Method and apparatus for predicting energy-saving rate of air conditioner, and air conditioner and computer-readable storage medium

Abstract

Disclosed in the present application are a method and apparatus for predicting the energy-saving rate of an air conditioner, and an air conditioner and a computer-readable storage medium. The method for predicting the energy-saving rate of an air conditioner comprises: when a target air conditioner enables an energy-saving mode, acquiring the current environmental information, the current operating condition information and the current setting information; acquiring a real-time energy-saving power of the target air conditioner; inputting the current environmental information, the current operating condition information and the current setting information into a preset non-energy-saving power prediction model, and obtaining a real-time non-energy-saving power by means of the prediction of the non-energy-saving power prediction model, wherein the non-energy-saving power prediction model is constructed by means of operation information of the air conditioner in a non-energy-saving mode; and determining a real-time energy-saving rate on the basis of the real-time energy-saving power and the real-time non-energy-saving power.

Description

Air Conditioning Energy Efficiency Prediction Method, Device, Air Conditioner, and Computer-Readable Storage Medium

[0001] This application claims priority to Chinese patent application No. 202410692564.X, filed on May 30, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0002] 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. Background Technology

[0003] The application of energy-saving functions in air conditioners is becoming increasingly widespread, achieving certain energy savings. However, the energy efficiency calculation for air conditioners typically compares energy consumption before and after activating energy-saving mode. Since only energy consumption in energy-saving mode is collected when the user activates the energy-saving function, and not in non-energy-saving mode, existing energy efficiency calculation methods cannot obtain real-time energy efficiency through retrospective testing. The energy consumption values ​​used to calculate the energy efficiency rate do not belong to the same point in time, leading to inaccuracies in the calculated rate. Technical issues

[0004] 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. Technical solutions

[0005] To achieve the above objectives, this application provides a method for predicting air conditioning energy efficiency, the method comprising:

[0006] Obtain the real-time energy-saving power of the target air conditioner;

[0007] 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.

[0008] The real-time energy saving rate is determined based on the real-time energy saving power and the real-time non-energy saving power.

[0009] In some embodiments, the step of obtaining the real-time energy-saving power of the target air conditioner includes:

[0010] 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.

[0011] In some embodiments, after the step of determining the real-time energy saving rate based on the real-time energy saving power and the real-time non-energy saving power, the method further includes;

[0012] 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.

[0013] Based on the energy consumption of the energy-saving mode and the energy consumption of the non-energy-saving mode, the expected energy saving corresponding to the expected duration is calculated.

[0014] In some embodiments, 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:

[0015] 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;

[0016] Based on the energy-saving sample dataset, an energy-saving power prediction model is constructed;

[0017] Collect 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;

[0018] Based on the non-energy-saving sample dataset, a non-energy-saving power prediction model is constructed.

[0019] In some embodiments, 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.

[0020] In some embodiments, after the step of determining the real-time energy saving rate based on the real-time energy-saving power and the real-time non-energy-saving power, the method further includes:

[0021] Obtain the real-time energy saving rate of the target air conditioner in each historical period after the energy saving mode is turned on;

[0022] Calculate the average of the real-time energy saving rates corresponding to each of the historical periods and the current period to obtain the cumulative energy saving rate.

[0023] In some embodiments, after the step of calculating the average of the real-time energy-saving rates corresponding to each of the historical periods and the current period to obtain the cumulative energy-saving rate, the method further includes:

[0024] 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

[0025] 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.

[0026] Furthermore, to achieve the above objectives, this application also provides an air conditioning energy-saving rate prediction device, the device comprising:

[0027] 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.

[0028] An energy-saving power acquisition module is used to acquire the real-time energy-saving power of the target air conditioner;

[0029] 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.

[0030] 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.

[0031] In addition, to achieve the above objectives, 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.

[0032] In addition, to achieve the above objectives, this application also provides a readable storage medium, which is a computer-readable storage medium, storing a program for implementing an 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.

[0033] In addition, to achieve the above objectives, 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. Attached Figure Description

[0034] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 is a flowchart illustrating an embodiment of the air conditioning energy saving rate prediction method in this application.

[0037] 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;

[0038] 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;

[0039] 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;

[0040] Figure 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.

[0041] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Embodiments of the present invention

[0042] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0044] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0045] 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. The following description uses an air conditioner controller as an example to illustrate this embodiment and the subsequent embodiments.

[0046] Existing air conditioner energy-saving functions struggle to demonstrate real-time energy-saving effects to users, and the cumulative energy-saving results for a single operation are inaccurate. Furthermore, users lack a clear understanding of energy efficiency during air conditioner use. Additionally, when a user activates the energy-saving function, only the energy consumption value under the current energy-saving state is available, not the energy consumption value under non-energy-saving states. Therefore, it is impossible to determine the real-time energy-saving rate through retrospective analysis.

[0047] To overcome the technical problems and defects existing in the prior art, this application provides an air conditioning energy saving rate prediction method. Referring to Figure 1, which is a flowchart of an embodiment of the air conditioning energy saving rate prediction method of this application, the air conditioning energy saving rate prediction method includes:

[0048] 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;

[0049] In this embodiment, the target air conditioner is one whose energy-saving rate needs to be calculated. 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).

[0050] Step S20: Obtain the real-time energy-saving power of the target air conditioner;

[0051] 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.

[0052] 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.

[0053] 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.

[0054] To overcome the shortcomings of traditional energy-saving rate calculation methods where energy-saving power and non-energy-saving rate data originate from different time points, 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. Since 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.

[0055] For example, 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.

[0056] In some embodiments, the step of obtaining the real-time energy-saving power of the target air conditioner may include:

[0057] 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.

[0058] It is understandable that, in addition to determining the real-time energy-saving power of the air conditioner by directly collecting the real-time operating power of the air conditioner, 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.

[0059] In some embodiments, after the step of determining the real-time energy saving rate based on the real-time energy saving power and the real-time non-energy saving power, the method further includes;

[0060] 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.

[0061] 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.

[0062] With a pre-built energy-saving power prediction model, 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.

[0063] For example, if the air conditioner continues to maintain the current environmental, operating, and setting information for a period of time in the future, it 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. Finally, 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.

[0064] Furthermore, after the step of determining the real-time energy saving rate based on the real-time energy-saving power and the real-time non-energy-saving power, the method further includes:

[0065] 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;

[0066] 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.

[0067] It should be noted that in this embodiment of the application, steps S10 and S20 are executed according to a preset cycle to determine the real-time energy saving rate corresponding to the current cycle. After obtaining the real-time energy saving rate, in order to further reflect the overall energy saving rate of the air conditioner since the energy saving mode has been turned on, 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.

[0068] For example, in conjunction with the content of the foregoing embodiments, in this embodiment of the application, environmental information, operating condition information, and user setting information for cycle i are first collected; then, real-time energy-saving power and real-time non-energy-saving power are obtained, and the real-time energy-saving rate S(i)=u(i)=f(i) / g(i) is determined, where i is the current cycle number, S(i) is the real-time energy-saving rate, u(i) refers to the real-time energy-saving rate output by the energy-saving rate prediction model u, f(i) is the real-time energy-saving power, and g(i) is the real-time non-energy-saving power. The energy-saving rate prediction model u is composed of the real-time energy-saving power prediction model, f, and the real-time non-energy-saving prediction model g (represented as u=f / g). Finally, within the current energy-saving function operation cycle, the average value of the real-time energy-saving rate corresponding to each operation cycle is calculated as the cumulative energy-saving rate Savg=(∑S(i)) / i.

[0069] For example, referring to FIG2 and in conjunction with the content of the foregoing embodiments, 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 formula for calculating the energy saving rate of the energy saving rate prediction model is expressed as u=f / g, where u is the energy saving rate, f is the energy saving power, and g is the non-energy saving power. Finally, 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.

[0070] In some embodiments, after the step of calculating the average of the real-time energy-saving rates corresponding to each of the historical periods and the current period to obtain the cumulative energy-saving rate, the method further includes:

[0071] 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,

[0072] 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.

[0073] To more intuitively and clearly demonstrate the current real-time and cumulative energy-saving effects of an air conditioner to users, on the one hand, a display screen installed on the air conditioner casing can show the values ​​of the real-time and cumulative energy-saving rates. On the other hand, 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.

[0074] To more intuitively understand the entire process of the air conditioner energy saving rate prediction method provided in this application embodiment, as an example, Figure 3 shows the specific execution steps of a feasible air conditioner energy saving rate prediction method: First, the air conditioner is turned on and the energy saving function is activated. Every T time interval (e.g., 30s), the following logic is executed: collect the current environmental information, operating condition information, and user setting information, and then call the energy saving rate prediction model u=f / g, where the energy saving rate prediction model u is composed of the real-time energy saving power prediction model f and the real-time non-energy saving power g, to obtain the real-time energy saving rate S(i)=u(i)=f(i) / g(i). Then, the average value of the real-time energy saving rate of all cycles of this energy saving operation is calculated as the cumulative energy saving rate Savg=(∑S(i)) / i.

[0075] In some embodiments, 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 may further include:

[0076] 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;

[0077] Step A20: Construct an energy-saving power prediction model based on the energy-saving sample dataset;

[0078] 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;

[0079] Step A40: Construct a non-energy-saving power prediction model based on the non-energy-saving sample dataset.

[0080] This application embodiment also provides a method for pre-constructing an energy saving rate prediction model. It should be noted that steps A10 to A40 include methods for constructing an energy saving power prediction model and a non-energy saving power prediction model, respectively. In practical application scenarios, if 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.

[0081] Specifically, during the process of collecting 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, the activation of the air conditioner's energy-saving mode is used as the data filtering condition. Environmental information, operating condition information, and user settings are used as independent variables, and power is used as the dependent variable to construct an energy-saving power prediction model with the expression "f(environmental information, operating condition information, user settings) = power". It can be understood that 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.

[0082] Specifically, 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. Using the 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, a non-energy-saving power prediction model is constructed: "g(environmental information, operating condition information, user settings) = power". It can be understood that the non-energy-saving power prediction model learns the correlation and inherent patterns between the independent and dependent variables in the non-energy-saving sample dataset during training. Therefore, using this model, when environmental information, operating condition information, and user settings are input, real-time non-energy-saving power can be predicted.

[0083] In some embodiments, 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.

[0084] 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.

[0085] Specifically, when the target air conditioner is preset, 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.

[0086] When 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.

[0087] When the preset air conditioner meets the same preset conditions as the target air conditioner, energy-saving rate prediction models are typically developed for specific scenarios. For example, by collecting historical data from a large number of air conditioners in a specific region, the resulting energy-saving rate prediction model has better prediction accuracy for that specific region and can better learn the inherent laws 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. While generalized energy-saving rate prediction models are built by collecting historical operating data of air conditioners in a laboratory setting, their advantage is that the original data collection is more accurate and the applicable range is wider, but the data volume is smaller. In actual model training, appropriate preset air conditioner samples can be selected according to actual needs to obtain a targeted energy-saving rate prediction model with high prediction accuracy.

[0088] 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. When the user activates the air conditioner's energy-saving function, the real-time and cumulative energy saving rates can be displayed intuitively, making it more intuitive and intelligent.

[0089] This application embodiment also provides an air conditioning energy saving rate prediction device. Referring to FIG4, the air conditioning energy saving rate prediction device includes:

[0090] 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.

[0091] Energy-saving power acquisition module 20 is used to acquire the real-time energy-saving power of the target air conditioner;

[0092] 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.

[0093] 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.

[0094] In some embodiments, the energy-saving power acquisition module 20 is further configured to:

[0095] 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.

[0096] In some embodiments, the air conditioning energy efficiency prediction device further includes an energy saving prediction module, the energy saving prediction module being used for:

[0097] 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.

[0098] Based on the energy consumption of the energy-saving mode and the energy consumption of the non-energy-saving mode, the expected energy saving corresponding to the expected duration is calculated.

[0099] In some embodiments, the air conditioning energy saving rate prediction device further includes a model building module, the model building module being used for:

[0100] 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;

[0101] Based on the energy-saving sample dataset, an energy-saving power prediction model is constructed;

[0102] Collect 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;

[0103] Based on the non-energy-saving sample dataset, a non-energy-saving power prediction model is constructed.

[0104] In some embodiments, 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.

[0105] In some embodiments, the air conditioning energy saving rate prediction device further includes a cumulative energy saving module, the cumulative energy saving module being used for:

[0106] Obtain the real-time energy saving rate of the target air conditioner in each historical period after the energy saving mode is turned on;

[0107] Calculate the average of the real-time energy saving rates corresponding to each of the historical periods and the current period to obtain the cumulative energy saving rate.

[0108] In some embodiments, the air conditioning energy saving rate prediction device further includes a display module, the display module being used for:

[0109] The real-time energy saving rate and / or the cumulative energy saving rate are displayed on the screen of the target air conditioner;

[0110] And / or, push the real-time energy saving rate and / or the cumulative energy saving rate to the mobile terminal bound to the target air conditioner.

[0111] 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. Compared with the prior art, 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.

[0112] 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.

[0113] Referring now to 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.

[0114] As shown in Figure 5, 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. Typically, 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. Although 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.

[0115] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, 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. In such embodiments, 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. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of embodiments of this disclosure.

[0116] 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. Compared with the prior art, 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.

[0117] It should be understood that various parts of this disclosure can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0118] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0119] 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.

[0120] 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. In this embodiment, 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.

[0121] 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.

[0122] The aforementioned computer-readable storage medium carries one or more programs. When 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.

[0123] 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. In cases involving remote computers, 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).

[0124] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, 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. It should also be noted that in some alternative implementations, 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. It should also be noted that 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.

[0125] 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.

[0126] 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. Compared with the prior art, 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.

[0127] 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.

[0128] 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.

[0129] The above are merely optional embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent scope of this application.

Claims

1. A method for predicting air conditioning energy efficiency, wherein, The air conditioning energy efficiency prediction method includes: When the target air conditioner turns on the energy-saving mode, obtain the current environmental information, current operating condition information, and current setting information; Obtain the real-time energy-saving power of the target air conditioner; 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.

2. The air conditioning energy saving rate prediction method as described in claim 1, wherein, 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 then predicted by the energy-saving power prediction model, which is constructed from the operating information of the air conditioner in energy-saving mode.

3. The air conditioning energy saving rate prediction method as described in claim 2, wherein, After the step of determining the real-time energy saving rate based on the real-time energy saving power and the real-time non-energy saving power, the method further includes: 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. Based on the energy consumption of the energy-saving mode and the energy consumption of the non-energy-saving mode, the expected energy saving corresponding to the expected duration is calculated.

4. The air conditioning energy saving rate prediction method as described in claim 2 or 3, wherein, Before the step of inputting the current environmental information, current operating condition information, and current setting information into the preset energy-saving power prediction model, the method further includes: 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; Based on the energy-saving sample dataset, an energy-saving power prediction model is constructed; Collect 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; Based on the non-energy-saving sample dataset, a non-energy-saving power prediction model is constructed.

5. The air conditioning energy saving rate prediction method as described in claim 4, wherein, 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 the target air conditioner, wherein the preset conditions are being in the same region, in the same season, or in the same household.

6. The air conditioning energy saving rate prediction method according to any one of claims 1 to 5, wherein, After the step of determining the real-time energy saving rate based on the real-time energy-saving power and the real-time non-energy-saving power, the method further includes: Obtain the real-time energy saving rate of the target air conditioner in each historical period after the energy saving mode is turned on; Calculate the average of the real-time energy saving rates corresponding to each of the historical periods and the current period to obtain the cumulative energy saving rate.

7. The air conditioning energy saving rate prediction method as described in claim 6, wherein, After the step of calculating the average of the real-time energy saving rates corresponding to each of the historical periods and the current period to obtain the cumulative energy saving rate, 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.

8. An air conditioning energy saving rate prediction device, wherein, The air conditioning energy saving rate prediction device includes: 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.

9. An air conditioner, wherein, The air conditioner includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the steps of the air conditioning energy saving rate prediction method as described in any one of claims 1 to 7.

10. A readable storage medium, wherein, The readable storage medium is a computer-readable storage medium, and the computer-readable storage medium 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 in any one of claims 1 to 7.

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