WO2025246763A1 - 空调节能率预测方法、装置、空调及计算机可读存储介质 - Google Patents

空调节能率预测方法、装置、空调及计算机可读存储介质

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Publication number
WO2025246763A1
WO2025246763A1 PCT/CN2025/091531 CN2025091531W WO2025246763A1 WO 2025246763 A1 WO2025246763 A1 WO 2025246763A1 CN 2025091531 W CN2025091531 W CN 2025091531W WO 2025246763 A1 WO2025246763 A1 WO 2025246763A1
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WO
WIPO (PCT)
Prior art keywords
energy
saving
air conditioner
real
time
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/CN2025/091531
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English (en)
French (fr)
Inventor
唐善玄
樊其锋
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
GD Midea Air Conditioning Equipment Co Ltd
Original Assignee
GD Midea Air Conditioning Equipment Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by GD Midea Air Conditioning Equipment Co Ltd filed Critical GD Midea Air Conditioning Equipment Co Ltd
Publication of WO2025246763A1 publication Critical patent/WO2025246763A1/zh
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/46—Improving electric energy efficiency or saving
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/61—Control or safety arrangements characterised by user interfaces or communication using timers
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F1/00—Details not covered by groups G06F3/00 - G06F13/00 and G06F21/00
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power management, i.e. event-based initiation of a power-saving mode
    • G06F1/3234—Power saving characterised by the action undertaken
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F24F2110/64—Airborne particle content
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/50—Air quality properties
    • F24F2110/65—Concentration of specific substances or contaminants

Definitions

  • This application relates to the field of compressor technology, and in particular to a method, apparatus, air conditioner, and computer-readable storage medium for predicting air conditioner energy efficiency.
  • the main objective of this application is to provide a method, device, air conditioner, and computer-readable storage medium for predicting air conditioner energy efficiency, aiming to solve the technical problem of low accuracy of current air conditioner output energy efficiency.
  • this application provides a method for predicting air conditioning energy efficiency, the method comprising:
  • the current environmental information, current operating condition information, and current setting information are input into a preset non-energy-saving power prediction model.
  • the real-time non-energy-saving power is predicted by the non-energy-saving power prediction model, wherein the non-energy-saving power prediction model is constructed from the operating information of the air conditioner in non-energy-saving mode.
  • the real-time energy saving rate is determined based on the real-time energy saving power and the real-time non-energy saving power.
  • the step of obtaining the real-time energy-saving power of the target air conditioner includes:
  • the current environmental information, current operating condition information, and current setting information are input into a preset energy-saving power prediction model.
  • the real-time energy-saving power is predicted by the energy-saving power prediction model, which is constructed from the operating information of the air conditioner in energy-saving mode.
  • the method further includes;
  • the real-time energy-saving power Based on the real-time energy-saving power, the real-time non-energy-saving power, and the preset estimated duration, calculate the energy consumption in energy-saving mode and the energy consumption in non-energy-saving mode within the estimated duration.
  • the expected energy saving corresponding to the expected duration is calculated.
  • the method before the step of inputting the current environmental information, current operating condition information, and current setting information into a preset energy-saving power prediction model, the method further includes:
  • an energy-saving power prediction model is constructed
  • a non-energy-saving power prediction model is constructed.
  • the preset air conditioner is the target air conditioner, an air conditioner of the same model as the target air conditioner, an experimental air conditioner, an air conditioner of the same model as the target air conditioner, or an air conditioner that meets preset conditions as the target air conditioner, wherein the preset conditions are being in the same region, in the same season, or in the same household.
  • the method further includes:
  • the method further includes:
  • the real-time energy saving rate and/or the cumulative energy saving rate are displayed on the screen of the target air conditioner; and/or
  • the real-time energy saving rate and/or the cumulative energy saving rate are pushed to the mobile terminal bound to the target air conditioner.
  • this application also provides an air conditioning energy-saving rate prediction device, the device comprising:
  • the information acquisition module is used to acquire current environmental information, current operating condition information, and current setting information when the target air conditioner turns on the energy-saving mode.
  • An energy-saving power acquisition module is used to acquire the real-time energy-saving power of the target air conditioner
  • the non-energy-saving prediction module is used to input the current environmental information, current operating condition information and current setting information into a preset non-energy-saving power prediction model, and to predict the real-time non-energy-saving power through the non-energy-saving power prediction model.
  • the non-energy-saving power prediction model is constructed from the operating information of the air conditioner in non-energy-saving mode.
  • the energy saving rate prediction module is used to determine the real-time energy saving rate based on the real-time energy saving power and the real-time non-energy saving power.
  • this application also provides an air conditioner, which is a physical device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the steps of the air conditioner energy saving rate prediction method as described above.
  • this application also provides a readable storage medium, which is a computer-readable storage medium, and stores a program for implementing the air conditioning energy saving rate prediction method.
  • the program for implementing the air conditioning energy saving rate prediction method is executed by a processor to implement the steps of the air conditioning energy saving rate prediction method as described above.
  • this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the air conditioning energy saving rate prediction method described above.
  • Figure 1 is a flowchart illustrating an embodiment of the air conditioning energy saving rate prediction method in this application.
  • Figure 2 is a schematic diagram of the process of predicting the energy saving rate using the energy saving power prediction model and the non-energy saving power prediction model in the embodiments of this application;
  • Figure 3 is a schematic diagram of the entire process of a feasible air conditioning energy saving rate prediction method in an embodiment of this application;
  • Figure 4 is a schematic diagram of the structural composition of an air conditioning energy saving rate prediction device in an embodiment of this application.
  • FIG. 5 is a schematic diagram of the equipment structure of the hardware operating environment involved in the air conditioning energy saving rate prediction method in the embodiments of this application.
  • the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as an air conditioner controller, or an electronic device or control device capable of performing the above functions.
  • an air conditioner controller or an electronic device or control device capable of performing the above functions.
  • the following description uses an air conditioner controller as an example to illustrate this embodiment and the subsequent embodiments.
  • the air conditioning energy saving rate prediction method includes:
  • Step S10 When the target air conditioner turns on the energy-saving mode, obtain the current environmental information, current operating condition information, and current setting information;
  • the target air conditioner is one whose energy-saving rate needs to be calculated.
  • the air conditioner When the air conditioner is turned on by the user in energy-saving mode or is in energy-saving mode by default after being turned on, it begins to collect parameters such as current environmental information, operating condition information, and setting information.
  • Environmental information may include temperature, wind speed, and humidity; operating condition information may include the air conditioner's compressor operating frequency, fan speed, and real-time power; and setting information may include parameters input by the user via the air conditioner remote control, such as target temperature, target wind speed, and control mode.
  • the environmental information, operating condition information, and setting information corresponding to the target air conditioner reflect its current operating status and are used as independent variables in the air conditioner energy-saving rate prediction method of this embodiment to predict the corresponding energy-saving rate under the current operating conditions. It is understood that during the operation of the air conditioner, environmental information, operating condition information, and setting information, as independent variables, will affect the actual operating power of the air conditioner (dependent variable), thereby further affecting the air conditioner's energy-saving rate (dependent variable
  • Step S20 Obtain the real-time energy-saving power of the target air conditioner
  • the target air conditioner Since the target air conditioner has already activated its energy-saving mode, its actual operating power can be used as the real-time energy-saving power. This can be achieved by using a power meter (also known as a power monitor or power quality analyzer). Specifically, by setting the power meter to the air conditioner's power line, it can display electrical parameters such as current, voltage, and power in real time, allowing direct reading of the air conditioner's real-time energy-saving power at any given moment. Alternatively, the real-time energy-saving power can be predicted using a pre-built energy-saving power prediction model based on current environmental information, current operating conditions, and current settings. No restrictions are imposed on this approach.
  • Step S30 Input the current environmental information, current operating condition information and current setting information into a preset non-energy-saving power prediction model, and predict the real-time non-energy-saving power through the non-energy-saving power prediction model.
  • the non-energy-saving power prediction model is constructed from the operating information of the air conditioner in non-energy-saving mode.
  • Step S40 Determine the real-time energy saving rate based on the real-time energy saving power and the real-time non-energy saving power.
  • this embodiment employs a pre-built and trained non-energy-saving power prediction model to analyze and process collected current environmental information, operating condition information, and setting information to predict the non-energy-saving power corresponding to the current operating status of the air conditioner. Furthermore, based on the energy-saving power and non-energy-saving power obtained at the current time point, the real-time energy-saving rate is determined.
  • the non-energy-saving power prediction model obtains prediction results from the air conditioner's operating condition parameters at the current time point, the obtained real-time energy-saving power and non-energy-saving power both correspond to the same time point, resulting in a more accurate real-time energy-saving rate that better reflects the current energy-saving status of the air conditioner.
  • the real-time energy saving rate can be obtained by calculating the ratio of real-time energy-saving power to real-time non-energy-saving power, where real-time non-energy-saving power is greater than real-time energy-saving power, and the real-time energy saving rate is a value in the range of 0 to 1, which can be expressed as a percentage.
  • the step of obtaining the real-time energy-saving power of the target air conditioner may include:
  • Step S21 Input the current environmental information, current operating condition information and current setting information into the energy-saving power prediction model, and predict the real-time energy-saving power through the energy-saving power prediction model.
  • the energy-saving power prediction model is constructed from the operating information of the air conditioner in energy-saving mode.
  • a pre-built and trained energy-saving power prediction model can also be used to predict the real-time energy-saving power of the air conditioner.
  • the energy-saving power prediction model is built and trained using parameters such as historical environmental information, historical operating condition information, and historical setting information of the air conditioner in energy-saving mode.
  • the method further includes;
  • Step S50 Calculate the energy consumption in energy-saving mode and the energy consumption in non-energy-saving mode within the estimated time period based on the real-time energy-saving power, the real-time non-energy-saving power, and the preset estimated time period.
  • Step S60 Calculate the expected energy saving corresponding to the expected duration based on the energy consumption of the energy-saving mode and the energy consumption of the non-energy-saving mode.
  • this embodiment of the application can not only predict the current real-time energy saving rate based on the current environmental information, operating condition information and setting information, but also predict the energy saving situation over a future period of time.
  • the air conditioner can predict the future energy-saving situation when the air conditioner continues to use the energy-saving mode.
  • This is mainly achieved by calculating the product of the air conditioner's real-time energy-saving power and the expected duration, which yields the expected energy consumption over the future period, i.e., the energy consumption in the energy-saving mode.
  • Calculating the air conditioner's real-time non-energy-saving power and the expected duration yields the non-energy-saving mode energy consumption assuming the energy-saving mode is not activated.
  • the difference between the two is calculated to obtain the expected energy saving over the expected duration after the air conditioner activates the energy-saving mode compared to when it is not activated. This allows users to intuitively experience the energy savings brought by the energy-saving mode, improving the intelligence level of the air conditioner.
  • the method further includes:
  • Step S30 Obtain the real-time energy saving rate of the target air conditioner in each historical period after the energy saving mode is turned on;
  • Step S40 Calculate the average value of the real-time energy saving rate corresponding to each of the historical periods and the current period to obtain the cumulative energy saving rate.
  • steps S10 and S20 are executed according to a preset cycle to determine the real-time energy saving rate corresponding to the current cycle.
  • the real-time energy saving rate of each previous historical cycle can be obtained and the average value can be calculated by combining it with the real-time energy saving rate of the current cycle.
  • the average energy saving rate of each cycle is the cumulative energy saving rate.
  • the cumulative energy saving rate reflects the average energy saving over a period of time, which can avoid the influence of random errors in certain cycles and more accurately reflect the overall energy saving of the air conditioner.
  • the process of predicting the energy saving rate by combining the energy saving power prediction model and the non-energy saving power prediction model in this application embodiment includes: First, when the air conditioner is turned on and the energy saving mode is activated, the energy saving rate prediction model composed of the pre-built energy saving power prediction model and the non-energy saving power prediction model is called.
  • the real-time energy saving rate is output. Then, the cumulative energy saving rate is calculated based on the real-time energy saving rate of multiple cycles. In the new cycle, the process of calculating the real-time energy saving rate is returned, and the cumulative energy saving rate is updated in real time after the real-time energy saving rate of each cycle is determined.
  • the method further includes:
  • Step S50 Display the real-time energy saving rate and/or the cumulative energy saving rate on the screen of the target air conditioner; and/or,
  • Step S60 Push the real-time energy saving rate and/or the cumulative energy saving rate to the mobile terminal bound to the target air conditioner.
  • a display screen installed on the air conditioner casing can show the values of the real-time and cumulative energy-saving rates.
  • more intelligent air conditioners usually have a corresponding app, which can display the cumulative energy-saving power and the current real-time energy-saving power after the air conditioner has been turned on in energy-saving mode on the user's mobile terminal (such as a mobile phone) through the app bound to the air conditioner. This allows users to intuitively and accurately obtain the energy-saving status of the air conditioner and improve the user experience.
  • T time interval e.g. 30s
  • the method may further include:
  • Step A10 Collect first environmental information, first operating condition information, and first setting information of the preset air conditioner in energy-saving mode to obtain an energy-saving sample dataset, wherein the operating condition information includes at least the operating power;
  • Step A20 Construct an energy-saving power prediction model based on the energy-saving sample dataset
  • Step A30 Collect the second environmental information, second operating condition information, and second setting information of the preset air conditioner in non-energy-saving mode to obtain a non-energy-saving sample dataset;
  • Step A40 Construct a non-energy-saving power prediction model based on the non-energy-saving sample dataset.
  • This application embodiment also provides a method for pre-constructing an energy saving rate prediction model.
  • steps A10 to A40 include methods for constructing an energy saving power prediction model and a non-energy saving power prediction model, respectively.
  • the real-time energy saving power used to calculate the real-time energy saving rate is obtained by real-time acquisition of the operating power of the air conditioner in energy saving mode, then there is no need to construct an energy saving power prediction model, that is, only steps A30 and A40 need to be executed.
  • the activation of the air conditioner's energy-saving mode is used as the data filtering condition.
  • the energy-saving power prediction model learns the correlation and inherent patterns between the independent and dependent variables in the energy-saving sample dataset during training. Therefore, using this model, when environmental information, operating condition information, and user settings are input, real-time energy-saving power can be predicted.
  • second environmental information, second operating condition information, and second setting information are collected when the preset air conditioner is in non-energy-saving mode to obtain a non-energy-saving sample dataset.
  • air conditioner's energy-saving mode being off as the data filtering condition, and with environmental information, operating condition information, and user settings as independent variables and power as the dependent variable
  • the preset air conditioner is the target air conditioner, the experimental air conditioner, the air conditioner of the same model as the target air conditioner, or the air conditioner that meets the preset conditions as being in the same region, in the same season, or in the same household.
  • the energy-saving rate prediction model When training and constructing the energy-saving rate prediction model, real-time energy-saving rate prediction model, or real-time non-energy-saving rate prediction model corresponding to the preset air conditioner, the corresponding historical operating data of different preset air conditioners can be collected according to specific needs.
  • the model constructed is a personalized energy-saving rate prediction model, which is specifically used for predicting the energy-saving rate of the target air conditioner. Its advantages are small training data volume, fast construction speed, strong targeting, and high prediction accuracy, but it is only applicable to this air conditioner.
  • the preset air conditioner is the same model as the target air conditioner
  • a large amount of historical operating data of the same model of air conditioner can be collected to build a general energy saving rate prediction model.
  • the data volume is large, and the energy saving rate prediction model can be used for multiple air conditioners of the same model, with a wide range of applications.
  • energy-saving rate prediction models are typically developed for specific scenarios. For example, models built by collecting historical data from a large number of air conditioners in a specific region tend to have better prediction accuracy for that specific region and can better learn the inherent patterns and relationships between environmental information and energy-saving power in that region. Historical operating data of air conditioners collected under specific seasonal conditions enables the energy-saving rate prediction model to better understand the inherent correlation between environmental information and energy-saving power in the current season. Energy-saving rate prediction models trained using air conditioner operating data from a specific household have high prediction performance for the household's air conditioner energy-saving rate.
  • This application's embodiments calculate the power consumption under the assumptions of energy saving/energy saving not being enabled using prior prediction models such as real-time energy saving rate prediction models and real-time non-energy saving rate prediction models, thereby obtaining the real-time energy saving rate. This is more accurate than traditional solutions that calculate energy saving rates using energy saving data at different points in time.
  • the real-time and cumulative energy saving rates can be displayed intuitively, making it more intuitive and intelligent.
  • the air conditioning energy saving rate prediction device includes:
  • the information acquisition module 10 is used to acquire current environmental information, current operating condition information, and current setting information when the target air conditioner turns on the energy-saving mode.
  • Energy-saving power acquisition module 20 is used to acquire the real-time energy-saving power of the target air conditioner
  • the non-energy-saving prediction module 30 is used to input the current environmental information, current operating condition information and current setting information into a preset non-energy-saving power prediction model, and to predict the real-time non-energy-saving power through the non-energy-saving power prediction model.
  • the non-energy-saving power prediction model is constructed from the operating information of the air conditioner in non-energy-saving mode.
  • the energy saving rate prediction module 40 is used to determine the real-time energy saving rate based on the real-time energy saving power and the real-time non-energy saving power.
  • the energy-saving power acquisition module 20 is further configured to:
  • the current environmental information, current operating condition information, and current setting information are input into a preset energy-saving power prediction model.
  • the real-time energy-saving power is predicted by the energy-saving power prediction model, which is constructed from the operating information of the air conditioner in energy-saving mode.
  • the air conditioning energy efficiency prediction device further includes an energy saving prediction module, the energy saving prediction module being used for:
  • the real-time energy-saving power Based on the real-time energy-saving power, the real-time non-energy-saving power, and the preset estimated duration, calculate the energy consumption in energy-saving mode and the energy consumption in non-energy-saving mode within the estimated duration.
  • the expected energy saving corresponding to the expected duration is calculated.
  • the air conditioning energy saving rate prediction device further includes a model building module, the model building module being used for:
  • an energy-saving power prediction model is constructed
  • a non-energy-saving power prediction model is constructed.
  • the preset air conditioner is the target air conditioner, the experimental air conditioner, the air conditioner of the same model as the target air conditioner, or the air conditioner that meets the preset conditions as being in the same region, in the same season, or in the same household.
  • the air conditioning energy saving rate prediction device further includes a cumulative energy saving module, the cumulative energy saving module being used for:
  • the air conditioning energy saving rate prediction device further includes a display module, the display module being used for:
  • the real-time energy saving rate and/or the cumulative energy saving rate are displayed on the screen of the target air conditioner
  • the air conditioner energy-saving rate prediction device provided in this application employing the air conditioner energy-saving rate prediction method in the above embodiments, can solve the technical problem of low accuracy in the current air conditioner output energy-saving rate.
  • the beneficial effects of the air conditioner energy-saving rate prediction device provided in this application are the same as those of the air conditioner energy-saving rate prediction method provided in the above embodiments, and other technical features in the air conditioner energy-saving rate prediction device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.
  • This application also provides an air conditioner, which includes at least: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the air conditioner energy saving rate prediction method in the above embodiments.
  • FIG5 a structural schematic diagram of an air conditioner suitable for implementing embodiments of the present disclosure is shown.
  • the air conditioner shown in FIG5 is merely an example and should not impose any limitation on the functionality and scope of use of embodiments of the present disclosure.
  • the air conditioner may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004.
  • the RAM 1004 also stores various programs and data required for the operation of the air conditioner.
  • the processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005.
  • An input/output (I/O) interface 1006 is also connected to the bus.
  • the following systems can be connected to the I/O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009.
  • the communication device 1009 allows the air conditioner to communicate wirelessly or wiredly with other devices to exchange data.
  • the figure shows air conditioners with various systems, it should be understood that it is not required to implement or have all of the systems shown. More or fewer systems may be implemented alternatively.
  • embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts.
  • the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002.
  • processing device 1001 it performs the functions defined in the methods of embodiments of this disclosure.
  • the air conditioner provided in this application employing the air conditioner energy-saving rate prediction method in the above embodiments, can solve the technical problem of low accuracy in the energy-saving rate output of current air conditioners.
  • the beneficial effects of the air conditioner provided in this application are the same as those of the air conditioner energy-saving rate prediction method provided in the above embodiments, and other technical features of this air conditioner are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.
  • This application also provides a computer-readable storage medium having computer-readable program instructions stored thereon, the computer-readable program instructions being used to execute the air conditioning energy saving rate prediction method in the above embodiments.
  • the computer-readable storage medium provided in this application embodiment may be, for example, a USB flash drive, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.
  • the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device.
  • the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.
  • the aforementioned computer-readable storage medium may be included in the air conditioner; or it may exist independently and not be installed in the air conditioner.
  • the aforementioned computer-readable storage medium carries one or more programs.
  • the air conditioner executes one or more of these programs, the air conditioner causes the following: when the target air conditioner activates its energy-saving mode, it acquires current environmental information, current operating condition information, and current setting information; acquires the real-time energy-saving power of the target air conditioner; inputs the current environmental information, current operating condition information, and current setting information into a preset non-energy-saving power prediction model, and predicts the real-time non-energy-saving power through the non-energy-saving power prediction model, wherein the non-energy-saving power prediction model is constructed from the operating information of the air conditioner in non-energy-saving mode; and determines the real-time energy-saving rate based on the real-time energy-saving power and the real-time non-energy-saving power.
  • Computer program code for performing the operations of this disclosure can be written in one or more programming languages or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, and C++, and conventional procedural programming languages such as the "C" language or similar programming languages.
  • the program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server.
  • the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).
  • LAN local area network
  • WAN wide area network
  • each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function.
  • the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved.
  • each block in the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.
  • the modules described in the embodiments of this disclosure can be implemented in software or hardware.
  • the names of the modules do not necessarily limit the functionality of the unit itself.
  • the readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions for executing the above-described air conditioner energy-saving rate prediction method, thereby solving the technical problem of low accuracy in the energy-saving rate output of current air conditioners.
  • the beneficial effects of the computer-readable storage medium provided in this application embodiment are the same as the beneficial effects of the air conditioner energy-saving rate prediction method provided in the above embodiments, and will not be repeated here.
  • This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the air conditioning energy saving rate prediction method described above.
  • the computer program product provided in this application can solve the technical problem of low accuracy in the energy saving rate of current air conditioners. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the air conditioner energy saving rate prediction method provided in the above embodiments, and will not be repeated here.

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Abstract

本申请公开了一种空调节能率预测方法、装置、空调及计算机可读存储介质，该空调节能率预测方法包括：当目标空调开启节能模式时，获取当前环境信息、当前工况信息以及当前设定信息；获取目标空调的实时节能功率；将当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过非节能功率预测模型预测获得实时非节能功率，其中，非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；根据实时节能功率和实时非节能功率，确定实时节能率。

Description

空调节能率预测方法、装置、空调及计算机可读存储介质
本申请要求于2024年5月30日申请的、申请号为202410692564.X的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及压缩机技术领域，尤其涉及一种空调节能率预测方法、装置、空调及计算机可读存储介质。
 背景技术
目前的空调节能功能应用越来越广泛，起到了一定的电能节约效果，而在进行空调的节能率计算时，通常基于未开启节能模式前的能耗情况与开启节能模式后的实际能耗进行比较来确定。由于当用户开启空调节能功能时，只能采集到节能状态下的能耗值，而无法取得非节能状态下的能耗值，因此现有的节能率计算方法无法通过后验检测方式获得实时节能率，用于计算节能率的能耗值并不属于同一时间点，通过不同时间点的能耗值计算节能率会导致存在节能率不够准确的缺陷。
 技术问题
本申请的主要目的在于提供一种空调节能率预测方法、装置、空调及计算机可读存储介质，旨在解决目前空调输出的节能率准确性较低的技术问题。
 技术解决方案
为实现上述目的，本申请提供一种空调节能率预测方法，所述空调节能率预测方法包括：
获取所述目标空调的实时节能功率；
将所述当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过所述非节能功率预测模型预测获得实时非节能功率，其中，所述非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；
根据所述实时节能功率和所述实时非节能功率，确定实时节能率。
在一些实施例中，所述获取所述目标空调的实时节能功率的步骤，包括：
将所述当前环境信息、当前工况信息以及当前设定信息输入预设的节能功率预测模型，通过所述节能功率预测模型预测获得实时节能功率，其中，所述节能功率预测模型是由空调在节能模式下的运行信息构建得到。
在一些实施例中，在所述根据所述实时节能功率和所述实时非节能功率，确定实时节能率的步骤之后，所述方法还包括；
根据所述实时节能功率、所述实时非节能功率以及预设的预计时长，计算所述预计时长内的节能模式能耗和非节能模式能耗；
基于所述节能模式能耗和所述非节能模式能耗，计算所述预计时长对应的预计节能量。
在一些实施例中，在所述将所述当前环境信息、当前工况信息以及当前设定信息输入预设的节能功率预测模型的步骤之前，所述方法还包括：
采集预设空调处于节能模式下的第一环境信息、第一工况信息以及第一设定信息，获得节能样本数据集，其中，所述工况信息至少包括运行功率；
根据所述节能样本数据集，构建节能功率预测模型；
采集所述预设空调处于非节能模式下的第二环境信息、第二工况信息以及第二设定信息，获得非节能样本数据集；
根据所述非节能样本数据集，构建非节能功率预测模型。
在一些实施例中，所述预设空调为所述目标空调、与所述目标空调同型号的空调、实验空调、与所述目标空调同型号的空调、或与所述目标空调符合预设条件的空调，其中，所述预设条件为处于同一地区、处于同一季节、或处于同一家庭。
在一些实施例中，在所述根据所述实时节能功率和所述实时非节能功率，确定实时节能率的步骤之后，所述方法还包括：
获取所述目标空调在开启节能模式后在各个历史周期内分别对应的实时节能率；
计算各所述历史周期以及当前周期分别对应的实时节能率的平均值，得到累计节能率。
在一些实施例中，在所述计算各所述历史周期以及当前周期分别对应的实时节能率的平均值，得到累计节能率的步骤之后，所述方法还包括：
在所述目标空调的屏幕上展示所述实时节能率和/或所述累计节能率；和/或
向与所述目标空调绑定的移动终端推送所述实时节能率和/或所述累计节能率。
此外，为实现上述目的，本申请还提供一种空调节能率预测装置，所述装置包括：
信息获取模块，用于当目标空调开启节能模式时，获取当前环境信息、当前工况信息以及当前设定信息；
节能功率获取模块，用于获取所述目标空调的实时节能功率；
非节能预测模块，用于将所述当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过所述非节能功率预测模型预测获得实时非节能功率，其中，所述非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；
节能率预测模块，用于根据所述实时节能功率和所述实时非节能功率，确定实时节能率。
此外，为实现上述目的，本申请还提供一种空调，所述空调为实体设备，所述空调包括：至少一个处理器；以及，与所述至少一个处理器通信连接的存储器；其中，所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如上所述空调节能率预测方法的步骤。
此外，为实现上述目的，本申请还提供一种可读存储介质，所述可读存储介质为计算机可读存储介质，所述计算机可读存储介质上存储有实现空调节能率预测方法的程序，所述实现空调节能率预测方法的程序被处理器执行以实现如上所述空调节能率预测方法的步骤。
此外，为实现上述目的，本申请还提供一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现如上述的空调节能率预测方法的步骤。
 附图说明
此处的附图被并入说明书中并构成本说明书的一部分，示出了符合本申请的实施例，并与说明书一起用于解释本申请的原理。
为了更清楚地说明本申请实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，对于本领域普通技术人员而言，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本申请实施例中空调节能率预测方法实施例的流程示意图；
图2为本申请实施例中通过节能功率预测模型和非节能功率预测模型预测节能率的流程示意图；
图3为本申请实施例中一种可行的空调节能率预测方法全流程示意图；
图4为本申请实施例中一种空调节能率预测装置的结构组成示意图；
图5为本申请实施例中空调节能率预测方法涉及的硬件运行环境的设备结构示意图。
本申请目的实现、功能特点及优点将结合实施例，参照附图做进一步说明。
 本发明的实施方式
为使本申请的上述目的、特征和优点能够更加明显易懂，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述。显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动的前提下所获得的所有其它实施例，均属于本申请保护的范围。
应当理解，此处所描述的具体实施例仅仅用以解释本申请的技术方案，并不用于限定本申请。
为了更好的理解本申请的技术方案，下面将结合说明书附图以及具体的实施方式进行详细的说明。
本实施例的执行主体可以是一种具有数据处理、网络通信以及程序运行功能的计算服务设备，例如空调的控制器等，或者是一种能够实现上述功能的电子设备、控制设备等。以下以空调控制器作为执行主体为例，对本实施例及下述各实施例进行说明。
现有的空调节能功能，难以向用户展现实时的节能效果，且本次运行的累计节能效果的结果也不准确，并且用户难以对空调使用中节能有更加直观的体验。另外，当用户开启节能功能时，只能取得当前节能状态下的能耗值，而无法取得非节能状态下的能耗值，因此，无法通过后验检测出实时的节能率。
为了克服上述现有技术中存在的技术问题和缺陷，本申请实施例提供了一种空调节能率预测方法，参照图1，图1为本申请空调节能率预测方法实施例的流程示意图，所述空调节能率预测方法包括：
步骤S10，当目标空调开启节能模式时，获取当前环境信息、当前工况信息以及当前设定信息；
本申请实施例中，目标空调为需要计算节能率的空调，当该空调被用户开启节能模式或开机后默认为节能模式时，即开始采集当前环境信息、工况信息以及设定信息等参数，其中，环境信息可以包括温度、风速、湿度等，工况信息可以包括空调的压缩机运行频率、风机风速以及实时功率等，设定信息可以包括用户通过空调遥控器向空调输入的设定参数，例如目标温度、目标风速、控制模式等。目标空调所对应的环境信息、工况信息以及设定信息反映了空调当前的运行状况，在本申请实施例的空调节能率预测方法中用于作为自变量使用，以预测出当前运行状况下对应的节能率。可以理解的是，在空调工作的过程中，环境信息、工况信息以及设定信息作为自变量会影响空调的实际运行功率（因变量），从而进一步影响到空调节能率（因变量）。
步骤S20，获取所述目标空调的实时节能功率；
其中，因为目标空调已经开启了节能模式，所以可以采用目标空调的实际运行功率作为实时节能功率，可以通过使用功率计（也称为电力监测仪或电能质量分析仪）来获取实际运行功率，具体通过将功率计设置到空调的电源线上，能实时显示电流、电压及功率等电参数，这样可以直接读取空调在任何时刻的实时节能功率；另外，也可以通过预先构建的节能功率预测模型根据当前环境信息、当前工况信息以及当前设定信息预测得到实时节能功率，在此不做限制。
步骤S30，将所述当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过所述非节能功率预测模型预测获得实时非节能功率，其中，所述非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；
步骤S40，根据所述实时节能功率和所述实时非节能功率，确定实时节能率。
为了克服传统节能率计算方式中节能功率的数据和非节能率的数据来源于不同时间节点的缺陷，本申请实施例中采用预先构建训练好的非节能功率预测模型来对采集到的当前环境信息、工况信息以及设定信息等信息进行分析处理，以预测在空调的当前运行状况下对应的非节能功率，再进一步根据获取到当前时间点的节能功率和非节能功率确定实时节能率，非节能功率预测模型是通过当前时间点的空调运行状况参数得到的预测结果，所以获取到的实时节能功率和非节能功率均对应同一时间点，对应的实时节能率也具有更高的准确度，能更好地体现出空调当前的节能情况。
示例性地，可以通过计算实时节能功率与实时非节能功率的比值，得到实时节能率，其中，实时非节能功率大于实时节能功率，实时节能率为0至1范围中的数值，可以以百分比形式展现。
在一些实施例中，所述获取所述目标空调的实时节能功率的步骤，可以包括：
步骤S21，将所述当前环境信息、当前工况信息以及当前设定信息输入所述节能功率预测模型，通过所述节能功率预测模型预测获得实时节能功率，其中，所述节能功率预测模型是由空调在节能模式下的运行信息构建得到。
可以理解的是，除了通过直接采集空调的实时运行功率方式确定空调的实时节能功率，也可以采用预先构建训练的节能功率预测模型的方法来预测空调的实时节能功率，节能功率预测模型是通过空调在节能模式下的历史环境信息、历史工况信息以及历史设定信息等参数构建训练得到。
在一些实施例中，在所述根据所述实时节能功率和所述实时非节能功率，确定实时节能率的步骤之后，所述方法还包括；
步骤S50，根据所述实时节能功率、所述实时非节能功率以及预设的预计时长，计算所述预计时长内的节能模式能耗和非节能模式能耗；
步骤S60，基于所述节能模式能耗和所述非节能模式能耗，计算所述预计时长对应的预计节能量。
在已经预先构建节能功率预测模型的情况下，本申请实施例中不但可以根据当前的环境信息、工况信息以及设定信息预测出当前的实时节能率，还可以对未来一段时长内的节能情况进行预测。
示例性地，预测空调在未来一段时长内持续保持当前的环境信息、工况信息以及设定信息的情况下，可以预测在空调继续使用节能模式的情况下的未来节能情况，主要通过计算空调的实时节能功率和预计时长的积，可以得出在未来一段时长内的预期能耗量，即节能模式能耗；计算空调的实时非节能功率与预计时长，可以得到假设未开启节能模式的情况下空调的非节能模式能耗，最后计算两者之差，得到本次空调开启节能模式后相比未开启节能模式的情况下在预计时长内的预计节能量，让用户能直观地感受到节能模式所带来的电能节约，提高空调的智能化程度。
进一步地，在所述根据所述实时节能功率和所述实时非节能功率，确定实时节能率的步骤之后，所述方法还包括：
步骤S30，获取所述目标空调在开启节能模式后在各个历史周期内分别对应的实时节能率；
步骤S40，计算各所述历史周期以及当前周期分别对应的实时节能率的平均值，得到累计节能率。
需要说明的是，本申请实施例中按照预设周期执行步骤S10和步骤S20，以确定本周期对应的实时节能率，在获得实时节能率后，为了进一步体现空调自开启节能模式以来的总体节能率情况，可以获取之前各个历史周期的实时节能率，并结合当前周期的实时节能率进行平均值计算，每个周期的平均节能率即为累计节能率，累计节能率反映了一段时长内的平均节能情况，能避免在某些周期的偶然误差影响，更准确地反映出空调的整体节能情况。
示例性地，结合前述各实施例的内容，本申请实施例中，首先收集周期i的环境信息、工况信息、用户设定信息；再获取实时节能功率和实时非节能功率，并确定实时节能率S(i)=u(i)=f(i)/g(i)，其中，i为当前周期序号，S(i)为实时节能率，u(i)是指节能率预测模型u输出的实时节能率，f(i)为实时节能功率，g(i)为实时非节能功率，其中，节能率预测模型u由实时节能功率预测模型和f和实时非节能预测模型g组成（表示为u=f/g），最后在本次节能功能运行周期内，统计各运行周期分别对应的实时节能率的平均值，作为累计节能率Savg=(∑S(i))/i。
示例性地，参照图2，结合前述各实施例的内容，本申请实施例中结合节能功率预测模型和非节能功率预测模型预测节能率的流程包括：首先，当空调开机且开启节能模式的情况下，调用预先构建的节能功率预测模型和非节能功率预测模型组成的节能率预测模型，其中，节能率预测模型计算节能率的公式表示为u=f/g，其中，u为节能率，f为节能功率，g为非节能功率，最终输出实时节能率，然后根据多个周期的实时节能率计算出累计节能率，在新的周期，返回计算实时节能率的过程，并在每个周期的实时节能率确定后，实时更新累计节能率。
在一些实施例中，在所述计算各所述历史周期以及当前周期分别对应的实时节能率的平均值，得到累计节能率的步骤之后，所述方法还包括：
步骤S50，在所述目标空调的屏幕上展示所述实时节能率和/或所述累计节能率；和/或，
步骤S60，向与所述目标空调绑定的移动终端推送所述实时节能率和/或所述累计节能率。
为了更直观清晰地向用户展示空调当前的实时节能效果和累计节能效果，一方面，空调外壳上装设的显示屏可以显示该实时节能率和累计节能率的数值，另一方面，智能化程度更高的空调通常开发有对应的App（application，应用程序），可以通过与该空调绑定的App在用户的移动终端（如手机）上展示本次空调开启节能模式后的累计节能功率和当前的实时节能功率，让用户能直观准确地获取空调的节能情况，提高用户体验感。
为了更直观地理解本申请实施例中提供的空调节能率预测方法的全部流程，作为一种示例，图3展示了一种可行的空调节能率预测方法的具体执行步骤：首先，空调开机，且开启节能功能，每间隔T时间（如30s），执行如下逻辑，收集当前的环境信息、工况信息、用户设定信息，再调用节能率预测模型u=f/g，其中，所述节能率预测模型u是由实时节能功率预测模型f和实时非节能功率g组成，获得实时节能率S(i)=u(i)=f(i)/g(i)，然后统计本次节能运行所有周期的实时节能率的平均值，作为累计节能率Savg=(∑S(i))/i。
在一些实施例中，在所述将所述当前环境信息、当前工况信息以及当前设定信息输入预设的节能功率预测模型的步骤之前，所述方法还可以包括：
步骤A10，采集预设空调处于节能模式下的第一环境信息、第一工况信息以及第一设定信息，获得节能样本数据集，其中，所述工况信息至少包括运行功率；
步骤A20，根据所述节能样本数据集，构建节能功率预测模型；
步骤A30，采集所述预设空调处于非节能模式下的第二环境信息、第二工况信息以及第二设定信息，获得非节能样本数据集；
步骤A40，根据所述非节能样本数据集，构建非节能功率预测模型。
本申请实施例还提供了一种预先构建节能率预测模型的方法，需要说明的是，步骤A10至步骤A40中包括分别构建节能功率预测模型和非节能功率预测模型的方法，在实际应用场景中，若用于计算实时节能率的实时节能功率是通过实时采集空调在节能模式下的运行功率得到的，则无需构建节能功率预测模型，即仅需要执行步骤A30和步骤A40即可。
具体地，采集预设空调处于节能模式下的第一环境信息、第一工况信息以及第一设定信息，获得节能样本数据集的过程中，以空调的节能模式开启作为数据筛选条件，以环境信息、工况信息、用户设定等信息为自变量，以功率为因变量，构建“f(环境信息、工况信息、用户设定)=功率”的节能功率预测模型。可以理解的是，节能功率预测模型在训练中学习了节能样本数据集中各自变量与因变量之间的关联关系和内在规律，因此通过该模型，当输入环境信息、工况信息、用户设定时，可以预测实时的节能功率。
具体地，采集所述预设空调处于非节能模式下的第二环境信息、第二工况信息以及第二设定信息，获得非节能样本数据集，以空调的节能模式关闭作为数据筛选条件以环境信息、工况信息、用户设定等信息为自变量，以功率为因变量，构建“g(环境信息、工况信息、用户设定)=功率”的非节能功率预测模型。可以理解的是，非节能功率预测模型在训练中学习了非节能样本数据集中各自变量与因变量之间的关联关系和内在规律，因此通过该模型，当输入环境信息、工况信息、用户设定时，可以预测实时的非节能功率。
在一些实施例中，所述预设空调为所述目标空调、实验空调、与所述目标空调同型号的空调、或与所述目标空调符合预设条件的空调，其中，所述预设条件为处于同一地区、处于同一季节、或处于同一家庭。
在训练构建预设空调对应的节能率预测模型、实时节能率预测模型或实时非节能率预测模型时，可以根据具体需求采集不同的预设空调的相应历史运行数据。
具体地，当预设空调为目标空调时，构建的模型为个性化节能率预测模型，专用于该目标空调的节能率预测，优势是训练数据量小，构建速度快，针对性强，预测精度高，但仅适用该空调。
当预设空调为与所述目标空调同型号的空调时，可以采集大量同型号空调的历史运行数据来构建通用节能率预测模型，数据量大，构建得到的节能率预测模型可用于同型号的多台空调，应用范围广。
当预设空调为与所述目标空调符合相同预设条件的空调时，通常针对一些特殊场景，例如通过对特定地区的大量空调进行历史数据采集，构建得到的节能率预测模型对该特定地区的模型的预测精度较好，能更好地学习该地区的环境信息与节能功率之间的内在规律和联系；在特定季节情况下采集的空调历史运行数据能使节能率预测模型更好地理解当前季节的环境信息与节能功率之间的内在关联；对于特定家庭的空调运行数据采集训练得到的节能率预测模型对该家庭内的空调节能率预测效果较高；而通过实验室下的空调历史运行数据采集，构建得到通用化节能率预测模型，优势是原始数据采集较为精确，适应范围较广，但数据量较小。在实际的模型训练中，可以根据实际需求选择合适的预设空调样本，得到针对性强，预测精度高的节能率预测模型。
本申请实施例通过实时节能率预测模型、实时非节能率预测模型等先验预测模型，计算出假设开启节能/未开启节能下的功率，从而得出实时节能率，相比传统方案中利用不同时间点的节能情况计算节能率更加准确。当用户开启空调的节能功能时，可以向用户直观的展示实时与累积节能率，更加直观和智能。
本申请实施例还提供一种空调节能率预测装置，参照图4，所述空调节能率预测装置包括：
信息获取模块10，用于当目标空调开启节能模式时，获取当前环境信息、当前工况信息以及当前设定信息；
节能功率获取模块20，用于获取所述目标空调的实时节能功率；
非节能预测模块30，用于将所述当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过所述非节能功率预测模型预测获得实时非节能功率，其中，所述非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；
节能率预测模块40，用于根据所述实时节能功率和所述实时非节能功率，确定实时节能率。
在一些实施例中，所述节能功率获取模块20还用于：
将所述当前环境信息、当前工况信息以及当前设定信息输入预设的节能功率预测模型，通过所述节能功率预测模型预测获得实时节能功率，其中，所述节能功率预测模型是由空调在节能模式下的运行信息构建得到。
在一些实施例中，所述空调节能率预测装置还包括节能预计模块，所述节能预计模块用于：
根据所述实时节能功率、所述实时非节能功率以及预设的预计时长，计算所述预计时长内的节能模式能耗和非节能模式能耗；
基于所述节能模式能耗和所述非节能模式能耗，计算所述预计时长对应的预计节能量。
在一些实施例中，所述空调节能率预测装置还包括模型构建模块，所述模型构建模块用于：
采集预设空调处于节能模式下的第一环境信息、第一工况信息以及第一设定信息，获得节能样本数据集，其中，所述工况信息至少包括运行功率；
根据所述节能样本数据集，构建节能功率预测模型；
采集所述预设空调处于非节能模式下的第二环境信息、第二工况信息以及第二设定信息，获得非节能样本数据集；
根据所述非节能样本数据集，构建非节能功率预测模型。
在一些实施例中，所述预设空调为所述目标空调、实验空调、与所述目标空调同型号的空调、或与所述目标空调符合预设条件的空调，其中，所述预设条件为处于同一地区、处于同一季节、或处于同一家庭。
在一些实施例中，所述空调节能率预测装置还包括累计节能模块，所述累计节能模块用于：
获取所述目标空调在开启节能模式后在各个历史周期内分别对应的实时节能率；
计算各所述历史周期以及当前周期分别对应的实时节能率的平均值，得到累计节能率。
在一些实施例中，所述空调节能率预测装置还包括展示模块，所述展示模块用于：
在所述目标空调的屏幕上展示所述实时节能率和/或所述累计节能率；
和/或，向与所述目标空调绑定的移动终端推送所述实时节能率和/或所述累计节能率。
本申请提供的空调节能率预测装置，采用上述实施例中的空调节能率预测方法，能够解决目前空调输出的节能率准确性较低的技术问题。与现有技术相比，本申请提供的空调节能率预测装置的有益效果与上述实施例提供的空调节能率预测方法的有益效果相同，且所述空调节能率预测装置中的其他技术特征与上述实施例方法公开的特征相同，在此不做赘述。
本申请实施例还提供一种空调，所述空调至少包括：至少一个处理器；以及，与至少一个处理器通信连接的存储器；其中，存储器存储有可被至少一个处理器执行的指令，指令被至少一个处理器执行，以使至少一个处理器能够执行上述实施例中的空调节能率预测方法。
下面参考图5，其示出了适于用来实现本公开实施例的空调的结构示意图。图5示出的空调仅仅是一个示例，不应对本公开实施例的功能和使用范围带来任何限制。
如图5所示，空调可以包括处理装置1001（例如中央处理器、图形处理器等），其可以根据存储在只读存储器（ROM：Read Only Memory）1002中的程序或者从存储装置1003加载到随机访问存储器（RAM：Random Access Memory）1004中的程序而执行各种适当的动作和处理。在RAM1004中，还存储有空调操作所需的各种程序和数据。处理装置1001、ROM1002以及RAM1004通过总线1005彼此相连。输入/输出（I/O）接口1006也连接至总线。通常，以下系统可以连接至I/O接口1006：包括例如触摸屏、触摸板、键盘、鼠标、图像传感器、麦克风、加速度计、陀螺仪等的输入装置1007；包括例如液晶显示器（LCD：Liquid Crystal Display）、扬声器、振动器等的输出装置1008；包括例如磁带、硬盘等的存储装置1003；以及通信装置1009。通信装置1009可以允许空调与其他设备进行无线或有线通信以交换数据。虽然图中示出了具有各种系统的空调，但是应理解的是，并不要求实施或具备所有示出的系统。可以替代地实施或具备更多或更少的系统。
特别地，根据本公开的实施例，上文参考流程图描述的过程可以被实现为计算机软件程序。例如，本公开的实施例包括一种计算机程序产品，其包括承载在计算机可读介质上的计算机程序，该计算机程序包含用于执行流程图所示的方法的程序代码。在这样的实施例中，该计算机程序可以通过通信装置从网络上被下载和安装，或者从存储装置1003被安装，或者从ROM1002被安装。在该计算机程序被处理装置1001执行时，执行本公开实施例的方法中限定的上述功能。
本申请提供的空调，采用上述实施例中的空调节能率预测方法，能解决目前空调输出的节能率准确性较低的技术问题。与现有技术相比，本申请实施例提供的空调的有益效果与上述实施例提供的空调节能率预测方法的有益效果相同，且该空调中的其他技术特征与上一实施例方法公开的特征相同，在此不做赘述。
应当理解，本公开的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式的描述中，具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
以上所述，仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以所述权利要求的保护范围为准。
本申请实施例还提供一种计算机可读存储介质，具有存储在其上的计算机可读程序指令，计算机可读程序指令用于执行上述实施例中的空调节能率预测方法。
本申请实施例提供的计算机可读存储介质例如可以是U盘，但不限于电、磁、光、电磁、红外线、或半导体的系统、系统或器件，或者任意以上的组合。计算机可读存储介质的更具体例子可以包括但不限于：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机访问存储器（RAM：Random Access Memory）、只读存储器（ROM：Read Only Memory）、可擦式可编程只读存储器（EPROM：Erasable Programmable Read Only Memory或闪存）、光纤、便携式紧凑磁盘只读存储器（CD-ROM：Compact Disc-Read Only Memory）、光存储器件、磁存储器件、或者上述的任意合适的组合。在本实施例中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、系统或者器件使用或者与其结合使用。计算机可读存储介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：电线、光缆、RF（Radio Frequency：射频）等等，或者上述的任意合适的组合。
上述计算机可读存储介质可以是空调中所包含的；也可以是单独存在，而未装配入空调中。
上述计算机可读存储介质承载有一个或者多个程序，当上述一个或者多个程序被空调执行时，使得空调：当目标空调开启节能模式时，获取当前环境信息、当前工况信息以及当前设定信息；获取所述目标空调的实时节能功率；将所述当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过所述非节能功率预测模型预测获得实时非节能功率，其中，所述非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；根据所述实时节能功率和所述实时非节能功率，确定实时节能率。
可以以一种或多种程序设计语言或其组合来编写用于执行本公开的操作的计算机程序代码，上述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网（LAN：Local Area Network）或广域网（WAN：Wide Area Network）—连接到用户计算机，或者，可以连接到外部计算机（例如利用因特网服务提供商来通过因特网连接）。
附图中的流程图和框图，图示了按照本申请各种实施例的系统、方法和计算机程序产品的可能实现的体系架构、功能和操作。在这点上，流程图或框图中的每个方框可以代表一个模块、程序段、或代码的一部分，该模块、程序段、或代码的一部分包含一个或多个用于实现规定的逻辑功能的可执行指令。也应当注意，在有些作为替换的实现中，方框中所标注的功能也可以以不同于附图中所标注的顺序发生。例如，两个接连地表示的方框实际上可以基本并行地执行，它们有时也可以按相反的顺序执行，这依所涉及的功能而定。也要注意的是，框图和/或流程图中的每个方框、以及框图和/或流程图中的方框的组合，可以用执行规定的功能或操作的专用的基于硬件的系统来实现，或者可以用专用硬件与计算机指令的组合来实现。
描述于本公开实施例中所涉及到的模块可以通过软件的方式实现，也可以通过硬件的方式来实现。其中，模块的名称在某种情况下并不构成对该单元本身的限定。
本申请提供的可读存储介质为计算机可读存储介质，所述计算机可读存储介质存储有用于执行上述空调节能率预测方法的计算机可读程序指令，能够解决目前空调输出的节能率准确性较低的技术问题。与现有技术相比，本申请实施例提供的计算机可读存储介质的有益效果与上述实施例提供的空调节能率预测方法的有益效果相同，在此不做赘述。
本申请实施例还提供一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现如上述的空调节能率预测方法的步骤。
本申请提供的计算机程序产品能够解决目前空调输出的节能率准确性较低的技术问题。与现有技术相比，本申请实施例提供的计算机程序产品的有益效果与上述实施例提供的空调节能率预测方法的有益效果相同，在此不做赘述。
以上仅为本申请的可选实施例，并非因此限制本申请的专利范围，凡是利用本申请说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本申请的专利处理范围内。

Claims (10)

  1. 一种空调节能率预测方法，其中，所述空调节能率预测方法包括：
    当目标空调开启节能模式时，获取当前环境信息、当前工况信息以及当前设定信息；
    获取所述目标空调的实时节能功率；
    将所述当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过所述非节能功率预测模型预测获得实时非节能功率，其中，所述非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；
    根据所述实时节能功率和所述实时非节能功率，确定实时节能率。
  2. 如权利要求1所述空调节能率预测方法，其中，所述获取所述目标空调的实时节能功率的步骤，包括：
    将所述当前环境信息、当前工况信息以及当前设定信息输入预设的节能功率预测模型，通过所述节能功率预测模型预测获得实时节能功率，其中，所述节能功率预测模型是由空调在节能模式下的运行信息构建得到。
  3. 如权利要求2所述空调节能率预测方法，其中，在所述根据所述实时节能功率和所述实时非节能功率，确定实时节能率的步骤之后，所述方法还包括；
    根据所述实时节能功率、所述实时非节能功率以及预设的预计时长，计算所述预计时长内的节能模式能耗和非节能模式能耗；
    基于所述节能模式能耗和所述非节能模式能耗，计算所述预计时长对应的预计节能量。
  4. 如权利要求2或3所述空调节能率预测方法，其中，在所述将所述当前环境信息、当前工况信息以及当前设定信息输入预设的节能功率预测模型的步骤之前，所述方法还包括：
    采集预设空调处于节能模式下的第一环境信息、第一工况信息以及第一设定信息，获得节能样本数据集，其中，所述工况信息至少包括运行功率；
    根据所述节能样本数据集，构建节能功率预测模型；
    采集所述预设空调处于非节能模式下的第二环境信息、第二工况信息以及第二设定信息，获得非节能样本数据集；
    根据所述非节能样本数据集，构建非节能功率预测模型。
  5. 如权利要求4所述空调节能率预测方法，其中，所述预设空调为所述目标空调、实验空调、与所述目标空调同型号的空调、或与所述目标空调符合预设条件的空调，其中，所述预设条件为处于同一地区、处于同一季节、或处于同一家庭。
  6. 如权利要求1至5中任一项所述空调节能率预测方法，其中，在所述根据所述实时节能功率和所述实时非节能功率，确定实时节能率的步骤之后，所述方法还包括：
    获取所述目标空调在开启节能模式后在各个历史周期内分别对应的实时节能率；
    计算各所述历史周期以及当前周期分别对应的实时节能率的平均值，得到累计节能率。
  7. 如权利要求6所述空调节能率预测方法，其中，在所述计算各所述历史周期以及当前周期分别对应的实时节能率的平均值，得到累计节能率的步骤之后，所述方法还包括：
    在所述目标空调的屏幕上展示所述实时节能率和/或所述累计节能率；和/或
    向与所述目标空调绑定的移动终端推送所述实时节能率和/或所述累计节能率。
  8. 一种空调节能率预测装置，其中，所述空调节能率预测装置包括：
    信息获取模块，用于当目标空调开启节能模式时，获取当前环境信息、当前工况信息以及当前设定信息；
    节能功率获取模块，用于获取所述目标空调的实时节能功率；
    非节能预测模块，用于将所述当前环境信息、当前工况信息以及当前设定信息输入预设的非节能功率预测模型，通过所述非节能功率预测模型预测获得实时非节能功率，其中，所述非节能功率预测模型是由空调在非节能模式下的运行信息构建得到；
    节能率预测模块，用于根据所述实时节能功率和所述实时非节能功率，确定实时节能率。
  9. 一种空调，其中，所述空调包括：
    至少一个处理器；以及，
    与所述至少一个处理器通信连接的存储器；其中，
    所述存储器存储有可被所述至少一个处理器执行的指令，所述指令被所述至少一个处理器执行，以使所述至少一个处理器能够执行如权利要求1至7中任一项所述空调节能率预测方法的步骤。
  10. 一种可读存储介质，其中，所述可读存储介质为计算机可读存储介质，所述计算机可读存储介质上存储有实现空调节能率预测方法的程序，所述实现空调节能率预测方法的程序被处理器执行以实现如权利要求1至7中任一项所述空调节能率预测方法的步骤。
PCT/CN2025/091531 2024-05-30 2025-04-27 空调节能率预测方法、装置、空调及计算机可读存储介质 Pending WO2025246763A1 (zh)

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