
Wherein a, b, c, d, e, f and h are correction coefficients; t is the running time of the air conditioner after the room temperature reaches the set temperature; t isInner, 0The room temperature changes at the moment of starting up; mu is the average heat capacity of the wall; other detection parameters dry bulb temperature TdHumidity DwOutdoor temperature TwCan be obtained according to the temperature and humidity sensor of the air conditioner, Td，Tw，TLAfter the parameters are detected, load prediction t influencing the room temperature can be made, and the unit is s or min or h, namely the set air conditioner running time is reached.
Fig. 2 is a flowchart of an air conditioner control method according to an exemplary embodiment of the present invention.
As shown in fig. 2, at step S1, when the air conditioner enters the constant temperature mode, the current environmental parameters and the operation parameters of the air conditioner are acquired; the environmental parameters here include outdoor environmental parameters and indoor environmental parameters. As a preferred embodiment of the present embodiment, the outdoor environment parameter includes an outdoor temperature, and the indoor environment parameter includes an indoor temperature and an indoor humidity. Further preferably, the current environmental parameters of the air conditioner are as follows: outdoor temperature, indoor humidity.
At step S2, inputting the environmental parameters and the operation parameters into a first neural network model of the air conditioner (for example, an air conditioning system prediction neural network model shown in fig. 3) to obtain the current operation capacity and power of the air conditioner, and inputting the environmental parameters and the operation parameters into an air conditioner load model to obtain the current load of the air conditioner;
at step S3, correcting the air conditioner operation capability according to the load variation of the air conditioner and the current operation capability of the air conditioner;
at step S4, the corrected air conditioner operation capacity, indoor temperature, outdoor temperature, and indoor humidity are input to the second neural network model to obtain the optimal operation parameters of the air conditioner.
Fig. 3 is a schematic diagram of an air conditioning system predictive neural network model according to an exemplary embodiment of the present invention.
As shown in fig. 3, an air conditioning system neural network prediction model is established, as shown in fig. 2, an input layer of the air conditioning system neural network prediction model comprises compressor frequency, room humidity, air deflector position, rotating speeds of an inner fan and an outer fan, opening degree of an electronic expansion valve and the like, a large amount of measured operation data of the air conditioner are used as model input and output layer parameters, and after the model is continuously trained and learned, the model can rapidly predict the operation capacity, power, energy efficiency and the like of the air conditioner at the moment according to the input parameters.
Fig. 4 is an air conditioner control strategy optimizing neural network model according to an exemplary embodiment of the present invention.
As shown in fig. 4, a corresponding relationship between the environmental parameter, the air conditioner output capacity power consumption and the controller parameter is established, and contrary to the prediction of fig. 3, the neural network model of fig. 4 can be used to predict the optimal controller parameter according to the current environmental parameter and the output capacity parameter of the air conditioner. As shown in FIG. 4, the input layer of the second neural network model (i.e., the air conditioner control strategy optimizing neural network model) includes the corrected air conditioner operation capacity Q and the outdoor temperature TwHumidity D in the room, indoor temperature Td. The output layer is the opening P of the electronic expansion valve, the running frequency F of the compressor and the rotating speed N of the external fanoInner fan rotating speed Ni。
FIG. 5 is an exemplary optimization control flow diagram at constant temperature load according to the present invention.
As shown in fig. 5, the principle of the constant temperature capability optimizing control is as follows: air conditioning cold dieIn the starting operation process, the system controller continuously obtains the environmental temperature (dry-bulb temperature) TdAnd a user-set temperature Ts. First, determine | Td-TsWhether the size of | is smaller than a set threshold epsilon, wherein epsilon can be taken as the following value range: 0.5-1 ℃.
According to one or more embodiments of the invention, when | Td-TsIf the value is greater than epsilon, the air conditioner is still in the cooling or heating regulation stage, and the refrigeration control mode is still executed again; when | Td-TsIf the | is less than or equal to epsilon, the temperature control of the air conditioner is basically close to the temperature set by the user, and a constant temperature control mode is entered; at the moment, the system controller obtains the temperature T of the dry bulbdHumidity DwOutdoor temperatureTwBesides, the current operating parameters (frequency F, inner fan rotating speed N) of the air conditioner need to be obtainediOuter fan speed NoOpening degree P) of electronic expansion valve and wall surface temperature Tq. The operation capacity Q of the air conditioner under the current operation condition can be calculated by utilizing the air conditioner neural network prediction modelnAnd power WnThe unit of the operation capacity of the air conditioner is watt, and the unit refers to the refrigerating capacity or the heating capacity output by the air conditioner in real time.
In addition, other parameters influencing the change of the room load can be calculated according to other temperature and humidity conditions; outdoor environmental load variation Δ QwAccording to the outdoor ambient temperature TwThe estimation calculation yields:
ΔQw＝f(Td)＝α1·(Tw,n-Tw,n-1)+β1formula (2)
Maintaining structural load variation Δ QqAccording to wall temperature TqThe estimation calculation yields:
ΔQq＝f(Tq)＝α2·(Tq,n-Tq,n-1)+γ2·(Tq,n 4-Tq,n-1 4)+β2formula (3)
Sensible and latent load Δ Qs+ΔQLAccording to the indoor air temperature TdAnd calculating an air humidity D estimate to obtain:
ΔQL+ΔQs＝f(Td,D)＝α3*(Td,n-Td,n-1)+γ3*Dn*(1+β3*Td,n)*[m1+m2*(Td,n)4+m3*lg(Td,n)]-γ3*Dn-1*(1+β3*Td,n-1)*[m1+m2*lg(Td,n-1)4+m3*lg(Td,n-1)]equation (4)
Wherein alpha is1，β1，α2，β2，γ2，α3，γ3，β3，，m1，m2，m3，If the parameter is a coefficient dimension of 1, if the parameter is a constant term, the dimension is J or kJ, lg represents a logarithmic function with a base 10, and n-1 represent the nth and nth-1 periods of the air conditioner operation. Wherein alpha is1，，α2，α3，β3，γ2，m2，γ3，m2，m3Is a coefficient, beta1，β2，，m1Is a constant term.
Then calculating the output load of the air conditioner required by the room to maintain the room at constant temperature in the nth sampling period
Q＝Qn+ΔQw+ΔQq+ΔQL+ΔQsFormula (5)
Wherein, is Δ Qw+ΔQq+ΔQL+ΔQsAnd Q is the air conditioner required refrigerating capacity compensated by other loads in order to maintain the variable quantity of other loads in the constant temperature state. Wherein Q and air-conditioning control parameter (frequency F, inner fan rotating speed N)iOuter fan speed NoElectronic expansion valve opening P) and current air conditioning environment parameter (dry bulb temperature T)dHumidity DwOutdoor temperature Tw) It is related.
When the room load demand Q obtained by calculation and the cold quantity Q output by the control strategy optimizing neural network are equivalent, but the power is minimum Wn＝min{W1，W2，W3… … (FIG. 4 neural network inputs)The optimal operation parameter of the layer is frequency F', and the rotating speed N of the internal fani', outer fan speed No'electronic expansion valve opening P') is provided for the system to execute, and the system executes the control which is matched with the actual environment load but has the minimum operation power at the moment, so that the further operation energy saving of the air conditioner is realized.
According to one or more embodiments of the present invention, in order to prevent the above control method from significantly affecting the indoor environment due to factors such as the ambient temperature and heat leakage of the opening and closing of the window and door, the room temperature variation is detected after the air conditioner executes the optimal control strategy, and if | Td-TsIf the load variation of the room is estimated to be larger if the value is greater than epsilon, the system control parameters are slowly restored to the original control strategy (frequency F, rotating speed N of the internal fan)iOuter fan speed NoElectronic expansion valve opening P), if Td-TsAnd if | ≦ epsilon, determining to execute the new control strategy.
According to one or more embodiments of the invention, the control strategy of fig. 5 is continuously updated and the constant temperature mode control strategy is executed by the system unless a shutdown command is received.
The control flow of FIG. 5 is shown as an example in accordance with one or more embodiments of the invention:
firstly, the air conditioner enters a constant temperature stage, and the room temperature approaches the temperature | T set by the userd-TsEpsilon is less than or equal to | is less than or equal to; secondly, the current air-conditioning operation strategy is (frequency F, inner fan rotation speed N)iOuter fan speed NoElectronic expansion valve opening P); thirdly, the real-time refrigerating capacity Q of the current air conditioner can be predicted through the neural network model in the figure 3nRunning power consumption W in real time; thirdly, the load variation quantity delta Q of the current load and the last detection period can be calculated according to the detected parametersw+ΔQq+ΔQL+ΔQs(ii) a Revising the current actual refrigerating capacity QnObtaining Q; thirdly, the optimal control strategy (frequency F', inner fan rotating speed N) under the compensated Q and power minimum optimizing target can be obtained through the neural network optimizing model shown in the figure 3i', outer fan speed No', electronic expansion valve opening P'); the last cycle row performs the logic described above.
According to one or more embodiments of the present invention, when the air conditioner is in the constant temperature control stage at the n-1 th cycle operation period, i.e. | Td-TsWhen | ≦ epsilon, after the optimal operation parameter with the minimum operation power is obtained through the calculation of the step S4 in the nth operation period, the air conditioner executes the following operation after executing the optimal operation parameter: if the indoor environment temperature greatly deviates from the set temperature in the nth operation period, namely | Td-TsIf the | is more than epsilon, replacing the operation parameter calculated in the n +1 th period with the operation parameter of the original n-1 period; if the environment temperature in the chamber in the nth operation period is still within the range of Td-Ts ≦ epsilon, the operation parameters executed in the nth period are continuously executed in the n +1 th period, wherein TdFor the air-conditioning dry bulb temperature, TsEpsilon is a preset threshold value for the temperature set by the user.
The invention also provides an air conditioner control device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is used for: when the computer program is executed, the above-described air conditioning control method is implemented.
The invention also provides an air conditioner which adopts the air conditioner control method or comprises the air conditioner control device.
According to one or more embodiments of the invention, a multi-dimensional load variation estimation model is established, the real-time capacity of the air conditioner is corrected, and the temperature control accuracy of the air conditioner in the constant temperature control stage is realized.
According to one or more embodiments of the invention, the air conditioner system capacity prediction neural network model and the strategy optimization model are utilized, and the room load correction model is combined, so that the temperature control stability, energy conservation and consumption reduction of the air conditioner at the constant temperature stage are further realized.
According to one or more embodiments of the invention, the room temperature fluctuation prevention control is added in the control of the invention, so that the room temperature control stability is further increased, and the influence of large load fluctuation on the indoor comfort is prevented.
In accordance with one or more embodiments of the present invention, control logic in methods of the present invention may implement processes such as the flows of the above methods of the present invention using encoded instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium (e.g., a hard disk drive, a flash memory, a read-only memory, an optical disk, a digital versatile disk, a cache, a random-access memory, and/or any other storage device or storage disk) in which information is stored for any period of time (e.g., for extended periods of time, permanent, transitory instances, temporary caches, and/or information caches). As used herein, the term "non-transitory computer-readable medium" is expressly defined to include any type of computer-readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media.
In accordance with one or more embodiments of the present invention, the method of the present invention may be implemented using control circuitry, (control logic, a master control system or control module), which may include one or more processors, or which may internally include a non-transitory computer-readable medium. In particular, the master control system or control module may comprise a microcontroller MCU. The processor implementing the processes of the present method may be such as, but not limited to, one or more single-core or multi-core processors. The processor(s) may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, etc.). The processor may be coupled thereto and/or may include a memory/storage device and may be configured to execute instructions stored in the memory/storage device to implement various applications and/or operating systems running on the controller in accordance with the present invention.
The drawings referred to above and the detailed description of the invention, which are exemplary of the invention, serve to explain the invention without limiting the meaning or scope of the invention as described in the claims. Accordingly, modifications may be readily made by those skilled in the art from the foregoing description. Further, those skilled in the art may delete some of the constituent elements described herein without deteriorating the performance, or may add other constituent elements to improve the performance. Further, the order of the steps of the methods described herein may be varied by one skilled in the art depending on the environment of the process or apparatus. Therefore, the scope of the present invention should be determined not by the embodiments described above but by the claims and their equivalents.
While the invention has been described in connection with what is presently considered to be practical embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (11)
1. An air conditioner control method includes:
s1: when the air conditioner enters a constant temperature mode, acquiring current environmental parameters and operation parameters of the air conditioner;
s2: inputting the environmental parameters and the operation parameters into a first neural network model of the air conditioner to obtain the current operation capacity and power of the air conditioner, and inputting the environmental parameters and the operation parameters into an air conditioner load model to obtain the current load of the air conditioner;
s3: correcting the operation capacity of the air conditioner according to the load variation of the air conditioner and the current operation capacity of the air conditioner;
s4: and inputting the corrected air conditioner operation capacity and the current environment parameter of the air conditioner into a second neural network model to obtain the optimal operation parameter of the air conditioner.
2. The method of claim 1, wherein: the load of the air conditioner includes an outdoor environmental load, a maintenance structure load, a latent heat load, and a sensible heat load.
3. The method of claim 1, wherein the step S3 includes: the corrected air conditioner operation capacity Q is Qn+ΔQw+ΔQq+ΔQL+ΔQsWherein Q is the corrected air conditioner operation capacity, QnFor the current operation capability of the air conditioner, Δ Qw is the outdoor environmental load variation, Δ QqTo maintain structural load variation; delta QsFor sensible load variation, Δ QLFor latent heat loadingThe amount of change.
4. The method according to claim 1, wherein the step S4 includes: obtaining the optimal operation parameter when the air conditioner operation power is minimum according to the corrected air conditioner operation capacity and the current environment parameter of the air conditioner, wherein the current environment parameter of the air conditioner comprises an outdoor environment parameter and an indoor environment parameter, the outdoor environment parameter comprises an outdoor temperature, and the indoor environment parameter comprises: indoor temperature, indoor humidity.
5. The method of claim 1, wherein the operating parameters of the air conditioner are an operating frequency of the air conditioner, an operating speed of an internal fan of the air conditioner, a speed of an external fan of the air conditioner, and an opening degree of an electronic expansion valve of the air conditioner.
6. The method of claim 1, wherein in the step S1, when | T | (T |)d-TsWhen | < epsilon, the air conditioner enters a constant temperature control mode, wherein TdFor the air-conditioning dry bulb temperature, TsEpsilon is a preset threshold value for the temperature set by the user.
7. The method of claim 6, wherein the method further comprises:
when the air conditioner is in the constant temperature control stage in the (n-1) th cycle operation period, namely | Td-TsWhen | ≦ epsilon, after the optimal operation parameter with the minimum operation power is obtained through the calculation of the step S4 in the nth operation period, the air conditioner executes the following operation after executing the optimal operation parameter:
if the indoor environment temperature greatly deviates from the set temperature in the nth operation period, namely | Td-TsIf the | is more than epsilon, replacing the operation parameter calculated in the n +1 th period with the operation parameter of the original n-1 period;
if the ambient temperature in the chamber in the nth operation period is still in the range of Td-Ts | ≦ epsilon, the operation parameters executed in the nth period are continuously executed in the n +1 th period,
wherein T isdFor air conditioningDry bulb temperature, TsEpsilon is a preset threshold value for the temperature set by the user.
8. The method of claim 1, wherein the current environmental parameters comprise: indoor temperature Tq，Indoor humidity DwAir temperature T of air conditionerdOutdoor temperature TwWherein the indoor temperature TqThe calculation method is as follows: t isq＝a*TInner, 0*(b*t2+c)-d(e*t3+f*t2+ g × t + h)/μ, wherein a, b, c, d, e, f and h are correction coefficients; t is the air-conditioning running time after the set temperature is reached; t isInner, 0The room temperature changes at the moment of starting up; mu is the wall mean heat capacity, wherein Td、Dw、TwObtained by temperature and humidity sensors of the air conditioner.
9. The method of claim 3, wherein the outdoor environmental load variation Δ Q of the current operation cycle nwAccording to the outdoor ambient temperature TwThe estimation calculation yields: delta Qw＝f(Td)＝α1·(Tw,n-Tw,n-1)+β1；
Maintaining structural load variation Δ QqAccording to wall temperature TqThe estimation calculation yields:
ΔQq＝f(Tq)＝α2·(Tq,n-Tq,n-1)+γ2·(Tq,n 4-Tq,n-1 4)+β2；
sensible and latent load Δ Qs+ΔQLAccording to the indoor air temperature TdAnd calculating an air humidity D estimate to obtain:
ΔQL+ΔQs＝f(Td,D)＝α3*(Td,n-Td,n-1)+γ3*Dn*(1+β3*Td,n)*[m1+m2*(Td,n)4+m3*lg(Td,n)]-γ3*Dn-1*(1+β3*Td,n-1)*[m1+m2*lg(Td,n-1)4+m3*lg(Td,n-1)]，
wherein alpha is1，β1，α2，β2，γ2，α3，γ3，β3，，m1，m2，m3And (3) calculating coefficients for the constant terms, wherein if the parameter is a coefficient dimension of 1, if the parameter is a constant term, the dimension is J or kJ, lg represents a logarithmic function with the base 10, and n-1 represent the n-th and n-1-th periods of the air conditioner operation.
10. An air conditioning control apparatus comprising a memory storing a computer program and a processor for: when executing the computer program, implementing the method according to any of claims 1-9.
11. An air conditioner employing the air conditioner control method according to any one of claims 1 to 9, or comprising the air conditioner control device according to claim 10.
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