CN115435385A - Multi-split air conditioning system, control method thereof and storage medium - Google Patents
Multi-split air conditioning system, control method thereof and storage medium Download PDFInfo
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- CN115435385A CN115435385A CN202110619525.3A CN202110619525A CN115435385A CN 115435385 A CN115435385 A CN 115435385A CN 202110619525 A CN202110619525 A CN 202110619525A CN 115435385 A CN115435385 A CN 115435385A
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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
- F24F1/00—Room units for air-conditioning, e.g. separate or self-contained units or units receiving primary air from a central station
- F24F1/0003—Room units for air-conditioning, e.g. separate or self-contained units or units receiving primary air from a central station characterised by a split arrangement, wherein parts of the air-conditioning system, e.g. evaporator and condenser, are in separately located units
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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
- F24F11/88—Electrical aspects, e.g. circuits
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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
- F24F11/89—Arrangement or mounting of control or safety devices
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Abstract
The application discloses air conditioning system of multi-split air conditioner and control method, storage medium thereof, the control method includes: acquiring a load prediction parameter of the air conditioning system; obtaining a predicted total load rate of the air conditioning system based on the load prediction parameters; acquiring a correction coefficient based on the actual total load rate of the air conditioning system; correcting the predicted total load rate by using the correction coefficient; and acquiring a control target value based on the corrected predicted total load rate, and controlling the air conditioning system through the control target value. Through the mode, the predicted total load rate is corrected on line through the correction coefficient, so that the accuracy of the predicted total load rate is improved, the prediction accuracy of the air-conditioning system is improved, and the reliability of the air-conditioning system is improved.
Description
Technical Field
The application relates to the technical field of air conditioners, in particular to a multi-split air conditioner system, a control method thereof and a storage medium.
Background
The multi-connected air conditioning system is generally applied to buildings such as malls, office buildings or hospitals. The multi-split air conditioning system may include an indoor unit and a plurality of indoor units, each indoor unit being for independently controlling one room or hot zone, and each indoor unit being connected to the outdoor unit through an expansion valve. The multi-split air conditioning system controls the opening degree of the expansion valve to distribute the refrigerating capacity or the heating capacity to each indoor unit.
In the process of predicting the load, the air conditioning system of the multi-split air conditioner system cannot avoid prediction errors because the air conditioning system of the multi-split air conditioner system is not provided with feedback, so that the prediction accuracy of the air conditioning system of the multi-split air conditioner system is low.
Disclosure of Invention
The application provides a multi-split air conditioning system, a control method thereof and a storage medium, which aim to solve the technical problem of low prediction precision in the prior art.
In order to solve the technical problem, the application adopts a technical scheme that: the control method is applied to an air conditioning system of a multi-split air conditioner, and comprises the following steps:
acquiring a load prediction parameter of the air conditioning system;
obtaining a total predicted load rate of the air conditioning system based on the load prediction parameters;
acquiring a correction coefficient based on the actual total load rate of the air conditioning system;
correcting the predicted total load rate by using the correction coefficient;
and acquiring a control target value based on the corrected predicted total load rate, and controlling the air conditioning system through the control target value.
Wherein the step of obtaining a correction factor based on an actual total load factor of the air conditioning system comprises:
calculating an average value of the actual total load rate of the air conditioning system in a first preset time period to obtain a first average value;
calculating the average value of the total predicted load rate of the air conditioning system in the first preset time period to obtain a second average value;
calculating a ratio between the first average value and the second average value to obtain the correction coefficient in a second preset time period after the first preset time period.
Wherein the step of calculating the average value of the actual total load rate of the air conditioning system in a first preset time period comprises:
calculating an actual total load value corresponding to a plurality of first time points of the air conditioning system in the first preset time period according to the operation data of the air conditioning system at the first time points;
calculating a ratio between each actual total load value and a rated total load value of the air conditioning system to obtain the actual total load rate corresponding to the first time point;
averaging the actual total load rates corresponding to the plurality of first time points to obtain an average value of the actual total load rates.
Wherein the step of obtaining a predicted total load rate of the air conditioning system based on the load prediction parameter comprises:
inputting the load prediction parameters into a load prediction model to obtain a total predicted load value of the air conditioning system;
and calculating the ratio of the total predicted load value to the rated total load value of the air conditioning system to obtain the total predicted load rate.
Wherein the load prediction parameters include at least one or a combination of meteorological data, indoor temperature measurements, indoor temperature setpoints, or indoor heat source information.
Wherein the load prediction model comprises a pre-operation model, and the step of obtaining the predicted total load rate of the air conditioning system based on the load prediction parameters comprises:
and in a pre-operation stage after the air conditioning system is started, inputting the load prediction parameters into the pre-operation model.
Wherein the load prediction model further comprises a formal model, and the step of obtaining the predicted total load rate of the air conditioning system based on the load prediction parameters further comprises:
and in a formal operation stage after the air conditioning system operates for a preset time, training by using the operation data of the air conditioning system to obtain the formal model, and inputting the load prediction parameters into the formal model.
Wherein the training of the operational data of the air conditioning system to obtain the formal model comprises:
updating the formal model based on the operational data over a preset period.
Wherein the controlling of the air conditioning system by the control target value includes:
obtaining a first energy consumption of the air conditioning system based on the control target value currently used by the air conditioning system;
obtaining a second energy consumption of the air conditioning system based on the control target value obtained by current calculation of the air conditioning system;
and obtaining the energy saving rate of the air conditioning system based on the first energy consumption and the second energy consumption.
Wherein the controlling of the air conditioning system by the control target value further includes:
in response to the energy saving rate being greater than or equal to a preset threshold value, replacing the currently used control target value with the currently calculated control target value;
and responding to the energy saving rate smaller than the preset threshold value, and reserving the currently used control target value.
In order to solve the technical problem, the application adopts a technical scheme that: provided is a multi-split air conditioning system, which includes a processor and a memory; the memory stores a computer program, and the processor is used for executing the computer program to realize the control method.
In order to solve the technical problem, the application adopts a technical scheme that: there is provided a computer-readable storage medium storing program instructions that can be executed to implement the control method described above.
The beneficial effects of the embodiment of the application are that: the control method comprises the steps of obtaining a load prediction parameter of the air conditioning system, obtaining a total prediction load rate of the air conditioning system based on the load prediction parameter, obtaining a correction coefficient based on an actual total load rate of the air conditioning system, correcting the total prediction load rate by using the correction coefficient, obtaining a control target value based on the corrected total prediction load rate, and controlling the air conditioning system through the control target value. The method and the device have the advantages that the predicted total load rate is corrected on line through the correction coefficient, so that the accuracy of the predicted total load rate is improved, the prediction accuracy of the air-conditioning system is improved, and the reliability of the air-conditioning system is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without inventive efforts, wherein:
FIG. 1 is a block diagram of an embodiment of a multi-split air conditioning system of the present application;
FIG. 2 is a schematic flow chart diagram of an embodiment of a control method of the present application;
FIG. 3 is a flowchart illustrating an embodiment of step S202 in FIG. 2;
FIG. 4 is a flowchart illustrating an embodiment of step S203 in FIG. 2;
FIG. 5 is a flowchart illustrating an embodiment of step S401 in FIG. 4;
FIG. 6 is a flowchart illustrating an embodiment of step S205 in FIG. 2;
FIG. 7 is a schematic diagram of the relationship between the evaporating temperature set point and the predicted total load rate of the air conditioning system of the present application;
FIG. 8 is a schematic diagram of a simulation of the air conditioning system of the present application;
FIG. 9 is a block diagram of an embodiment of the air conditioning system of the present application;
FIG. 10 is a block diagram of another embodiment of the air conditioning system of the present application;
FIG. 11 is a schematic structural diagram of an embodiment of a computer storage medium according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures associated with the present application are shown in the drawings, not all of them. 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 application.
As shown in fig. 1, the multi-split air conditioning system 1 of the present application may include an outdoor unit 11 and a plurality of indoor units 12, and each of the plurality of indoor units 12 is connected to the outdoor unit 11. The outdoor unit 11 includes, but is not limited to, a controller 111, a compressor 112, and an external fan 113; in fig. 1, the number of the indoor units 12 is 3, each indoor unit 12 is connected to the outdoor unit 11 through an expansion valve (not shown), and the multi-split air conditioning system 1 adjusts the opening degree of the expansion valve to adjust the flow rate of the refrigerant input to the indoor unit 12.
In one embodiment, the multi-split air conditioning system 1 is controlled by using a PID (proportional-derivative-Integral) as an observed quantity, and the outdoor dry bulb temperature of the multi-split air conditioning system 1, the heat exchanger surface temperature of the indoor unit 12, the heat exchanger surface temperature of the outdoor unit 11, the heat exchanger surface temperature of the indoor unit 12, the inlet-outlet pressure of the compressor 112, and the like are used as a controlled quantity, and the frequency of the compressor 112, the rotational speed of the external fan 113, the rotational speed of the fan of the indoor unit 12, and the opening degree of the expansion valve of the multi-split air conditioning system 1 are used as a controlled quantity. The air conditioning system 1 of the multi-split air conditioner adjusts the control quantity of the air conditioning system 1 of the multi-split air conditioner according to the difference between the measured value of the observed quantity and the set value.
The multi-split air conditioning system 1 adopts PID control, and cannot obtain the cooling capacity or heating capacity required for reaching the set temperature, so that the compressor 112 and the external fan 113 operate at the preset maximum rotation speed, so that the multi-split air conditioning system 1 reaches the set temperature; the compressor 112 and the external fan 113 then run down, maintaining the set point operation. When the load of the multi-split air conditioning system 1 changes and the indoor temperature exceeds a set value and reaches a preset range, the multi-split air conditioning system 1 controls the compressor 112 and the external fan 113 to work in a variable frequency mode; when the indoor temperature exceeds the set value and does not reach the preset range, the multi-split air conditioning system 1 maintains the set value to operate; therefore, the cooling capacity or the heating capacity of the present embodiment cannot be matched with the load in real time.
In order to solve the problem that the refrigerating capacity or the heating capacity cannot be matched with the load in real time, the application provides a control method. Referring to fig. 2, fig. 2 is a schematic flow chart of an embodiment of a control method of the present application. The control method of the present embodiment is applied to the multi-split air conditioning system 1 shown in fig. 1; in other embodiments, the control method may be applied to a multi-split air conditioning system adopting other structures. The control method comprises the following steps:
s201: a load prediction parameter of the air conditioning system 1 is obtained.
The load prediction parameters include at least one or a combination of meteorological data, indoor temperature measurements, indoor temperature setpoints, or indoor heat source information. The air conditioning system 1 may obtain the load prediction parameter of the air conditioning system 1 through local collection or a network.
Wherein, the meteorological data collected by the air conditioning system 1 comprises outdoor meteorological data; the outdoor weather data may include outdoor temperature, outdoor relative humidity, or angle of solar illumination, among others. Specifically, the air conditioning system 1 acquires a weather forecast through a network to acquire outdoor weather data from the weather forecast; or the air conditioning system 1 directly detects the outdoor weather data through the outdoor unit 11, the outdoor unit 11 may be provided with a temperature sensor or a temperature detection device for detecting the outdoor weather data.
The temperature value measured in the room or hot zone in which each indoor unit 12 of the air conditioning system 1 is located is taken as an indoor temperature measurement value. The air conditioning system 1 can directly measure the temperature value of the room or the hot zone where the indoor unit 12 is located through the indoor unit 12. The indoor temperature set value indicates an indoor temperature value preset by the air conditioning system 1, wherein the air conditioning system 1 operates based on the indoor temperature set value such that the indoor temperature measurement value reaches the indoor temperature set value. Specifically, the controller 111 acquires an indoor temperature set value, and controls the operation of the compressor 112 and the outdoor fan 113 based on the indoor temperature set value.
The indoor heat source information may include indoor heat source equipment information or indoor personnel information, the indoor heat source equipment may be equipment generating heat indoors, and the indoor heat source equipment may include a gas stove; the indoor personnel information may include personnel information and location information of personnel in the room.
In an embodiment, the air conditioning system 1 may perform preprocessing on the load prediction parameters after acquiring the load prediction parameters, so as to check the validity of the load prediction parameters. If the air conditioning system 1 does not detect that the load prediction parameter has an error, the air conditioning system 1 locally stores the acquired load prediction parameter so that the air conditioning system 1 operates based on the load prediction parameter. If the air conditioning system 1 detects that the load prediction parameter has an error, the air conditioning system 1 deletes the load prediction parameter and obtains the load prediction parameter stored in the air conditioning system 1 at the previous time, so that the air conditioning system 1 operates based on the load prediction parameter at the previous time.
Through the manner, the air conditioning system 1 of the embodiment improves the accuracy of the load prediction parameter by checking the validity of the load prediction parameter. Alternatively, the air conditioning system 1 may be provided with a data update period, and the air conditioning system 1 uploads the locally stored data to a server (not shown) for storage according to the data update period. Specifically, the data update period is set to 24 hours, and the air conditioning system 1 uploads the locally stored data to the server for storage every 24 hours, so as to prevent the data locally stored by the air conditioning system 1 from being lost.
S202: the predicted total load rate of the air conditioning system 1 is obtained based on the load prediction parameter.
The air conditioning system 1 acquires the load prediction parameters from the local storage or the server, and acquires the total predicted load rate of the air conditioning system 1 through the load prediction parameters. The air conditioning system 1 is preset with a load prediction model, or the server stores the load prediction model, and the air conditioning system 1 obtains the load prediction model from the server. The load prediction model is used for predicting the air conditioning system 1, and as shown in fig. 3, step S202 includes the following steps:
s301: the load prediction parameters are input to the load prediction model to obtain a predicted total load value of the air conditioning system 1.
The air conditioning system 1 inputs the obtained load prediction parameters into the load prediction model, that is, the load prediction model operates based on the load prediction parameters, so that the load prediction model predicts a total predicted load value, which may include a predicted cold load value and a predicted heat load value. When the air conditioning system 1 is in the cooling mode, predicting the total load value as a predicted cooling load value; when the air conditioning system 1 is in the heating mode, the predicted total load value is the predicted thermal load value.
The predicted total load value may be a load value predicted by the air conditioning system 1 for each room or each hot zone, or the predicted total load value may be a load value predicted by the air conditioning system 1 for the entire rooms or the hot zones of the air conditioning system 1.
S302: the ratio between the predicted total load value and the rated total load value of the air conditioning system 1 is calculated to obtain the predicted total load rate.
The air conditioning system 1 divides the predicted total load value by the rated total load value of the air conditioning system 1 to obtain an operation result, and the operation result is used as the predicted total load rate. The rated total load value of the air conditioning system 1 includes a rated cooling total load value and a rated heating total load value. When the air conditioning system 1 is in the cooling mode, the air conditioning system 1 divides the predicted cooling load value by the rated cooling total load value to obtain a predicted total load rate; when the air conditioning system 1 is in the heating mode, the air conditioning system 1 divides the predicted heat load value by the rated heating total load value to obtain a predicted total load factor.
Specifically, the air conditioning system 1 collects the load prediction parameters at the current time, the load prediction parameters at the first N1 times, and the outdoor meteorological data at the last N2 times, where N1 times are N1 times before the current time, N2 times are N2 times after the current time, N1 ranges from 0 to 23 hours, and N2 ranges from 1 to 24 hours.
The air conditioning system 1 obtains the outdoor meteorological data of the last N2 moments through weather forecast, inputs the load prediction parameters of the current moment, the load prediction parameters of the first N1 moments and the outdoor meteorological data of the last N2 moments into the load prediction model, and predicts the total predicted load rate of the air conditioning system 1 at the future N2 moments through the load prediction model.
S203: the correction coefficient is obtained based on the actual total load factor of the air conditioning system 1.
The air conditioning system 1 obtains the correction coefficient through calculation of an actual total load rate of the air conditioning system 1, wherein the actual total load rate is the total load rate obtained through real-time calculation when the air conditioning system 1 operates at the current moment.
S204: and correcting the predicted total load rate by using the correction coefficient.
The air conditioning system 1 calculates the predicted total load factor based on the correction coefficient to realize the correction. Specifically, the air conditioning system 1 multiplies the predicted total load factor by the correction coefficient to obtain a corrected predicted total load factor. In other embodiments, the air conditioning system 1 divides the predicted total load factor by the correction factor to obtain a corrected predicted total load factor. The method and the device calculate the predicted total load rate through the correction coefficient so that the corrected predicted total load rate is closer to the actual total load rate.
S205: a control target value is obtained based on the corrected predicted total load factor, and the air conditioning system 1 is controlled by the control target value.
After the air conditioning system 1 calculates the predicted total load factor based on the correction coefficient, the air conditioning system 1 obtains a control target value based on the corrected predicted total load factor, and the control target value may include, but is not limited to, an evaporation temperature set value Tes, a condensation temperature set value Tcs, and an indoor temperature set value of the air conditioning system 1. The air conditioning system 1 calculates a control target value based on the predicted total load factor by using an existing calculation method. The air conditioning system 1 controls the operation of the compressor 112 and the external fan 113 of the air conditioning system 1 based on the evaporation temperature set value Tes, the condensation temperature set value Tcs, and the indoor temperature set value, so as to control the operation of the air conditioning system 1.
Specifically, the air conditioning system 1 adjusts the corresponding control amount based on the control target value, wherein the air conditioning system 1 adjusts the control amounts such as the frequency of the compressor 112, the rotation speed of the external fan 113, and the opening degree of the expansion valve, so that the observed amount of the air conditioning system 1 reaches the control target value, wherein the observed amount of the air conditioning system 1 may be the indoor temperature, the outer surface temperature of the indoor heat exchanger, the outer surface temperature of the outdoor heat exchanger, and the inlet-outlet pressure of the compressor 112. Therefore, the air conditioning system 1 of the present embodiment can output the heating amount or the cooling amount that matches the control target value with lower energy consumption.
The air conditioning system 1 of the embodiment corrects the predicted total load rate by using the correction coefficient to realize online correction of the predicted total load rate, and the corrected predicted total load rate is closer to the actual total load rate, so that the accuracy of the predicted total load rate is improved, the prediction accuracy of the air conditioning system 1 is improved, and the reliability of the air conditioning system 1 is improved. In addition, the load prediction parameters include indoor heat source information (i.e., the load of the air conditioning system 1), and the air conditioning system 1 obtains the total predicted load rate of the air conditioning system 1 based on the load prediction parameters, that is, the load prediction parameters also change in real time when the load of the air conditioning system 1 changes, so as to solve the problem that the cooling capacity or the heating capacity cannot be matched with the load in real time, and improve the performance of the air conditioning system 1.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating an embodiment of the step S203 in fig. 2. Step S203 includes the steps of:
s401: and calculating the average value of the actual total load rate of the air conditioning system 1 in the first preset time period to obtain a first average value.
The first preset time may include a current time and the first N1 times before the current time. Specifically, the air conditioning system 1 calculates the sum of the actual total load rates at the current time and the first N1 times, and divides the sum of the actual total load rates at the current time and the first N1 times by the number of all the actual total load rates to obtain a first average value.
S402: and calculating the average value of the predicted total load rate of the air conditioning system 1 in the first preset time period to obtain a second average value.
The air conditioning system 1 further calculates an average value of the predicted total load rates of the air conditioning system 1 at the current time and the first N1 times, and specifically, the air conditioning system 1 acquires the predicted total load rates at the current time and the first N1 times, calculates a sum of the predicted total load rates at the current time and the first N1 times, and divides the sum of the predicted total load rates at the current time and the first N1 times by the number of all the predicted total load rates to obtain a second average value.
S403: and calculating the ratio of the first average value to the second average value to obtain the correction coefficient in a second preset time period after the first preset time period.
The air conditioning system 1 divides the first average value by the second average value to obtain the correction coefficient in a second preset time period after the current time. Wherein, the second preset time period may include N2 time instants after the current time instant, i.e., N2 time instants in the future. At this time, in step S204, the air conditioning system 1 may multiply the predicted total load factor by the correction coefficient to obtain the corrected predicted total load factor.
In other embodiments, the air conditioning system 1 divides the second average value by the first average value to obtain the correction coefficient in a second preset time period after the current time. In this case, in step S204, the air conditioning system 1 may divide the predicted total load factor by the correction coefficient to obtain a corrected predicted total load factor.
In this embodiment, the correction coefficient in the second preset time period after the current time is obtained by dividing the first average value by the second average value, so that the accuracy of the correction coefficient can be improved, the prediction accuracy of the air conditioning system 1 can be further improved, and the reliability of the air conditioning system 1 can be improved.
Referring to fig. 5, fig. 5 is a schematic flowchart illustrating an embodiment of the step S401 in fig. 4. Step S401 includes the steps of:
s501: and calculating an actual total load value corresponding to the first time point according to the operation data of the air conditioning system 1 at a plurality of first time points in a first preset time period.
The air conditioning system 1 acquires operation data in a current time and the first N1 times, where the current time and the first N1 times are a plurality of first time points, and the operation data may include, but is not limited to, an observed amount and a controlled amount of the air conditioning system 1. The air conditioning system 1 calculates an actual total load value corresponding to the first time point based on the operation data of the first time point, for example, if the first time point is the current time, the air conditioning system 1 obtains the operation data of the current time, and calculates a corresponding actual total load value based on the operation data of the current time.
S502: the ratio between each actual total load value and the rated total load value of the air conditioning system 1 is calculated to obtain the actual total load rate corresponding to the first time point.
The air conditioning system 1 calculates a plurality of actual total load values corresponding to the current time and the previous N1 times, and divides each actual total load value by a rated total load value of the air conditioning system 1 to obtain an actual total load rate corresponding to the first time point. Wherein the actual total load value may include an actual thermal load value and an actual cold load value; when the air conditioning system 1 is in the cooling mode, the air conditioning system 1 calculates a ratio between an actual cooling load value and a rated cooling total load value; when the air conditioning system 1 is in the heating mode, the air conditioning system 1 calculates a ratio between the actual heat load value and the rated heating total load value.
S503: and averaging the actual total load rates corresponding to the plurality of first time points to obtain an average value of the actual total load rates. The air conditioning system 1 calculates the sum of the actual total load rates corresponding to the current time and the previous N1 times, and then divides the sum of the actual total load rates by the number of the first time points to obtain an average value of the actual total load rates.
The air conditioning system 1 comprises a pre-operation stage and a formal operation stage after starting, namely the air conditioning system 1 enters the pre-operation stage after starting; and entering a formal operation stage after the air conditioning system 1 operates for a preset time. The load prediction model comprises a pre-operation model and a formal model.
Step S301 further includes: in the pre-operation stage after the air conditioning system 1 is started, the load prediction parameters are input to the pre-operation model. Specifically, after the air conditioning system 1 is started, the air conditioning system 1 is in the pre-operation stage, so that the air conditioning system 1 can obtain the pre-operation model, and the predicted total load rate of the air conditioning system 1 in the pre-operation stage is obtained through the load prediction parameters and the pre-operation model. For example: the air-conditioning system 1 stores the pre-operation model locally, and then the air-conditioning system 1 directly obtains the pre-operation model from the local storage; or, the server is configured to store the pre-operation model, and the air conditioning system 1 acquires the pre-operation model from the server, so as to obtain the predicted total load rate of the air conditioning system 1 in the pre-operation stage through the load prediction parameter and the pre-operation model.
The pre-operation model does not need the operation data of the air-conditioning system 1, that is, the air-conditioning system 1 does not have the operation data of the history in the pre-operation stage, so that the air-conditioning system 1 calls the pre-operation model, the situation that the total predicted load rate cannot be obtained under the condition that the operation data of the history does not exist can be avoided, and the prediction accuracy of the air-conditioning system 1 is improved. Specifically, the pre-operation model may be obtained by performing offline training on load data of a typical building similar to the building environment where the air conditioning system 1 is located, for example, if the building environment where the air conditioning system 1 is located is a hotel, the server may obtain the load data of the hotel and perform offline training on the load data to obtain the data model.
The pre-run model includes, but is not limited to: multivariate linear regression Models (MLR), long Short-Term Memory neural network models (LSTM), multi-layer Perceptron Models (MLP), and XGboost models, among others. The air conditioning system 1 inputs load prediction parameters to the pre-operation model, for example, the load prediction parameters include: the outdoor meteorological data, the time parameter, the indoor temperature set value, the number of indoor people, the heat dissipation capacity data of the equipment at the current time, the outdoor meteorological data and the indoor temperature set value at the N2 time, so that the air conditioning system 1 obtains the predicted total load rate based on the pre-operation model.
The existing model needs to be established based on certain operation data, so that the prediction can be realized only after the system operates for a period of time; the pre-operation model of the application does not need the operation data of the air conditioning system 1, and the air conditioning system 1 can directly call the pre-operation model without waiting, so that the efficiency is improved. Furthermore, the control method of the application corrects the predicted total load rate through the correction coefficient, so that the predicted total load rate is corrected on line, the reliability of prediction is improved, and the robustness of the air-conditioning system 1 is improved; in the pre-operation stage, the pre-operation model is limited by universality, a building environment which does not completely adapt to the air conditioning system 1 may exist, and the deviation of the predicted total load rate is reduced by correcting the predicted total load rate on line.
In other embodiments, the server stores load data of the building environment where the air conditioning system 1 is located, and the server may establish a formal model based on the load data, so that the air conditioning system 1 obtains the formal model from the server. Through the mode, the server or the air conditioning system 1 does not need to call a pre-operation model, the calculation amount of the server or the air conditioning system 1 can be reduced, and the calculation speed is improved.
After the air conditioning system 1 operates for a preset time, the air conditioning system 1 uploads the locally stored data to the server, so that the server acquires the operating data of the air conditioning system 1. When the server acquires enough operation data, the server trains the pre-operation model through the operation data to obtain a formal mode.
Therefore, step S301 further includes: in the formal operation stage after the air conditioning system 1 operates for the preset time, the formal model is obtained by utilizing the operation data training of the air conditioning system 1, and the load prediction parameters are input into the formal model. The formal model may include, but is not limited to, a type of pre-operation model, and may be a multivariate linear regression model or a long-short term memory neural network model.
Optionally, the step of training the formal model by using the operation data of the air conditioning system 1 further includes: and updating the formal model based on the operation data in the preset period. The air conditioning system 1 stores the operation data in the preset period to the server, the server acquires the operation data in the preset period, and trains the formal model through the operation data in the preset period to update the formal model, wherein the preset period can be 24 hours.
The server is used for establishing and updating the load prediction model so as to improve the prediction precision of the air conditioning system; the air conditioning system 1 obtains the predicted total load rate and the control target value of the air conditioning system 1 through the controller 111, so that the communication time delay between the air conditioning system 1 and the server is avoided, and the stability of the air conditioning system 1 is further improved.
Referring to fig. 6, fig. 6 is a flowchart illustrating an embodiment of step S205 in fig. 2. Step S205 further includes the steps of:
s601: based on the control target value currently used by the air conditioning system 1, the first energy consumption of the air conditioning system 1 is obtained.
The air conditioning system 1 obtains a control target value used currently, and obtains the first energy consumption of the air conditioning system 1 through calculation of the control target value used currently, so as to obtain the energy consumption of the air conditioning system 1 at the current moment.
S602: and obtaining the second energy consumption of the air conditioning system 1 based on the control target value obtained by current calculation of the air conditioning system 1.
The air conditioning system 1 obtains the control target value through calculation in the above steps S201 to S205, that is, the control target value obtained through prediction by the air conditioning system 1 through the load prediction model; and the second energy consumption of the air conditioning system 1 is predicted through the control target value obtained by the current calculation, namely the air conditioning system 1 predicts the energy consumption at the future N2 moment.
S603: and obtaining the energy saving rate of the air conditioning system 1 based on the first energy consumption and the second energy consumption.
Specifically, the air conditioning system 1 may calculate a difference between the first energy consumption and the second energy consumption, and calculate a ratio between the difference and the first energy consumption to obtain the energy saving rate of the air conditioning system 1. In order to increase the energy saving rate of the air conditioning system 1, the air conditioning system 1 is preset with a preset threshold value, and the energy saving rate is compared with the preset threshold value. The preset threshold may be 0.5% to 1%, for example, 0.5%, 0.75%, or 1%.
S604: and in response to the energy saving rate being greater than or equal to the preset threshold, replacing the currently used control target value with the currently calculated control target value.
When the air conditioning system 1 judges that the energy saving rate is greater than or equal to the preset threshold, the air conditioning system 1 replaces the currently used control target value with the currently calculated control target value, and the air conditioning system 1 is controlled to operate through the currently calculated control target value at the moment N2, so that energy saving is achieved.
S605: and responding to the energy saving rate being smaller than the preset threshold value, and keeping the currently used control target value.
If the air conditioning system 1 determines that the energy saving rate is smaller than the preset threshold, the air conditioning system 1 retains the currently used control target value.
The existing air conditioning system has unknown load, so the existing air conditioning system sets the evaporation temperature set value Tes to be lower, for example, the evaporation temperature set value Tes is 8-10 ℃ lower than the return air dew point temperature, the evaporation temperature set value Tes is 5 ℃, and in order to meet the temperature control requirements of different building environments and different load rates, the existing air conditioning system has low efficiency. The air conditioning system 1 of the present application can obtain the predicted total load rate by the above control method, calculate the evaporation temperature set value Tes based on the predicted total load rate, and further control the frequency of the compressor 112 and the control amount such as the rotation speed of the external fan 113, wherein the correspondence relationship between the evaporation temperature set value Tes and the predicted total load rate is shown in fig. 7.
As shown in fig. 7, the air-conditioning system 1 is in the pre-operation stage after being started, and the upper limit Tesmax of the evaporation temperature setting value of the air-conditioning system 1 may be set to 10 ℃, so that the air-conditioning system 1 has sufficient adjustment margin to satisfy the condition that the pre-operation model underestimates the load of the air-conditioning system 1.
After the air conditioning system 1 is in the formal operation stage after the operation for the preset time, the air conditioning system 1 may increase the upper limit Tesmax of the evaporation temperature setting value, for example, the upper limit Tesmax of the evaporation temperature setting value is set to 12 ℃, so that the predicted total load rate obtained by the air conditioning system 1 through the formal model is matched with the real-time evaporation temperature, thereby achieving energy saving.
In an application scenario embodiment, the multi-split air conditioning system 1 is a one-to-four multi-split air conditioning system, and the rated cooling capacity of the multi-split air conditioning system 1 is set to 30KW. The air conditioning system 1 is installed in an office building, and a simulation diagram of the air conditioning system 1 is shown in fig. 8 on the premise that the air conditioning system operates for one week in May.
A curve 81 in fig. 8 represents a simulation result of the air conditioning system 1 adopting the control method of the prior art, a curve 82 in fig. 8 represents a simulation result of the air conditioning system 1 adopting the control method disclosed in the above embodiment, and by comparing the curve 81 and the curve 82, it can be obtained that: when the air-conditioning system 1 is in the pre-operation stage, compared with the existing control method, the energy saving rate of the air-conditioning system 1 operated by adopting the control method is 6%; the air conditioning system 1 is in a formal operation stage after the air conditioning system operates for a preset time, and compared with the existing control method, the energy saving rate of the air conditioning system 1 operating by adopting the control method is 9% so as to realize energy saving.
Referring to fig. 9, fig. 9 is a schematic diagram of an embodiment of an air conditioning system according to the present application. The air conditioning system 1 of the present embodiment includes a data processing module 130, a load predicting module 131, a control module 132, and a load correcting module 133, wherein the data processing module 130 is connected to the load predicting module 131, the control module 132, and the load correcting module 133, and the load predicting module 131 is connected to the control module 132 through the load correcting module 133.
The data processing module 130 is configured to collect, pre-process, transmit, and store data, where the data includes the data disclosed in the foregoing embodiments, specifically, the operation data, the load prediction parameter, the corrected predicted total load rate, the control target value, the control amount, the observed amount, and the like. The data processing module 130 may be configured to collect measured values of the data, for example, the data processing module 130 collects an actual total load rate and a load prediction parameter of the air conditioning system 1; the data processing module 130 can also obtain weather forecast through the network to obtain outdoor weather data.
The data processing module 130 may be configured to perform preprocessing on the collected data, wherein the data processing module 130 performs preprocessing on the load prediction parameter to check the validity of the load prediction parameter. The data processing module 130 further stores the preprocessed data, for example, the data processing module 130 stores the preprocessed load prediction parameters. The data processing module 130 further uploads the locally stored data to the server for storage according to the data update period.
The load prediction module 131 is provided with a pre-operation model and a formal model, and is used for obtaining a total predicted load rate through a load prediction parameter and the pre-operation model in a pre-operation stage after the air conditioning system 1 is started; the method is used for training a pre-operation model through operation data of the air conditioning system 1 to obtain a formal model in a formal operation stage after the air conditioning system 1 operates for a preset time, and obtaining a total predicted load rate through load prediction parameters and the formal model.
The load correction module 133 is configured to obtain a correction coefficient based on the actual total load rate of the air conditioning system 1, and correct the predicted total load rate by using the correction coefficient. The control module 132 is configured to obtain a control target value based on the corrected predicted total load factor, and control the air conditioning system 1 by the control target value.
Referring to fig. 10, fig. 10 is a schematic diagram of a framework of another embodiment of the air conditioning system of the present application, and the air conditioning apparatus 1 of the present embodiment includes a processor 21 and a memory 22. Wherein the memory 22 has stored therein a computer program, and the processor 21 is configured to execute the computer program to implement the above-mentioned control method.
The processor 21 may be an integrated circuit chip having signal processing capability. The processor 21 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Referring to fig. 11, fig. 11 is a schematic structural diagram of an embodiment of a computer storage medium according to the present application, where the computer storage medium 300 of the present embodiment includes a computer program 31 that can be executed to implement the control method.
The computer storage medium 300 of this embodiment may be a medium that can store program instructions, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, or may also be a server that stores the program instructions, and the server may send the stored program instructions to other devices for operation, or may self-operate the stored program instructions.
In the several embodiments provided in the present application, it should be understood that the disclosed method and apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a module or a unit is only one type of logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, a network device, or the like) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (12)
1. A control method is characterized by being applied to an air conditioning system of a multi-split air conditioner, and comprising the following steps:
acquiring a load prediction parameter of the air conditioning system;
obtaining a predicted total load rate of the air conditioning system based on the load prediction parameters;
acquiring a correction coefficient based on the actual total load rate of the air conditioning system;
correcting the predicted total load rate by using the correction coefficient;
and acquiring a control target value based on the corrected predicted total load rate, and controlling the air conditioning system through the control target value.
2. The control method of claim 1, wherein the step of obtaining a correction factor based on an actual total load factor of the air conditioning system comprises:
calculating the average value of the actual total load rate of the air conditioning system in a first preset time period to obtain a first average value;
calculating the average value of the total predicted load rate of the air conditioning system in the first preset time period to obtain a second average value;
calculating a ratio between the first average value and the second average value to obtain the correction coefficient in a second preset time period after the first preset time period.
3. The control method according to claim 2, wherein the step of calculating the average value of the actual total load rate of the air conditioning system for a first preset time period comprises:
calculating an actual total load value corresponding to a plurality of first time points of the air conditioning system in the first preset time period according to the operation data of the air conditioning system at the first time points;
calculating a ratio between each actual total load value and a rated total load value of the air conditioning system to obtain the actual total load rate corresponding to the first time point;
averaging the actual total load rates corresponding to the plurality of first time points to obtain an average value of the actual total load rates.
4. The control method of claim 1, wherein the step of obtaining a predicted total load rate of the air conditioning system based on the load prediction parameter comprises:
inputting the load prediction parameters into a load prediction model to obtain a total predicted load value of the air conditioning system;
and calculating the ratio of the total predicted load value to the rated total load value of the air conditioning system to obtain the total predicted load rate.
5. The control method of claim 4, wherein the load prediction parameters comprise at least one or a combination of meteorological data, indoor temperature measurements, indoor temperature setpoints, or indoor heat source information.
6. The control method of claim 4, wherein the load prediction model comprises a pre-operation model, and the step of obtaining the predicted total load rate of the air conditioning system based on the load prediction parameters comprises:
and in a pre-operation stage after the air conditioning system is started, inputting the load prediction parameters into the pre-operation model.
7. The control method of claim 6, wherein the load prediction model further comprises a formal model, and the step of obtaining the predicted total load rate of the air conditioning system based on the load prediction parameters further comprises:
and in a formal operation stage after the air conditioning system operates for a preset time, training by using the operation data of the air conditioning system to obtain the formal model, and inputting the load prediction parameters into the formal model.
8. The control method of claim 7, wherein the step of training the formal model using the operation data of the air conditioning system comprises:
updating the formal model based on the operational data over a preset period.
9. The control method according to claim 1, wherein the step of controlling the air conditioning system by the control target value includes:
obtaining a first energy consumption of the air conditioning system based on the control target value currently used by the air conditioning system;
obtaining a second energy consumption of the air conditioning system based on the control target value obtained by current calculation of the air conditioning system;
and obtaining the energy saving rate of the air conditioning system based on the first energy consumption and the second energy consumption.
10. The control method according to claim 9, wherein the step of controlling the air conditioning system by the control target value further includes:
in response to the energy saving rate being greater than or equal to a preset threshold, replacing the currently used control target value with the currently calculated control target value;
and responding to the energy saving rate smaller than the preset threshold value, and reserving the currently used control target value.
11. The air conditioning system is characterized by comprising a processor and a memory; the memory has stored therein a computer program for execution by the processor to implement the control method according to any one of claims 1-10.
12. A computer-readable storage medium characterized in that it stores program instructions executable to implement the control method of any one of claims 1 to 10.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115899995A (en) * | 2022-12-09 | 2023-04-04 | 珠海格力电器股份有限公司 | Multi-split system control method and device, electronic equipment and storage medium |
| CN117366810A (en) * | 2023-10-26 | 2024-01-09 | 中国建筑科学研究院有限公司 | Air conditioning system control method and device |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115899995A (en) * | 2022-12-09 | 2023-04-04 | 珠海格力电器股份有限公司 | Multi-split system control method and device, electronic equipment and storage medium |
| CN117366810A (en) * | 2023-10-26 | 2024-01-09 | 中国建筑科学研究院有限公司 | Air conditioning system control method and device |
| CN117366810B (en) * | 2023-10-26 | 2024-06-07 | 中国建筑科学研究院有限公司 | Air conditioning system control method and device |
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