Control method and control device of air conditioning system and air conditioning system

Abstract

The invention provides a control method and device of an air conditioning system and the air conditioning system. The control method of the air conditioning system comprises the steps that future predicted supply power of the next day is obtained, and future predicted building load of the next day is obtained; and according to the future predicted supply power and the future predicted building load, the optimal control parameters of the air conditioning system in the future day are determined. The invention provides a control method and a control device of an air conditioning system and the air conditioning system, which are used for overcoming the defects in the prior art and realizing the following technical effects that the requirement of a user on comfort is ensured under the condition of limited electric quantity, and the use experience of the user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical appliances, and in particular to a control method, a control device and an air conditioning system. Background Art

[0002] In the related art, as the power load increases, the load peaks, resulting in more and more frequent power cuts. Under limited power, the cooling capacity or heating capacity of the air-conditioning system cannot be guaranteed, resulting in difficulty in ensuring user comfort and poor user experience. Summary of the invention

[0003] The present invention provides a control method, a control device and an air conditioning system for an air conditioning system, so as to solve the defects in the prior art and achieve the following technical effects: the user's comfort requirement is guaranteed under limited power and the user's experience is improved.

[0004] A control method for an air conditioning system according to a first embodiment of the present invention includes:

[0005] Obtaining a future forecast supply power for a future day, and obtaining a future forecast building load for the future day;

[0006] An optimal control parameter of the air conditioning system within the future day is determined according to the future predicted supplied power and the future predicted building load.

[0007] According to one embodiment of the present invention, the step of obtaining the future predicted power supply for the next day specifically includes:

[0008] Acquire the past power supply situation of the past day, the future holiday situation of the future day, and the future weather situation, wherein the past day refers to the day before the future day;

[0009] The future predicted power supply for the future day is obtained according to the past power supply situation, the future holiday situation and the future weather situation forecast.

[0010] According to an embodiment of the present invention, the step of obtaining the future predicted power supply for the next day based on the past power supply situation, the future holiday situation and the future weather situation forecast specifically includes:

[0011] The memory neural network LSTM model is used to predict the future power supply for the next day based on the past power supply conditions, the future holiday conditions and the future weather conditions.

[0012] According to an embodiment of the present invention, the step of obtaining the future predicted building load for the next day specifically includes:

[0013] Obtain the future solar radiation conditions, future ambient temperature conditions and future meteorological conditions for the next day, and obtain the past building load conditions for the past day;

[0014] The future predicted building load for the future day is predicted based on the future solar radiation conditions, the future ambient temperature conditions, the future meteorological conditions and the past building load conditions.

[0015] According to an embodiment of the present invention, the step of predicting the future predicted building load for the next day based on the future solar radiation conditions, the future ambient temperature conditions, the future meteorological conditions and the past building load conditions specifically includes:

[0016] According to the future solar radiation conditions, the future ambient temperature conditions, the future meteorological conditions and the past building load conditions, the memory neural network LSTM model is used to predict the future predicted building load for the next day.

[0017] According to an embodiment of the present invention, the step of determining the optimal control parameters of the air conditioning system in the future day according to the future predicted power supply and the future predicted building load specifically includes:

[0018] Obtaining the power grid control plan for the next day;

[0019] Obtaining the future actual power supply for the future day according to the power grid control plan and the future predicted power supply;

[0020] According to the future actual power supply and the future predicted building load, optimal control parameters of the air-conditioning system in the future day are determined.

[0021] According to an embodiment of the present invention, the step of obtaining the future actual power supply for the next day according to the power grid control plan and the future predicted power supply specifically includes:

[0022] Obtaining the power restriction requirement of the power grid in the power grid control plan to obtain the maximum power load, and obtaining the power restriction requirement of the user in the power grid control plan to obtain the minimum power load;

[0023] Based on the maximum power load and the minimum power load, the future predicted power supply for the future day is corrected to obtain the future actual power supply, wherein the range of the future actual power supply is greater than or equal to the minimum power load and less than or equal to the maximum power load.

[0024] According to an embodiment of the present invention, the optimal control parameters include the optimal start time of the pre-cooling / pre-heating mode of the air-conditioning system in the future day, and the step of determining the optimal control parameters of the air-conditioning system in the future day according to the future actual power supply and the future predicted building load specifically includes:

[0025] Based on the future predicted building load, future holidays in the future day, future solar radiation conditions and future ambient temperature conditions, the optimal start time of the air-conditioning system in the pre-cooling / pre-heating mode in the future day is determined to obtain the optimal control parameters.

[0026] According to an embodiment of the present invention, the step of determining the optimal control parameters of the air-conditioning system in the future day according to the future actual power supply and the future predicted building load specifically includes:

[0027] According to the future actual power supply and the future predicted building load, based on the pre-stored room priority order, the optimal control parameters corresponding to each room of the air-conditioning system are determined.

[0028] A control device for an air conditioning system according to a second aspect of an embodiment of the present invention comprises:

[0029] An acquisition module, used for acquiring the future forecasted supply power for the next day and acquiring the future forecasted building load for the next day;

[0030] A control module is used to determine the optimal control parameters of the air conditioning system in the future day according to the future predicted power supply and the future predicted building load.

[0031] According to an embodiment of the third aspect of the present invention, an air-conditioning system comprises a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the program, the control method of the air-conditioning system as described in the embodiment of the first aspect of the present invention is implemented.

[0032] In order to solve the technical defects existing in the related technology, the present invention provides a control method for an air-conditioning system. The control method obtains the future predicted power supply and the future predicted building load in the next day, and further analyzes the future predicted power supply and the future predicted building load to generate a control model, thereby obtaining the optimal control parameters of the air-conditioning system in the next day based on the control model, and then achieving the best comfort for the air-conditioning system based on the future predicted power supply and the future predicted building load. That is, the method ensures the user comfort requirements under limited power and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 is a flow chart of a control method for an air conditioning system provided by the present invention;

[0035] Figure 2 is a schematic structural diagram of a control device for an air conditioning system provided by the present invention;

[0036] Figure 3 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the embodiment of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0039] The control method, control device and air conditioner of the air-conditioning system proposed in the present invention are described below with reference to the accompanying drawings. Among them, before the embodiment of the present invention is described in detail, the entire application scenario is first described. The control method, control device, electronic device and computer-readable storage medium of the air-conditioning system of the embodiment of the present invention can be applied to the local air-conditioning system, the cloud platform in the Internet field, or other types of cloud platforms in the Internet field, or can also be applied to third-party devices. Among them, the third-party device may include many different types such as mobile phones, tablet computers, notebooks, car computers and other smart terminals.

[0040] The following description only takes the control method applicable to the air-conditioning system as an example. It should be understood that the control method of the embodiment of the present invention can also be applied to the cloud platform and third-party equipment.

[0041] It should also be noted that the air-conditioning control method proposed in the present invention is universal, that is, the method is applicable to the air-conditioning system for cooling or heating in a low-temperature environment or a high-temperature environment.

[0042] like Figure 1 As shown, a control method for an air conditioning system according to a first embodiment of the present invention includes:

[0043] Step S1, obtaining the future predicted supply power for the next day, and obtaining the future predicted building load for the next day;

[0044] Step S2, determining the optimal control parameters of the air conditioning system in the next day according to the future predicted power supply and the future predicted building load.

[0045] According to the control method of the air conditioning system of the embodiment of the present invention, the specific working process is as follows: First, the controller obtains the future predicted power supply for the next day and the future predicted building load for the next day, wherein the next day refers to the second day after the past day. After the controller obtains the future predicted power supply and the future predicted building load, the controller further analyzes the future predicted power supply and the future predicted building load, and the controller determines and obtains the optimal control parameters of the air conditioning system in the next day based on the future predicted power supply and the future predicted building load, wherein the optimal control parameters refer to the parameters for the air conditioning system to achieve the best comfort level based on the future predicted power supply and the future predicted building load.

[0046] Among them, the future predicted power supply refers to the predicted power that the power grid can supply to the air conditioning system in the next day, and the future predicted building load refers to the predicted building load of the building where the air conditioning system is located in the next day.

[0047] In the related art, as the power load increases, the load peaks, resulting in more and more frequent power cuts. Under limited power, the cooling capacity or heating capacity of the air-conditioning system cannot be guaranteed, resulting in difficulty in ensuring user comfort and poor user experience.

[0048] Therefore, in order to solve the technical defects existing in the above-mentioned related technologies, the present invention provides a control method for an air-conditioning system. The control method obtains the future predicted power supply and the future predicted building load in the next day, and further analyzes the future predicted power supply and the future predicted building load to obtain the optimal control parameters of the air-conditioning system in the next day, and then enables the air-conditioning system to achieve optimal comfort based on the future predicted power supply and the future predicted building load. That is, the method ensures the user's comfort requirements under limited power and improves the user experience.

[0049] According to some embodiments of the present invention, the step of obtaining the future predicted power supply for the next day specifically includes:

[0050] Obtain the past power supply situation of the past day, and obtain the future holiday situation and future weather situation of the future day, where the past day refers to the day before the future day;

[0051] Based on past power supply conditions, future holidays and future weather conditions, the future forecast power supply for the next day is predicted.

[0052] In this embodiment, the future predicted power supply for the coming day can be obtained through predictions based on other parameters, wherein before predicting the future predicted power supply, the controller needs to first obtain the past power supply conditions of the past day, and obtain the future holiday conditions and future weather conditions of the coming day, so that the controller can accurately predict the future predicted power supply based on the above-mentioned past power supply conditions, future holiday conditions and future weather conditions.

[0053] Among them, the past power supply situation refers to the power supply of the power grid to the air-conditioning system in the past day; the future holiday situation refers to whether the next day is a holiday and what kind of holiday it is. For example, when the next day is a holiday, the future power supply may be reduced; the future weather conditions refer to the weather and meteorological conditions in the next day.

[0054] It can be understood that the above-mentioned past power supply conditions, future holiday conditions and future weather conditions all have a great influence on the prediction of future power supply. Among them, the past power supply conditions can reflect the usual power conditions of the air-conditioning system in the past. The greater the power supply in the past, generally speaking, the greater the future power supply; the future holiday conditions can determine whether the future day is in the peak power consumption period. When the future day is a holiday, the future power supply will be reduced due to the impact of the peak period; future meteorological conditions also have a great influence on the future prediction of power supply. For example, when there is extreme weather such as heavy rain, heavy snow or typhoon in the future day, the future power supply may be interrupted.

[0055] In a specific embodiment of the present invention, the step of obtaining the future predicted power supply for the next day according to the past power supply situation, the future holiday situation and the future weather situation forecast specifically includes:

[0056] The memory neural network LSTM model is used to predict the future power supply for the next day based on the past power supply conditions, the future holiday conditions and the future weather conditions.

[0057] It needs to be explained that the description of the LSTM model is as follows: The full name of LSTM is Long Short Term Memory, which is a neural network that has the ability to remember long-term and short-term information. It belongs to the same recurrent neural network as RNN, but when the input sequence is too long, the weight matrix of RNN needs to be multiplied cyclically, which will cause the problem of gradient disappearance and gradient explosion. Therefore, RNN cannot solve the long-term dependency problem. LSTM was proposed to solve the long-term dependency problem. Among them, the above-mentioned BPNN model refers to the BP neural network system. The BPNN model is a classic neural network model, which is divided into an input layer, a hidden layer, and an output layer. Among them, the full name of BPNN is Back Propagation Neural Network.

[0058] In this embodiment, in order to achieve accurate prediction of the future predicted power supply for the next day, the method of the present invention also introduces an LSTM model, thereby achieving accurate prediction of the future predicted power supply based on past power supply conditions, future holiday conditions and future weather conditions.

[0059] According to some embodiments of the present invention, the step of obtaining the future predicted building load for the next day specifically includes:

[0060] Obtain the future solar radiation conditions, future ambient temperature conditions and future meteorological conditions for the next day, and obtain the past building loads for the past day;

[0061] Based on future solar radiation conditions, future ambient temperature conditions, future meteorological conditions and past building load conditions, the future predicted building load for the next day is predicted.

[0062] In this embodiment, the future predicted building load for the coming day can be obtained through predictions based on other parameters, wherein before predicting the future predicted building load, the controller needs to first obtain the future solar radiation conditions, future ambient temperature conditions and future meteorological conditions for the coming day, and obtain the past building load conditions for the past day, so that the controller can accurately predict the future predicted building load based on the above-mentioned future solar radiation conditions, future ambient temperature conditions, future meteorological conditions and past building loads.

[0063] Among them, future solar radiation conditions refer to the solar radiation conditions in the external environment in the next day; future ambient temperature conditions refer to the outdoor ambient temperature conditions in the next day. For example, when the future ambient temperature is higher, the future predicted building load will also be greater; future meteorological conditions refer to the weather and meteorological conditions in the next day; past building load conditions refer to the load conditions of the building where the air-conditioning system is located in the past day.

[0064] It can be understood that the above-mentioned future solar radiation conditions, future ambient temperature conditions, future meteorological conditions and past building loads all have a great influence on the prediction of future predicted building loads. Among them, the past building load conditions can reflect the usual building load conditions of the air-conditioning system in the past. The greater the past building load, generally speaking, the greater the predicted building load in the future; the future solar radiation conditions and future ambient temperature conditions can determine the impact of ambient light and ambient temperature on the building load in the future day. The greater the future solar radiation or the higher the future ambient temperature, the greater the predicted building load in the future; the future meteorological conditions also have a great influence on the future predicted building load. For example, when there is extreme weather such as heavy rain, heavy snow or typhoon in the future day, the future predicted building load will increase.

[0065] According to some embodiments of the present invention, the step of predicting the future predicted building load for the next day according to the future solar radiation conditions, the future ambient temperature conditions, the future meteorological conditions and the past building loads specifically includes:

[0066] According to the future solar radiation conditions, future ambient temperature conditions, future meteorological conditions and past building load conditions, the memory neural network LSTM model is used to predict the future predicted building load for the next day.

[0067] It can be understood that in this embodiment, in order to achieve accurate prediction of the future predicted building load for the next day, the method of the present invention also introduces a memory neural network LSTM model, so as to achieve accurate prediction of the future predicted building load based on future solar radiation conditions, future ambient temperature conditions, future meteorological conditions and past building load conditions.

[0068] According to some embodiments of the present invention, the step of determining the optimal control parameters of the air conditioning system in the future day according to the future predicted power supply and the future predicted building load specifically includes:

[0069] Obtaining the power grid control plan for the next day;

[0070] According to the power grid control plan and the future predicted power supply, obtaining the future actual power supply for the future day;

[0071] According to the future actual power supply and the future predicted building load, optimal control parameters of the air-conditioning system in the future day are determined.

[0072] Furthermore, the step of obtaining the future actual power supply for the next day according to the power grid control plan and the future predicted power supply specifically includes:

[0073] Obtaining the power restriction requirement of the power grid in the power grid control plan to obtain the maximum power load, and obtaining the power restriction requirement of the user in the power grid control plan to obtain the minimum power load;

[0074] Based on the maximum power load and the minimum power load, the future predicted power supply for the future day is corrected to obtain the future actual power supply, wherein the range of the future actual power supply is greater than or equal to the minimum power load and less than or equal to the maximum power load.

[0075] It can be understood that in the above embodiment, since the predicted future power supply is only a predicted value, not the actual power supply in the future day, and the actual power supply in the future day will also be affected by the power grid regulation plan, therefore, in order to obtain a more accurate power supply situation in the future day, the controller will obtain the power grid regulation plan, and use the power grid regulation plan to correct the future predicted power supply, so as to finally obtain the future actual power supply. It can be understood that the future actual power supply takes into account the impact of the power grid regulation plan, and is closer to the actual power supply in the future day.

[0076] Specifically, the power grid control plan includes power grid power restriction requirements, user power restriction requirements and automatic power restriction requirements. The specific principles are: first, power grid power restriction requirements take precedence (that is, strict control of power load), for example, lower than the maximum power load; second, user power restriction requirements take precedence (that is, limiting the power supply to meet the minimum comfort requirements of personnel), for example, higher than the minimum power load; third, automatic power restriction, and automatic identification of power restriction requirements for different usage scenarios (for example, power restriction in non-main functional areas).

[0077] According to some embodiments of the present invention, the optimal control parameters include the optimal start time of the air-conditioning system in the pre-cooling / pre-heating mode in the future day, and the step of determining the optimal control parameters of the air-conditioning system in the future day according to the future actual power supply and the future predicted building load specifically includes:

[0078] Based on the future predicted building load, future holidays in the future day, future solar radiation conditions and future ambient temperature conditions, the optimal start time of the air-conditioning system in the pre-cooling / pre-heating mode in the future day is determined to obtain the optimal control parameters.

[0079] For example, the controller predicts the optimal start time of pre-cooling or pre-heating of the air-conditioning system based on the future predicted building load, future holidays in the future day, future solar radiation and future ambient temperature using the BPNN model.

[0080] According to some embodiments of the present invention, the step of determining the optimal control parameters of the air conditioning system in the future day according to the future actual power supply and the future predicted building load specifically includes:

[0081] According to the future actual power supply and the future predicted building load, based on the pre-stored room priority order, the optimal control parameters corresponding to each room of the air-conditioning system are determined.

[0082] Among them, the room priority order can be set using a user-prestored method, or it can be divided based on actual conditions. For example, first obtain the room information of each room where the air-conditioning system is located, then divide the priority of each room according to the room information, and further determine the room priority order based on the priority division level of each room.

[0083] Specifically, the air-conditioning system is equipped with indoor units in the master bedroom, living room and kitchen, and the room priority order can be: the master bedroom has the highest priority, the living room has the second priority, and the kitchen has the lowest priority. At this time, under the actual power supply in the future, the optimal control parameters are: the indoor unit in the master bedroom selects the largest air volume and refrigerant distribution amount, the indoor unit in the living room selects the appropriate air volume and refrigerant distribution amount, and the indoor unit in the kitchen selects the smallest air volume and refrigerant distribution amount.

[0084] Furthermore, in combination with the future predicted building load, the future holiday conditions of the future day, the future solar radiation conditions and the future ambient temperature conditions, the optimal start time of the system pre-cooling / pre-heating mode in different rooms can also be obtained based on the priority order. For example, the room priority order can be: the master bedroom has the highest priority level, the living room has the second highest priority level and the kitchen has the lowest priority level, then the optimal start time is: the indoor unit in the master bedroom starts the pre-cooling mode or pre-heating mode first, the indoor unit in the living room starts the pre-cooling mode or pre-heating mode after the indoor unit in the master bedroom, and the indoor unit in the kitchen starts the pre-cooling mode or pre-heating mode last.

[0085] According to some embodiments of the present invention, in addition to the optimal start time of the system precooling / preheating mode, the optimal control parameters may also include parameters such as the target suction and exhaust pressure, superheat and equipment frequency of the air-conditioning system at a specific moment, and also include flow adjustment parameters of the indoor refrigerant in different areas.

[0086] A specific embodiment of the present invention is described below with reference to the accompanying drawings.

[0087] The control method of the present invention proposes a personalized model learning and control technology for grid demand response through active energy consumption management-response grid joint control. The method can develop a future multi-objective optimization control algorithm by predicting the power supply and building load of the next day in advance, and combine the optimization prediction of air conditioning system precooling and comfort model to predict the optimal control parameters of the equipment in the future.

[0088] The specific process of this method is as follows:

[0089] (1) Based on past power supply conditions, future holidays, and future weather conditions, the LSTM model is combined with the power grid dispatch plan to predict the power supply for the next day.

[0090] (2) Based on future solar radiation, future ambient temperature, future meteorological conditions, and past building load conditions, the LSTM model is used to predict the building load for the next day.

[0091] (3) Based on the power supply and building load for the next day, a control model is established to optimize the parameters of the air-conditioning system according to priorities and scenarios.

[0092] The control device of the air-conditioning system provided by the present invention is described below. The control device of the air-conditioning system described below and the control method of the air-conditioning system described above can be referred to each other.

[0093] like Figure 2 As shown, a control device for an air conditioning system according to an embodiment of the second aspect of the present invention comprises:

[0094] An acquisition module 110 is used to acquire the future forecasted supply power for the next day and acquire the future forecasted building load for the next day;

[0095] The control module 120 is used to determine the optimal control parameters of the air conditioning system in the next day according to the future predicted power supply and the future predicted building load.

[0096] An air conditioning system according to an embodiment of the third aspect of the present invention comprises a control device for the air conditioning system according to an embodiment of the second aspect of the present invention.

[0097] According to the control device and air-conditioning system of the embodiment of the present invention, by acquiring the future predicted power supply and the future predicted building load in the next day, and further analyzing the future predicted power supply and the future predicted building load to generate a control model, the optimal control parameters of the air-conditioning system in the next day are obtained based on the control model, and then the air-conditioning system achieves the best comfort on the basis of the future predicted power supply and the future predicted building load, that is, the user comfort requirements are guaranteed under limited power, thereby improving the user experience.

[0098] Figure 3 An example of a physical structure diagram of an electronic device is shown in FIG. Figure 3 As shown, the electronic device may include: a processor 810, a communication interface 820, a memory 830 and a communication bus 840, wherein the processor 810, the communication interface 820 and the memory 830 communicate with each other through the communication bus 840. The processor 810 may call the logic instructions in the memory 830 to execute the control method of the air conditioning system, including: obtaining the future predicted power supply for the next day, and obtaining the future predicted building load for the next day; determining the optimal control parameters of the air conditioning system in the next day according to the future predicted power supply and the future predicted building load.

[0099] In addition, the logic instructions in the above-mentioned memory 830 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0100] On the other hand, the present invention also provides a computer program product, which includes a computer program. The computer program can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the control methods of the air-conditioning system provided by the above methods, including: obtaining the future predicted power supply for the next day, and obtaining the future predicted building load for the next day; determining the optimal control parameters of the air-conditioning system in the next day based on the future predicted power supply and the future predicted building load.

[0101] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to execute the control method of the air-conditioning system provided by the above-mentioned methods, including: obtaining the future predicted power supply for the next day, and obtaining the future predicted building load for the next day; determining the optimal control parameters of the air-conditioning system in the next day based on the future predicted power supply and the future predicted building load.

[0102] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0103] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control method for an air conditioning system, characterized in that: include: Obtaining a future forecast supply power for a future day, and obtaining a future forecast building load for the future day; An optimal control parameter of the air conditioning system within the future day is determined according to the future predicted supplied power and the future predicted building load.

2. The control method of the air conditioning system according to claim 1, characterized in that: The step of obtaining the future forecasted power supply for the next day specifically includes: Acquire the past power supply situation of the past day, the future holiday situation of the future day, and the future weather situation, wherein the past day refers to the day before the future day; The future predicted power supply for the future day is obtained according to the past power supply situation, the future holiday situation and the future weather situation forecast.

3. The control method of the air conditioning system according to claim 2, characterized in that: The step of obtaining the future predicted power supply for the next day based on the past power supply situation, the future holiday situation and the future weather situation forecast specifically includes: The memory neural network LSTM model is used to predict the future power supply for the next day based on the past power supply conditions, the future holiday conditions and the future weather conditions.

4. The control method of the air conditioning system according to claim 1, characterized in that: The step of obtaining the future predicted building load for the next day specifically includes: Obtain the future solar radiation conditions, future ambient temperature conditions and future meteorological conditions for the next day, and obtain the past building load conditions for the past day; The future predicted building load for the future day is predicted based on the future solar radiation conditions, the future ambient temperature conditions, the future meteorological conditions and the past building load conditions.

5. The control method of the air conditioning system according to claim 4, characterized in that: The step of predicting the future predicted building load for the next day according to the future solar radiation conditions, the future ambient temperature conditions, the future meteorological conditions and the past building load conditions specifically includes: According to the future solar radiation conditions, the future ambient temperature conditions, the future meteorological conditions and the past building load conditions, the memory neural network LSTM model is used to predict the future predicted building load for the next day.

6. The control method of the air conditioning system according to any one of claims 1 to 5, characterized in that: The step of determining the optimal control parameters of the air conditioning system in the future day according to the future predicted power supply and the future predicted building load specifically includes: Obtaining the power grid control plan for the next day; Obtaining the future actual power supply for the future day according to the power grid control plan and the future predicted power supply; According to the future actual power supply and the future predicted building load, optimal control parameters of the air-conditioning system in the future day are determined.

7. The control method of the air conditioning system according to claim 6, characterized in that: The step of obtaining the future actual power supply for the next day according to the power grid control plan and the future predicted power supply specifically includes: Obtaining the power restriction requirement of the power grid in the power grid control plan to obtain the maximum power load, and obtaining the power restriction requirement of the user in the power grid control plan to obtain the minimum power load; Based on the maximum power load and the minimum power load, the future predicted power supply for the future day is corrected to obtain the future actual power supply, wherein the range of the future actual power supply is greater than or equal to the minimum power load and less than or equal to the maximum power load.

8. The control method of the air conditioning system according to claim 7, characterized in that: The optimal control parameters include the optimal start time of the air conditioning system in the pre-cooling / pre-heating mode in the future day, and the step of determining the optimal control parameters of the air conditioning system in the future day according to the future actual power supply and the future predicted building load specifically includes: Based on the future predicted building load, future holidays in the future day, future solar radiation conditions and future ambient temperature conditions, the optimal start time of the air-conditioning system in the pre-cooling / pre-heating mode in the future day is determined to obtain the optimal control parameters.

9. The control method of the air conditioning system according to claim 7, characterized in that: The step of determining the optimal control parameters of the air conditioning system in the future day according to the future actual power supply and the future predicted building load specifically includes: According to the future actual power supply and the future predicted building load, based on the pre-stored room priority order, the optimal control parameters corresponding to each room of the air-conditioning system are determined.

10. A control device for an air conditioning system, characterized in that: include: An acquisition module, used for acquiring the future forecasted supply power for the next day and acquiring the future forecasted building load for the next day; The control module determines the optimal control parameters of the air conditioning system in the future day according to the future predicted power supply and the future predicted building load.

11. An air conditioning system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the control method of the air conditioning system according to any one of claims 1 to 9 is implemented.

Images