A comfort optimization method, device, equipment and storage medium of an air conditioning system

Abstract

The application discloses a comfort optimization method, device and equipment of an air conditioning system and a storage medium, and the method comprises the following steps: determining a plurality of building surfaces of a target building; for each building surface, acquiring the accumulated solar radiation, the accumulated outdoor dry-bulb temperature and the accumulated refrigeration capacity of the air conditioning system corresponding to the building surface in real time; inputting the accumulated solar radiation, the accumulated outdoor dry-bulb temperature and the accumulated refrigeration capacity of the building surface into a temperature prediction model to obtain a temperature prediction value output by the temperature prediction model; determining the highest indoor temperature value of the target building from the temperature prediction values corresponding to the plurality of building surfaces; and performing optimization control on the chilled water supply temperature and / or the supply air temperature of the air conditioning system according to the highest indoor temperature value and a comfort expectation value. Compared with the prior art, the application can predict the highest indoor temperature and its position in real time, and perform comfort optimization control on the air conditioning system according to the highest indoor temperature, so that the indoor comfort can be ensured in real time.

Description

Technical Field

[0001] This application belongs to the field of air conditioning technology, and in particular relates to a method, apparatus, equipment and storage medium for optimizing the comfort of an air conditioning system. Background Technology

[0002] With economic and social development and the increasing demand for quality of life, the requirements for indoor environments are also constantly rising. Different environmental factors have varying impacts on comfort, with temperature being one of the main factors affecting comfort. Adjusting indoor temperature relies on adjusting and controlling the setpoints of relevant parameters in the air conditioning system, and air conditioning systems, mainly composed of chillers and fans, account for more than 60% of building energy consumption. Therefore, the optimized control of air conditioning systems must balance the dual goals of energy conservation and comfort.

[0003] Current optimization control methods for air conditioning systems mostly focus on energy saving and rarely consider the comfort of indoor occupants. This is partly because it is difficult to collect data related to the highest indoor temperature, and partly because existing methods for predicting the highest indoor temperature mostly rely on CFD (Computational Fluid Dynamics) simulations, which have problems such as complex modeling, long calculation time, and difficulty in real-time control. Summary of the Invention

[0004] In view of the above, embodiments of this application provide a method, apparatus, electronic device, and storage medium for optimizing the comfort of an air conditioning system to solve at least one of the above-mentioned technical problems.

[0005] The first aspect of this application provides a method for optimizing the comfort of an air conditioning system, including:

[0006] Identify multiple building facades of the target building, which is equipped with an air conditioning system;

[0007] For each building facade, the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system corresponding to the building facade are acquired in real time.

[0008] The cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity corresponding to the building facade are input into the temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; each building facade corresponds to a temperature prediction value, and the highest indoor temperature value of the target building is determined from the temperature prediction values ​​corresponding to all building facades; wherein, the temperature prediction model is established by training with the historical cumulative solar radiation, historical cumulative outdoor dry-bulb temperature, historical cumulative cooling capacity, and historical indoor temperature values ​​corresponding to the building facade as a sample set;

[0009] The chilled water supply temperature and / or air supply temperature of the air conditioning system are optimized and controlled based on the highest indoor temperature value and the expected comfort value.

[0010] The comfort optimization method for an air conditioning system according to the first aspect of this application involves determining multiple building surfaces of a target building; for each building surface, acquiring in real time the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system corresponding to that building surface; inputting the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity corresponding to that building surface into a temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; determining the maximum indoor temperature value of the target building from the multiple temperature prediction values ​​corresponding to the building surfaces; and optimizing the control of the chilled water supply temperature and / or air supply temperature of the air conditioning system based on the maximum indoor temperature value and the expected comfort value. Compared with the prior art, this application utilizes historical meteorological data and historical operating data of the air conditioning system to establish a prediction model for the maximum indoor temperature of the target building, and predicts the maximum indoor temperature and its location in real time based on the above model, improving the accuracy and speed of the prediction of the maximum indoor temperature, and optimizing the control of the air conditioning system for comfort based on the maximum indoor temperature, thus ensuring indoor comfort in real time.

[0011] The comfort optimization device for an air conditioning system according to a second aspect embodiment of this application includes:

[0012] The determination module is used to determine multiple building facades of a target building that is equipped with an air conditioning system;

[0013] The acquisition module is used to acquire, in real time, the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system for each building surface.

[0014] The prediction module is used to input the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity corresponding to the building surface into the temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; each building surface corresponds to a temperature prediction value, and the highest indoor temperature value of the target building is determined from the temperature prediction values ​​corresponding to all building surfaces; wherein, the temperature prediction model is established by training with the historical cumulative solar radiation, historical cumulative outdoor dry-bulb temperature, historical cumulative cooling capacity, and historical indoor temperature values ​​corresponding to the building surface as a sample set;

[0015] The optimization module is used to optimize the control of the chilled water supply temperature and / or air supply temperature of the air conditioning system based on the indoor maximum temperature value and the expected comfort value.

[0016] The comfort optimization device for an air conditioning system according to the second aspect of this application determines multiple building surfaces of a target building; for each building surface, it acquires the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system in real time; it inputs the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the building surface into a temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; it determines the maximum indoor temperature value of the target building from the multiple temperature prediction values ​​corresponding to the building surfaces; and it optimizes and controls the chilled water supply temperature and / or air supply temperature of the air conditioning system according to the maximum indoor temperature value and the expected comfort value. Compared with the prior art, this application uses historical meteorological data and historical operating data of the air conditioning system to establish a prediction model for the maximum indoor temperature of the target building, and predicts the maximum indoor temperature and its location in real time based on the above model, improving the accuracy and speed of the prediction of the maximum indoor temperature, and optimizing and controlling the air conditioning system for comfort based on the maximum indoor temperature, thus ensuring indoor comfort in real time.

[0017] An electronic device according to a third aspect of this application includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the comfort optimization method of the air conditioning system according to the first aspect of this application.

[0018] A computer-readable storage medium according to a fourth aspect of this application stores computer-readable instructions that can be executed by a processor to implement the comfort optimization method of the air conditioning system according to a first aspect embodiment. Attached Figure Description

[0019] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0020] Appendix Figure 1 A flowchart of a comfort optimization method for an air conditioning system provided in this application is shown;

[0021] Appendix Figure 2 A schematic diagram of the building's four facades is shown;

[0022] Appendix Figure 3 A schematic diagram illustrating the process of training and predicting temperature prediction models is shown.

[0023] Appendix Figure 4 A schematic diagram of the chilled water supply temperature optimization process is shown;

[0024] Appendix Figure 5 The diagram shows the chilled water supply temperature settings before and after optimization, as well as the temperature changes in each room.

[0025] Appendix Figure 6 A schematic diagram of the Feng Shui linkage optimization process is shown;

[0026] Appendix Figure 7 A schematic diagram of a comfort optimization device for an air conditioning system according to an embodiment of this application is shown.

[0027] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0029] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0030] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0031] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0032] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0033] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, specific embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0034] Example 1

[0035] Figure 1 This is a flowchart of a comfort optimization method for an air conditioning system provided in an embodiment of this application, such as... Figure 1 As shown, the comfort optimization method for this air conditioning system includes:

[0036] Step S101: Determine multiple building facades of the target building, which is equipped with an air conditioning system.

[0037] The target building can be any shape and has multiple building faces, which refer to walls perpendicular to the ground. For example, the most common type is a building with four building faces, but it can also be a building with six building faces, a building with eight building faces, or a building with circular walls. This application does not limit this.

[0038] Taking a building with four facades as an example, the four facades are the north side, east side, south side, and west side. Figure 2 As shown, the four building facades divide the building's interior space into four parts.

[0039] The target building is equipped with an air conditioning system that can regulate the indoor temperature of the target building.

[0040] Step S102: For each building surface, acquire the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system corresponding to the building surface in real time.

[0041] For each building facade, the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system can all affect the indoor temperature near that building facade.

[0042] For each building facade, solar radiation can increase the indoor temperature near that facade. The calculation process for the cumulative solar radiation corresponding to that facade is as follows:

[0043] 1. Obtain solar radiation data for the target building's location over a specific period based on historical meteorological data. This solar radiation data includes direct solar radiation, solar altitude angle, and solar azimuth angle. This historical meteorological data can be obtained through relevant meteorological websites.

[0044] 2. The instantaneous solar radiation of the building surface is obtained based on the solar radiation data and the orientation of the building surface. The instantaneous solar radiation refers to the horizontal incident solar radiation.

[0045] Specifically, instantaneous solar radiation = direct solar radiation of the building * sin(solar altitude angle) * whether the building surface radiates. Whether the building surface radiates depends on the building's orientation and the sun's azimuth angle; the coefficient for radiation is 1, and the coefficient for no radiation is 0.

[0046] 3. The sum of the cumulative solar radiation at the previous moment and the instantaneous solar radiation at the current moment is taken as the cumulative solar radiation at the current moment.

[0047] Specifically, the cumulative solar radiation at the current moment = the cumulative solar radiation at the previous moment of the same day + the instantaneous solar radiation at the current moment.

[0048] For each building facade, the calculation process for the corresponding cumulative outdoor dry-bulb temperature is as follows:

[0049] The cumulative outdoor dry-bulb temperature at the previous moment of the day is the sum of the outdoor dry-bulb temperature at the current moment.

[0050] Specifically, the current cumulative outdoor dry-bulb temperature = the previous day's cumulative outdoor dry-bulb temperature + the current outdoor dry-bulb temperature.

[0051] For each building surface, the calculation process for the cumulative cooling capacity of the air conditioning system corresponding to that building surface is as follows:

[0052] Specifically, the cumulative cooling capacity at the current moment = the cumulative cooling capacity at the previous moment of the day + the instantaneous cooling capacity at the current moment.

[0053] Step S103: Input the cumulative solar radiation, cumulative outdoor dry-bulb temperature and cumulative cooling capacity corresponding to the building surface into the temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; each building surface corresponds to a temperature prediction value, and the highest indoor temperature value of the target building is determined from the temperature prediction values ​​corresponding to all building surfaces.

[0054] The temperature prediction model is established by training on a sample set consisting of historical cumulative solar radiation, historical cumulative outdoor dry-bulb temperature, historical cumulative cooling capacity, and their corresponding historical indoor temperature values ​​corresponding to the building facade. Figure 3The diagram illustrates the training and prediction process of the temperature prediction model. In practical applications, the XGBoost regression algorithm can be used to establish a temperature prediction model for each building area location. The inputs are the cumulative solar radiation, cumulative cooling capacity, and cumulative outdoor dry-bulb temperature corresponding to the building surface, and the output is the predicted temperature value corresponding to the building surface.

[0055] like Figure 2 As shown, existing technologies measure indoor temperature by installing temperature sensors in the interior spaces corresponding to the four building facades. This application, however, predicts the indoor temperature corresponding to the four building facades based on a pre-trained temperature prediction model. Therefore, this application can accurately predict the indoor temperature even when no temperature sensors are installed or when the sensors malfunction. Then, the highest indoor temperature value and its location of the target building are determined from the predicted temperature values ​​corresponding to the four building facades.

[0056] Step S104: Optimize the control of the chilled water supply temperature and / or air supply temperature of the air conditioning system based on the indoor maximum temperature value and the expected comfort value.

[0057] In one embodiment, step S104 involves optimizing the chilled water supply temperature of the air conditioning system based on the highest indoor temperature value and the desired comfort level, specifically including the following steps:

[0058] Obtain the initial set value of the chilled water supply temperature of the air conditioning system;

[0059] Based on the initial set value of the chilled water supply temperature, the highest indoor temperature value, and the expected comfort value, the optimized set value of the chilled water supply temperature is obtained through the first formula;

[0060] The air conditioning system is optimized and controlled based on the optimized setpoint for the chilled water supply temperature.

[0061] The first formula is as follows:

[0062] y1=s1+a*err1 2 +b*err1+c;

[0063] Where y1 represents the optimized setting value of the chilled water supply temperature, and s1 represents the initial setting value of the chilled water supply temperature;

[0064] err1 = ss - zg, where ss represents the expected comfort value and zg represents the highest indoor temperature value; a, b, and c are fitting coefficients.

[0065] The optimized setpoint for the adjusted chilled water supply temperature is written into the air conditioning system controller.

[0066] It should be noted that the above formula is only one example of optimizing the chilled water supply temperature setpoint based on the highest temperature prediction value. The methods for optimizing the chilled water supply temperature setpoint include, but are not limited to, the methods described above.

[0067] For ease of understanding, this application provides the following: Figure 4 The diagram illustrates the process of optimizing the chilled water supply temperature. Figure 4 As shown, temperature prediction models are built and predicted in real time in the cloud, while optimization control is performed at the edge. Figure 4 The cloud-edge collaborative optimization method shown deploys a temperature prediction model in the cloud and predicts the highest indoor temperature in real time, while optimizing and controlling the air conditioning system at the edge. It has the advantages of high precision, low power consumption, fast response, and low latency.

[0068] Figure 5 This diagram shows the changes in chilled water supply temperature settings and room temperatures before and after optimization using the method proposed in this application. It can be seen that before using the method, the chilled water supply temperature was maintained at 8℃. At this temperature, most rooms were below 26℃, but some rooms exceeded 27℃, and even approached 28℃ at certain times around noon. This indicates that the room temperatures did not meet comfort requirements. After applying the method during weather conditions similar to July 1, 2022 (July 7, 2022), the temperatures in all rooms were maintained within a comfortable range. Therefore, the method proposed in this application can adjust the water supply temperature settings in real time based on changes in the highest indoor temperature to ensure indoor comfort.

[0069] In one embodiment, step S104 may further optimize the control of the chilled water supply temperature and air supply temperature of the air conditioning system based on the highest indoor temperature value and the desired comfort value, specifically including:

[0070] Obtain the initial set value of the air supply temperature of the air conditioning system;

[0071] Based on the initial setpoint of the supply air temperature, the initial setpoint of the chilled water temperature, and the optimized setpoint of the chilled water temperature, the optimized setpoint of the supply air temperature is obtained through the following second formula.

[0072] The air conditioning system is optimized and controlled based on the optimized setpoints for chilled water supply temperature and air supply temperature.

[0073] The second formula is as follows:

[0074] y2=s2+d*err2 2 +e*err2+f;

[0075] Where y2 represents the optimized setting value of the supply air temperature, and s2 represents the initial setting value of the supply air temperature;

[0076] err2 = y1 - s1; d, e, and f are fitting coefficients.

[0077] It should be noted that the above formula is only one example of optimizing the chilled water supply temperature and air supply temperature based on the highest temperature prediction value. The air-water linkage optimization method includes, but is not limited to, the above method.

[0078] For ease of understanding, this application provides the following: Figure 6 The diagram illustrates the feng shui linkage optimization process. (See attached diagram.) Figure 6 As shown, temperature prediction models are built and predicted in real time in the cloud, while optimization control is performed at the edge. Figure 6 The cloud-edge collaborative optimization method shown deploys a temperature prediction model in the cloud and predicts the highest indoor temperature in real time, while optimizing and controlling the air conditioning system at the edge. It has the advantages of low computational complexity, small memory footprint, fast modeling speed, and flexible scalability. It can quickly and accurately predict the highest indoor temperature and ensure that the indoor temperature is always within a comfortable range, achieving the dual goals of comfort and energy saving.

[0079] As can be seen, this application uses historical meteorological data and historical operating data of the air conditioning system to establish a prediction model for the indoor maximum temperature, and predicts the indoor maximum temperature and its location in real time based on the above model. Then, it adjusts and optimizes the parameter settings of the air conditioning system in real time according to whether the indoor maximum temperature exceeds the comfort range.

[0080] The comfort optimization method for an air conditioning system according to this application involves determining multiple building surfaces of a target building; for each building surface, acquiring the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system in real time; inputting the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the building surface into a temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; determining the maximum indoor temperature value of the target building from the multiple temperature prediction values ​​corresponding to the building surfaces; and optimizing the control of the chilled water supply temperature and / or air supply temperature of the air conditioning system based on the maximum indoor temperature value and the expected comfort value. Compared with the prior art, this application utilizes historical meteorological data and historical operating data of the air conditioning system to establish a prediction model for the maximum indoor temperature of the target building, and predicts the maximum indoor temperature and its location in real time based on the above model, improving the accuracy and speed of the maximum indoor temperature prediction, and optimizing the control of the air conditioning system for comfort based on the maximum indoor temperature, thus ensuring indoor comfort in real time.

[0081] Example 2

[0082] This application provides a comfort optimization device for an air conditioning system, which corresponds to the comfort optimization method for an air conditioning system in Embodiment 1. For relevant details, please refer to the description in Embodiment 1. The method embodiments described below are merely illustrative.

[0083] Figure 7 This is a schematic diagram of a comfort optimization device for an air conditioning system provided in an embodiment of this application, as shown below. Figure 7 As shown, the device 10 includes:

[0084] Module 101 is used to determine multiple building faces of a target building, which is equipped with an air conditioning system;

[0085] The acquisition module 102 is used to acquire, in real time, the cumulative solar radiation, cumulative outdoor dry-bulb temperature and cumulative cooling capacity of the air conditioning system for each building surface.

[0086] The prediction module 103 is used to input the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity corresponding to the building surface into the temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; each building surface corresponds to a temperature prediction value, and the highest indoor temperature value of the target building is determined from the temperature prediction values ​​corresponding to all building surfaces; wherein, the temperature prediction model is established by training with the historical cumulative solar radiation, historical cumulative outdoor dry-bulb temperature, historical cumulative cooling capacity, and historical indoor temperature values ​​corresponding to the building surface as a sample set;

[0087] The optimization module 104 is used to optimize the control of the chilled water supply temperature and / or air supply temperature of the air conditioning system based on the indoor maximum temperature value and the comfort expectation value.

[0088] In some embodiments of this application, the optimization module 104 is specifically used for:

[0089] Obtain the initial set value of the chilled water supply temperature of the air conditioning system;

[0090] Based on the initial set value of the chilled water supply temperature, the highest indoor temperature value, and the expected comfort value, the optimized set value of the chilled water supply temperature is obtained through the following first formula;

[0091] The air conditioning system is optimized and controlled based on the optimized setpoint for the chilled water supply temperature.

[0092] The first formula is as follows:

[0093] y1=s1+a*err1 2 +b*err1+c;

[0094] Where y1 represents the optimized setting value of the chilled water supply temperature, and s1 represents the initial setting value of the chilled water supply temperature;

[0095] err1 = ss - zg, where ss represents the expected comfort value and zg represents the highest indoor temperature value; a, b, and c are fitting coefficients.

[0096] In some embodiments of this application, the optimization module 104 is specifically used for:

[0097] Obtain the initial set value of the air supply temperature of the air conditioning system;

[0098] Based on the initial setpoint of the supply air temperature, the initial setpoint of the chilled water temperature, and the optimized setpoint of the chilled water temperature, the optimized setpoint of the supply air temperature is obtained through the following second formula.

[0099] The air conditioning system is optimized and controlled based on the optimized setpoints for chilled water supply temperature and air supply temperature.

[0100] The second formula is as follows:

[0101] y2=s2+d*err2 2 +e*err2+f;

[0102] Where y2 represents the optimized setting value of the supply air temperature, and s2 represents the initial setting value of the supply air temperature;

[0103] err2 = y1 - s1; d, e, and f are fitting coefficients.

[0104] In some embodiments of this application, the calculation process for the cumulative solar radiation corresponding to the building surface is as follows:

[0105] Solar radiation data for the area where the target building is located over a period of time is obtained based on historical meteorological data. This solar radiation data includes direct solar radiation, solar altitude angle, and solar azimuth angle.

[0106] The instantaneous solar radiation of the building surface is obtained based on the solar radiation data and the orientation of the building surface. This instantaneous solar radiation refers to the horizontal incident solar radiation.

[0107] The sum of the cumulative solar radiation at the previous moment and the instantaneous solar radiation at the current moment is taken as the cumulative solar radiation at the current moment.

[0108] In some embodiments of this application, the calculation process for the cumulative outdoor dry-bulb temperature corresponding to the building surface is as follows:

[0109] The cumulative outdoor dry-bulb temperature at the previous moment of the day is the sum of the outdoor dry-bulb temperature at the current moment.

[0110] In some embodiments of this application, the calculation process for the cumulative cooling capacity of the air conditioning system corresponding to the building surface is as follows:

[0111] The sum of the cumulative cooling capacity at the previous moment and the instantaneous cooling capacity at the current moment is taken as the cumulative cooling capacity at the current moment.

[0112] The comfort optimization device for the air conditioning system in this embodiment determines multiple building surfaces of the target building; for each building surface, it acquires the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system in real time; it inputs the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the building surface into a temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; it determines the maximum indoor temperature value of the target building from the multiple temperature prediction values ​​corresponding to the building surfaces; and it optimizes and controls the chilled water supply temperature and / or air supply temperature of the air conditioning system based on the maximum indoor temperature value and the expected comfort value. Compared with the prior art, this application uses historical meteorological data and historical operating data of the air conditioning system to establish a prediction model for the maximum indoor temperature of the target building, and predicts the maximum indoor temperature and its location in real time based on the above model, improving the accuracy and speed of the maximum indoor temperature prediction, and optimizing and controlling the air conditioning system for comfort based on the maximum indoor temperature, which can ensure indoor comfort in real time.

[0113] Example 3

[0114] This application also provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, it executes a method for optimizing the comfort of an air conditioning system according to any embodiment of the first embodiment. This electronic device can utilize a cloud server, and this application does not limit its use.

[0115] Specifically, the electronic device may include: a processor, a memory, a bus, and a communication interface, wherein the processor, the communication interface, and the memory are connected via the bus; the memory stores a computer program that can run on the processor, and when the processor runs the computer program, it executes the comfort optimization method of the air conditioning system provided in any of the foregoing embodiments of this application.

[0116] The memory may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk drive. Communication between this system network element and at least one other network element is achieved through at least one communication interface (wired or wireless), which can use the Internet, wide area network, local area network, metropolitan area network, etc.

[0117] The bus can be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. The memory is used to store the program, and the processor executes the program after receiving an execution instruction. The comfort optimization method for the air conditioning system disclosed in any of the foregoing embodiments of this application can be applied to the processor, or implemented by the processor.

[0118] The processor may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed through integrated logic circuits in the processor's hardware or through software instructions. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application-Specific Integrated Circuit (ASIC), an Off-the-shelf Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory; the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0119] The electronic device provided in this application embodiment and the comfort optimization method of the air conditioning system provided in this application embodiment are based on the same inventive concept and have the same beneficial effects as the methods they adopt, operate or implement.

[0120] Example 4

[0121] This application also provides a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to implement the comfort optimization method of the air conditioning system in any of the embodiments of Embodiment 1.

[0122] Examples of the computer-readable storage medium may also include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other optical or magnetic storage media, which will not be described in detail here.

[0123] The computer-readable storage medium provided in the above embodiments of this application and the comfort optimization method of the air conditioning system provided in the embodiments of this application are based on the same inventive concept and have the same beneficial effects as the methods adopted, run or implemented by the application stored therein.

[0124] It should be noted that:

[0125] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0126] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of this application, various features of this application are sometimes grouped together into a single embodiment, figure, or description thereof. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, inventive aspects lie in fewer than all features of a single foregoing disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of this application.

[0127] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and further, they can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0128] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of this application and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.

[0129] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some or all of the components in the virtual machine creation apparatus according to embodiments of this application. This application can also be implemented as a device or apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0130] It should be noted that the above embodiments are illustrative of this application and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0131] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made under the concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A method for optimizing the comfort of an air conditioning system, characterized in that, include: Identify multiple building facades of the target building, which is equipped with an air conditioning system; For each building facade, the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system corresponding to the building facade are acquired in real time. The cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity corresponding to the building facade are input into the temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; each building facade corresponds to a temperature prediction value, and the highest indoor temperature value of the target building is determined from the temperature prediction values ​​corresponding to all building facades; wherein, the temperature prediction model is established by training with the historical cumulative solar radiation, historical cumulative outdoor dry-bulb temperature, historical cumulative cooling capacity, and historical indoor temperature values ​​corresponding to the building facade as a sample set; The chilled water supply temperature and / or air supply temperature of the air conditioning system are optimized and controlled based on the highest indoor temperature value and the expected comfort value. The optimization control of the chilled water supply temperature of the air conditioning system based on the highest indoor temperature and the expected comfort level includes: Obtain the initial set value of the chilled water supply temperature of the air conditioning system; Based on the initial set value of the chilled water supply temperature, the highest indoor temperature value, and the expected comfort value, the optimized set value of the chilled water supply temperature is obtained through the following first formula; The air conditioning system is optimized and controlled based on the optimized setpoint for the chilled water supply temperature. The first formula is as follows: y1=s1+ ; Where y1 represents the optimized setting value of the chilled water supply temperature, and s1 represents the initial setting value of the chilled water supply temperature; =ss-zg, where ss represents the expected comfort value and zg represents the highest indoor temperature value; a , b , c The fitting coefficients are denoted as .

2. The method for optimizing the comfort of an air conditioning system according to claim 1, characterized in that, Based on the stated maximum indoor temperature and desired comfort level, the chilled water supply temperature and air supply temperature of the air conditioning system are optimized and controlled, including: Obtain the initial set value of the air supply temperature of the air conditioning system; Based on the initial setpoint of the supply air temperature, the initial setpoint of the chilled water temperature, and the optimized setpoint of the chilled water temperature, the optimized setpoint of the supply air temperature is obtained through the following second formula. The air conditioning system is optimized and controlled based on the optimized setpoints for chilled water supply temperature and air supply temperature. The second formula is as follows: y2=s2+ ; Where y2 represents the optimized setting value of the supply air temperature, and s2 represents the initial setting value of the supply air temperature; =y1-s1; d , e , f The fitting coefficients are denoted as .

3. The method for optimizing the comfort of an air conditioning system according to claim 1, characterized in that, The calculation process for the cumulative solar radiation corresponding to the building surface is as follows: Solar radiation data for the area where the target building is located over a period of time is obtained based on historical meteorological data. This solar radiation data includes direct solar radiation, solar altitude angle, and solar azimuth angle. The instantaneous solar radiation of the building surface is obtained based on the solar radiation data and the orientation of the building surface. This instantaneous solar radiation refers to the horizontal incident solar radiation. The sum of the cumulative solar radiation at the previous moment and the instantaneous solar radiation at the current moment is taken as the cumulative solar radiation at the current moment.

4. The method for optimizing the comfort of an air conditioning system according to claim 1, characterized in that, The calculation process for the cumulative outdoor dry-bulb temperature corresponding to the building surface is as follows: The cumulative outdoor dry-bulb temperature at the previous moment of the day is the sum of the outdoor dry-bulb temperature at the current moment.

5. The method for optimizing the comfort of an air conditioning system according to claim 1, characterized in that, The calculation process for the cumulative cooling capacity of the air conditioning system corresponding to the building surface is as follows: The sum of the cumulative cooling capacity at the previous moment and the instantaneous cooling capacity at the current moment is taken as the cumulative cooling capacity at the current moment.

6. A comfort optimization device for an air conditioning system, characterized in that, include: The determination module is used to determine multiple building facades of a target building that is equipped with an air conditioning system; The acquisition module is used to acquire, in real time, the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity of the air conditioning system for each building surface. The prediction module is used to input the cumulative solar radiation, cumulative outdoor dry-bulb temperature, and cumulative cooling capacity corresponding to the building surface into the temperature prediction model to obtain the temperature prediction value output by the temperature prediction model; each building surface corresponds to a temperature prediction value, and the highest indoor temperature value of the target building is determined from the temperature prediction values ​​corresponding to all building surfaces; wherein, the temperature prediction model is established by training with the historical cumulative solar radiation, historical cumulative outdoor dry-bulb temperature, historical cumulative cooling capacity, and historical indoor temperature values ​​corresponding to the building surface as a sample set; The optimization module is used to optimize the control of the chilled water supply temperature and / or air supply temperature of the air conditioning system based on the indoor maximum temperature value and the comfort expectation value. The optimization module is specifically used for: Obtain the initial set value of the chilled water supply temperature of the air conditioning system; Based on the initial set value of the chilled water supply temperature, the highest indoor temperature value, and the expected comfort value, the optimized set value of the chilled water supply temperature is obtained through the following first formula; The air conditioning system is optimized and controlled based on the optimized setpoint for the chilled water supply temperature. The first formula is as follows: y1=s1+ ; Where y1 represents the optimized setting value of the chilled water supply temperature, and s1 represents the initial setting value of the chilled water supply temperature; =ss-zg, where ss represents the expected comfort value and zg represents the highest indoor temperature value; a , b , c represents the fitting coefficient.

7. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program, implements the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, It stores computer-readable instructions that can be executed by a processor to implement the method as described in any one of claims 1 to 5.

Images