Method and device for predicting energy consumption of air conditioner in automobile
By obtaining the target sample operation data and navigation information of the automobile air conditioner, it is judged whether the air conditioner will reach the temperature equilibrium point and the energy consumption prediction model is determined. This solves the problems of high computational cost and complex model in the existing methods, and realizes flexible and convenient energy consumption. consumption forecast.
Patent Information
- Application Number
- CN202411387191.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-30
- Publication Date
- 2025-01-21
AI Technical Summary
The existing automotive air conditioner energy consumption prediction method requires a large amount of real-time environmental data and vehicle status data, which is costly and complex, making it difficult to effectively apply in scenarios where real-time prediction is required.
By obtaining the operating data of the target sample, including the operating data corresponding to the temperature balance point, combined with the navigation information, it is judged whether the air conditioner will reach the temperature balance point, and the energy consumption prediction mode is determined based on the judgment results to perform energy consumption prediction.
It can reduce system implementation and operating costs without relying on expensive network infrastructure, improve the flexibility and convenience of air-conditioning energy consumption prediction, and is suitable for scenarios with limited data transmission conditions or cost-sensitive scenarios.
Smart Images
Figure CN119337091A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of automobile technology, and in particular to a method and device for predicting energy consumption of an air conditioner in a car. Background Art
[0002] At present, the task of predicting the consumption of automobile accessories mainly includes methods based on physical models: such methods are usually based on a deep understanding and mathematical modeling of the air-conditioning system, and consider the impact of multiple factors on air-conditioning energy consumption, such as ambient temperature, humidity, solar radiation, vehicle speed, and number of passengers. That is, by building a complex thermodynamic model to predict the energy consumption of air-conditioning under specific conditions.
[0003] Methods based on physical models often require a large amount of detailed real-time environmental data and vehicle status data, including but not limited to external temperature, humidity, vehicle speed, number of passengers, window opening and closing status, etc. In practical applications, the advantage of this method is that it has a solid theoretical foundation, but the disadvantage is that the model is complex, requires a large amount of real-time data input, and has a high computational cost. Especially in scenarios where real-time predictions are required, collecting and processing this data can be very complex and costly, and in order to accurately reflect various factors in the real world, physical models are usually quite complex, which not only increases the difficulty of model construction and maintenance, but can also lead to excessive consumption of computing resources. Summary of the invention
[0004] In view of the above problems, a method and device for predicting energy consumption of air conditioner in a car is proposed to overcome the above problems or at least partially solve the above problems, including:
[0005] A method for predicting energy consumption of an air conditioner in a car, the method comprising:
[0006] In response to controlling the air conditioner in the car, target sample operation data is acquired; wherein the target sample operation data includes operation data corresponding to the temperature balance point;
[0007] Determine the required operating time of the air conditioner according to the navigation information of the current trip, and judge whether the air conditioner will reach a temperature equilibrium point in the current trip according to the operating time and the target sample operating data;
[0008] According to the judgment result, a target energy consumption prediction mode is determined, and energy consumption of the air conditioner is predicted according to the target energy consumption prediction mode.
[0009] Optionally, before acquiring the target sample operation data in response to controlling the air conditioner in the car, the method further includes:
[0010] Acquire a plurality of historical control parameter groups, and obtain a plurality of time series curves of temperature changes according to the plurality of historical control parameter groups;
[0011] Performing time series filtering on each of the time series curves, and finding the temperature equilibrium point of each of the time series curves;
[0012] According to each of the temperature balance points, sample operation data corresponding to each of the historical control parameter groups is obtained, and a mapping relationship is established;
[0013] The plurality of historical control parameter groups and the plurality of sample operation data are preset and saved according to the mapping relationship.
[0014] Optionally, the acquiring multiple historical control parameter groups includes:
[0015] Acquire a historical control parameter set, and obtain multiple candidate historical control parameter groups according to the start / stop status of the air conditioner in the historical control parameter set;
[0016] The plurality of candidate historical control parameter groups are subjected to denoising processing to obtain a plurality of the historical control parameter groups.
[0017] Optionally, finding the temperature equilibrium point of each of the timing curves includes:
[0018] According to the target temperature in the historical control parameter group, a maximum temperature threshold and a minimum temperature threshold are set to obtain a temperature threshold window; wherein the maximum temperature threshold is a value greater than the target temperature, and the minimum temperature threshold is a value less than the target temperature;
[0019] Obtaining a candidate temperature balance point according to the temperature threshold window and the timing curve;
[0020] A stationarity test is performed on the candidate temperature balance point to obtain the temperature balance point.
[0021] Optionally, the sample operation data includes energy consumption required for the temperature balance point, power after the temperature balance point, and time required for the temperature balance point;
[0022] Among them, the energy consumption required for the temperature balance point is the energy consumption required for the air conditioner to run to the temperature balance point, the time required for the temperature balance point is the time required for the air conditioner to run to the temperature balance point, and the power after the temperature balance point is the average power of the air conditioner running to the temperature balance point.
[0023] Optionally, obtaining sample operation data corresponding to each of the historical control parameter groups according to each of the temperature balance points and establishing a mapping relationship includes:
[0024] According to the temperature balance point, the energy consumption required for the temperature balance point, the time required for the temperature balance point, and the power after the temperature balance point are obtained;
[0025] A mapping relationship between the historical control parameter group and the energy consumption required for the temperature balance point, the time required for the temperature balance point, and the power after the temperature balance point is established.
[0026] Optionally, the obtaining of target sample operation data in response to the control of the air conditioner in the car includes:
[0027] In response to controlling the air conditioner in the automobile, acquiring real-time control parameters of the air conditioner;
[0028] Performing similarity measurement between the real-time control parameter and a plurality of preset historical control parameter groups, and determining a target historical control parameter group according to the similarity measurement result;
[0029] Determine the target sample operation data corresponding to the target historical control parameter group.
[0030] Optionally, determining the target energy consumption prediction mode according to the judgment result includes:
[0031] The air conditioner has not reached the temperature equilibrium point, and energy consumption is predicted according to the energy consumption required for the temperature equilibrium point, the operating time, and the time required for the temperature equilibrium point;
[0032] The air conditioner has reached the temperature balance point, and energy consumption is predicted based on the energy consumption required for the temperature balance point, the power after the temperature balance point, the operating time, and the time required for the temperature balance point.
[0033] An energy consumption prediction device for an air conditioner in a car, the device comprising:
[0034] A target sample operation data acquisition module is used to acquire target sample operation data in response to the control of the air conditioner in the car; wherein the target sample operation data includes operation data corresponding to the temperature balance point;
[0035] a temperature balance point judgment module, for determining the required operating time of the air conditioner according to the navigation information of the current trip, and judging whether the air conditioner will reach the temperature balance point in the current trip according to the operating time and the target sample operating data;
[0036] The energy consumption prediction module is used to determine a target energy consumption prediction mode according to the judgment result, and perform energy consumption prediction on the air conditioner according to the target energy consumption prediction mode.
[0037] An electronic device comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the above-mentioned method for predicting energy consumption of air conditioning in a car.
[0038] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting energy consumption of an air conditioner in a car as described above is implemented.
[0039] The embodiments of the present invention have the following advantages:
[0040] In an embodiment of the present invention, target sample operating data is obtained in response to the control of an air conditioner in a car, wherein the target sample operating data includes operating data corresponding to a temperature balance point; based on navigation information of a current trip, the operating time required for the air conditioner is determined, and based on the operating time and the target sample operating data, it is judged whether the air conditioner will reach a temperature balance point in the current trip; based on the judgment result, a target energy consumption prediction mode is determined, and energy consumption of the air conditioner is predicted according to the target energy consumption prediction mode, thereby achieving cost savings, avoiding dependence on expensive network infrastructure, reducing the cost of system implementation and operation, and making the energy consumption prediction process of the air conditioner in the car more flexible and convenient, which is particularly suitable for scenarios where network conditions for data transmission are limited or cost-sensitive. BRIEF DESCRIPTION OF THE DRAWINGS
[0041] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.
[0042] Figure 1 is a flowchart of the steps of a method for predicting energy consumption of an air conditioner in a car provided by some embodiments of the present invention;
[0043] Figure 2 is a schematic diagram of a timing curve of a historical control parameter group provided by some embodiments of the present invention;
[0044] Figure 3 is a schematic diagram of the smoothing effect of a timing curve provided by some embodiments of the present invention;
[0045] Figure 4 It is a structural block diagram of an energy consumption prediction device for an air conditioner in a car provided by some embodiments of the present invention. DETAILED DESCRIPTION
[0046] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0047] Reference Figure 1 , shows a flowchart of the steps of a method for predicting energy consumption of an air conditioner in a car provided by some embodiments of the present invention, which may specifically include the following steps:
[0048] Step 101, in response to controlling the air conditioner in the car, obtaining target sample operation data; wherein the target sample operation data includes operation data corresponding to the temperature balance point;
[0049] While the user is driving the car, the user can control the air conditioner in the car. In response to the user's control operation of the air conditioner, target sample operation data can be obtained. The sample operation data can include air conditioning operation data corresponding to the temperature balance point. During the actual operation of the air conditioner in the car, the actual temperature will gradually stabilize. Therefore, the temperature balance point can be the first data point when the temperature gradually stabilizes during the operation of the air conditioner.
[0050] In one embodiment of the present invention, before acquiring the target sample operation data in response to controlling the air conditioner in the automobile, the method further includes:
[0051] Sub-step 11, obtaining a plurality of historical control parameter groups, and obtaining a plurality of time series curves of temperature changes according to the plurality of historical control parameter groups.
[0052] Before a user drives a car to control the air conditioner in the car and obtains target sample operation data, multiple historical control parameter groups can be obtained, so that multiple temperature change timing curves can be obtained based on the multiple historical control parameter groups.
[0053] Specifically, this stage can be called the offline processing stage. In this stage, multiple historical control parameter groups can be obtained, each of which may include historical control parameters in dimensions such as air conditioning cooling status, CCU (Control Center Unit) target temperature, CCU actual temperature, and CCU actual power. After obtaining multiple historical control parameter groups, a timing curve schematic diagram of multiple historical control parameters in the historical control parameter group can be obtained based on the multiple historical control parameter groups, so that multiple timing curves of temperature changes can be obtained from the multiple timing curve schematic diagrams.
[0054] In one example, a corresponding timing curve diagram can be obtained based on each historical control parameter group, and the timing curve diagram can include timing curve diagrams of multiple historical control parameters in the historical control parameter group. Then, a timing curve diagram of temperature change can be obtained from the timing curve diagrams of multiple historical control parameters, wherein the timing curve diagram of temperature change can be a timing curve diagram of actual temperature change.
[0055] For example, see Attachment Figure 2 , is a timing curve diagram of a historical control parameter group provided by some embodiments of the invention, in which the timing curve diagrams of target temperature, actual temperature and actual power versus time are shown, wherein curve 1 is the timing curve diagram of target temperature, curve 2 is the timing curve diagram of actual temperature, and curve 3 is the timing curve diagram of actual power.
[0056] At the same time, it can be seen from curve 2 and curve 3 that the actual power changes dramatically with the change of the actual temperature in the early stage, and after the actual temperature tends to be balanced, the actual power also becomes stable.
[0057] Sub-step 12: performing time series filtering on each of the time series curves, and finding the temperature equilibrium point of each of the time series curves.
[0058] After obtaining a plurality of time series curves of temperature changes, time series filtering may be performed on the time series curves of temperature changes, and then the temperature balance point corresponding to each historical control parameter group may be found through the time series curves after time series filtering.
[0059] In practical applications, the actual temperature change curve can be smoothed in time series, and LOWESS (Locally Weighted Scatterplot Smoothing, a non-parametric smoothing technique) filtering is used to preprocess the CCU energy consumption data. By smoothing the data, short-term fluctuations caused by environmental changes, measurement errors and other factors can be eliminated, making subsequent energy consumption analysis more accurate and reliable. Especially in the process of finding temperature balance points and energy consumption forecasting, smoothed data can provide clearer signals and help more accurately locate the transition points of energy consumption patterns, thereby optimizing the accuracy of energy consumption forecasting. LOWESS does not require prior assumptions about the distribution form of the data, and can effectively remove short-term fluctuations and outliers in time series data, revealing the basic trend of the data, which is crucial for energy consumption forecasting.
[0060] As an example, see the attached Figure 3 , a schematic diagram of the smoothing effect of a timing curve provided by some embodiments of the present invention shows a schematic diagram of the smoothing effect after timing filtering is performed on the timing curve of temperature change.
[0061] Sub-step 13, according to each of the temperature balance points, obtaining the sample operation data corresponding to each of the historical control parameter groups, and establishing a mapping relationship.
[0062] After the temperature balance point is found, the sample operation data corresponding to the historical control parameter group can be obtained according to the temperature balance point, and a mapping relationship between the historical control parameter group and the corresponding sample operation data can be established.
[0063] In practical applications, the temperature balance point is the key point for identifying the transition of air conditioning energy consumption mode in the car. Figure 2 From curve 2 (actual temperature time series curve) and curve 3 (actual power time series curve) in Figure 1, it can be seen that before the actual temperature of the air conditioner in the car reaches the temperature balance point, the actual power changes dramatically with the change of the actual temperature. After the actual temperature reaches the temperature balance point, the actual power also becomes stable. Therefore, the temperature balance point is crucial to distinguish the dynamic change stage and the relatively stable stage of energy consumption. The temperature balance point can be used as a marker to obtain the air conditioner operation data related to the temperature balance point, that is, the sample operation data.
[0064] Sub-step 14, presetting and saving the plurality of the historical control parameter groups and the plurality of the sample operation data according to the mapping relationship.
[0065] According to the mapping relationship, multiple historical control parameter groups and multiple sample operation data are preset and saved. For example, they can be preset and saved in the data file in the form of a MAP (Map Table).
[0066] In some embodiments of the present invention, the acquiring of multiple historical control parameter groups includes:
[0067] Sub-step 21, obtaining a historical control parameter set, and obtaining a plurality of candidate historical control parameter groups according to the start / stop status of the air conditioner in the historical control parameter set.
[0068] A historical control parameter set is obtained, and multiple candidate historical control parameter groups can be obtained according to the start and stop states of the air conditioner in the historical control parameter set.
[0069] Specifically, the historical control parameter group may include historical control parameters such as air conditioning cooling state, CCU target temperature, CCU actual temperature, CCU actual power, etc., collect a large amount of data containing historical control parameters, namely, historical control parameter set, and can be divided into multiple data segments, namely, multiple candidate historical control parameter groups, according to the start and stop state changes of the air conditioning CCU. For example, a CCU start and stop can be used as a data segment, and each data segment represents a switch of the CCU working mode.
[0070] In practical applications, in order to ensure the adequacy of data analysis and considering the impact of region and temperature on cooling power, CAN (Controller Area Network) signal data may be required to meet the sampling requirements of multiple vehicles in different cities and months.
[0071] Sub-step 22, performing denoising processing on the plurality of candidate historical control parameter groups to obtain a plurality of the historical control parameter groups.
[0072] The multiple candidate historical control parameter groups may be subjected to denoising processing to remove some noise data, thereby obtaining multiple historical control parameter groups.
[0073] Specifically, these noise data can mainly include the following:
[0074] 1) There are a large number of missing values in the time dimension in a data segment;
[0075] 2) The duration of a kinematic segment is less than 5 minutes (to ensure that the actual temperature in the same segment has reached the temperature equilibrium point);
[0076] 3) The interval between two adjacent segments of the same vehicle is less than 30 minutes (to ensure that different data segments do not affect each other).
[0077] In some embodiments of the present invention, finding the temperature equilibrium point of each of the timing curves includes:
[0078] Sub-step 31, according to the target temperature in the historical control parameter group, set the maximum temperature threshold and the minimum temperature threshold to obtain a temperature threshold window; wherein the maximum temperature threshold is a value greater than the target temperature, and the minimum temperature threshold is a value less than the target temperature.
[0079] When the actual temperature reaches equilibrium, it may be lower than the target temperature or higher than the target temperature. Therefore, the maximum temperature threshold and the minimum temperature threshold can be set according to the target temperature in the historical control parameter group, and the maximum temperature threshold and the minimum temperature threshold are used as the interval as the temperature threshold window. The maximum temperature threshold is a value greater than the target temperature, and the minimum temperature threshold is a value less than the target temperature.
[0080] Specifically, the minimum temperature threshold can be set to T min and the maximum temperature threshold T max , these two thresholds are determined based on the understanding of the characteristics of time series.
[0081] In practical applications, the target temperature can generally be set 4 degrees above and below as the interval between the maximum temperature threshold and the minimum temperature threshold, that is, the temperature threshold window.
[0082] Sub-step 32, obtaining a candidate temperature balance point according to the temperature threshold window and the timing curve.
[0083] After obtaining the temperature threshold window, you can use the temperature threshold window to traverse forward from the end of the time series curve of temperature change after time series filtering, and for each point, check whether it is within the temperature threshold window. If the value of a point exceeds the maximum temperature threshold or the minimum temperature threshold, then the point may mark a significant change in the behavior of the sequence, that is, a potential temperature equilibrium point. Once a point is found that meets the threshold condition, it is marked as a candidate temperature equilibrium point.
[0084] Sub-step 33: performing a stationarity test on the candidate temperature equilibrium point to obtain the temperature equilibrium point.
[0085] Specifically, after identifying the candidate temperature equilibrium point, the ADF (Augmented Dickey-Fuller Test) test can be used to further verify the stationarity of the time series curve near this position. Among them, the ADF test is a commonly used statistical test for determining whether a time series has a unit root, that is, whether the sequence is stationary. In the present invention, the time series curve can be divided into two subsequences with the candidate temperature equilibrium point as the boundary, and the subsequence on the right can be subjected to an ADF test. If the p-value of the ADF test is less than the set significance level (for example, 0.05), the null hypothesis is rejected, and it is believed that the subsequence of the sequence near the equilibrium point is stationary. On the contrary, if the p-value is greater than the significance level, the null hypothesis is accepted, indicating that the subsequence may have a unit root, that is, non-stationary. Then, the temperature equilibrium point is obtained.
[0086] In some embodiments of the present invention, the sample operation data includes energy consumption required for the temperature balance point, power after the temperature balance point, and time required for the temperature balance point;
[0087] Among them, the energy consumption required for the temperature balance point is the energy consumption required for the air conditioner to run to the temperature balance point, the time required for the temperature balance point is the time required for the air conditioner to run to the temperature balance point, and the power after the temperature balance point is the average power of the air conditioner running to the temperature balance point.
[0088] Specifically, the operating data corresponding to the temperature balance point, that is, the sample operating data, may include the energy consumption required for the temperature balance point, the power after the temperature balance point, and the time required for the temperature balance point, wherein the energy consumption required for the temperature balance point may be the energy consumption required for the air conditioner to run to the temperature balance point, the time required for the temperature balance point may be the time required for the air conditioner to run to the temperature balance point, and the power after the temperature balance point may be the average power of the air conditioner when it runs to the temperature balance point.
[0089] In some embodiments of the present invention, obtaining sample operation data corresponding to each of the historical control parameter groups according to each of the temperature balance points and establishing a mapping relationship includes:
[0090] Sub-step 41, according to the temperature balance point, obtaining the energy consumption required for the temperature balance point, the time required for the temperature balance point and the power after the temperature balance point;
[0091] After obtaining the temperature balance point, calculations can be performed based on the temperature balance point to obtain the energy consumption required for the temperature balance point, the time required for the temperature balance point, and the power after the temperature balance point.
[0092] Specifically, after obtaining the temperature balance point, the time required for the temperature balance point and the power after the temperature balance point can be obtained through the time series curve of temperature change. The energy consumption required for the temperature balance point can be calculated using the power integration method to obtain the energy consumption required for the temperature balance point.
[0093] In addition, the calculation of energy consumption after the temperature equilibrium point can be simplified to multiply the power after the temperature equilibrium point by the remaining time after the temperature equilibrium point, thereby simplifying the calculation.
[0094] Sub-step 42, establishing a mapping relationship between the historical control parameter group and the energy consumption required for the temperature balance point, the time required for the temperature balance point, and the power after the temperature balance point.
[0095] A mapping relationship between the historical control parameter group and the energy consumption required for the corresponding temperature balance point, the time required for the temperature balance point and the power after the temperature balance point can be established, that is, a mapping relationship between the historical control parameter group and the corresponding sample operation data can be established.
[0096] In some embodiments of the present invention, the step of obtaining target sample operation data in response to controlling the air conditioner in the automobile includes:
[0097] Sub-step 51, in response to controlling the air conditioner in the automobile, obtaining real-time control parameters of the air conditioner;
[0098] While the user is driving the car, the user can control the air conditioner in the car. In response to the user's control operations on the air conditioner, real-time control parameters of the air conditioner can be obtained. The real-time control parameters may include real-time CCU target temperature, CCU actual temperature, CCU initial actual power and other parameters.
[0099] Sub-step 52, performing similarity measurement between the real-time control parameter and a plurality of preset historical control parameter groups, and determining a target historical control parameter group according to the similarity measurement result;
[0100] Specifically, multiple historical control parameters can be obtained from the aforementioned preset data file stored according to the mapping relationship, and similarity measurement can be performed between the real-time control parameters and the preset multiple historical control parameter groups, wherein the similarity measurement can be performed using the Euclidean distance, so that the historical control parameter group most similar to the current conditions can be retrieved from the preset data file stored according to the mapping relationship, that is, the target historical control parameter group.
[0101] Sub-step 53, determining the target sample operation data corresponding to the target historical control parameter group.
[0102] After obtaining the target historical control parameter group, the target sample operation data can be obtained according to the mapping relationship in the data file stored according to the preset mapping relationship. The target sample operation data may include the energy consumption required for the temperature balance point, the power after the temperature balance point, and the time required for the temperature balance point.
[0103] Step 102, determining the required operating time of the air conditioner according to the navigation information of the current trip, and judging whether the air conditioner will reach a temperature equilibrium point in the current trip according to the operating time and the target sample operating data;
[0104] When the user is driving the car, the navigation information can be used to determine the time from when the air conditioner in the car is turned on to when the car arrives at the destination, that is, the required operating time of the air conditioner. Therefore, based on the required operating time of the air conditioner and the target sample operating data, it can be determined whether the air conditioner in the car can reach the temperature equilibrium point when the car arrives at the destination.
[0105] In one example, the target sample operation data may include the time required for the temperature balance point, that is, the time required for the temperature balance point can be obtained through the target sample operation data and compared with the operation time required by the air conditioner to determine whether the air conditioner in the car can reach the temperature balance point when the car arrives at the destination.
[0106] If the time required for the temperature balance point is greater than the operating time required for the air conditioner, the temperature balance point cannot be reached; if the time required for the temperature balance point is less than the operating time required for the air conditioner, the temperature balance point can be reached.
[0107] Step 103: determine a target energy consumption prediction mode according to the judgment result, and perform energy consumption prediction on the air conditioner according to the target energy consumption prediction mode.
[0108] After obtaining the judgment result of whether the temperature equilibrium point can be reached, the target energy consumption prediction mode can be determined, so that the energy consumption of the air conditioner can be predicted according to the target energy consumption prediction mode.
[0109] In some embodiments of the present invention, determining the target energy consumption prediction mode according to the judgment result includes:
[0110] The air conditioner has not reached the temperature equilibrium point, and energy consumption is predicted according to the energy consumption required for the temperature equilibrium point, the operating time, and the time required for the temperature equilibrium point;
[0111] The air conditioner has reached the temperature balance point, and energy consumption is predicted based on the energy consumption required for the temperature balance point, the power after the temperature balance point, the operating time, and the time required for the temperature balance point.
[0112] Specifically, when the air conditioner in the car cannot reach the temperature equilibrium point, the energy consumption can be predicted based on the energy consumption required for the temperature equilibrium point, the required operating time of the air conditioner in the car, and the time required for the temperature equilibrium point.
[0113] When the air conditioner in the car can reach the temperature balance point, the energy consumption can be predicted based on the energy consumption required for the temperature balance point, the power after the temperature balance point, the required operating time of the air conditioner in the car and the time required for the temperature balance point.
[0114] In one example, the following formula can be used to predict energy consumption:
[0115]
[0116] In the formula, W is the predicted energy consumption, T is the required operating time of the air conditioner, and the energy consumption required at the temperature equilibrium point is W. 1 , P 2 is the power after temperature equilibrium point, t 1 is the time required for the temperature balance point. Formula ① corresponds to the energy consumption prediction based on the time proportion calculation method when the air conditioner in the car cannot reach the temperature balance point; Formula ② corresponds to the energy consumption prediction calculation method when the air conditioner in the car can reach the temperature balance point.
[0117] In the present invention, by responding to the control of the air conditioner in the car, target sample operating data is obtained, wherein the target sample operating data includes operating data corresponding to the temperature balance point; according to the navigation information of the current trip, the operating time required for the air conditioner is determined, and according to the operating time and the target sample operating data, it is judged whether the air conditioner will reach the temperature balance point in the current trip; according to the judgment result, a target energy consumption prediction mode is determined, and according to the target energy consumption prediction mode, the energy consumption of the air conditioner is predicted, thereby achieving cost savings, avoiding dependence on expensive network infrastructure, reducing the cost of system implementation and operation, and making the energy consumption prediction process of the air conditioner in the car more flexible and convenient, which is particularly suitable for scenarios where network conditions for data transmission are limited or cost-sensitive.
[0118] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.
[0119] Reference Figure 4 , shows a schematic diagram of the structure of an energy consumption prediction device for an air conditioner in a car provided by some embodiments of the present invention, which may specifically include the following modules:
[0120] An energy consumption prediction device for an air conditioner in a car, characterized in that the method comprises:
[0121] The target sample operation data acquisition module 401 is used to acquire target sample operation data in response to the control of the air conditioner in the car; wherein the target sample operation data includes operation data corresponding to the temperature balance point;
[0122] The temperature balance point judgment module 402 is used to determine the required operating time of the air conditioner according to the navigation information of the current trip, and to judge whether the air conditioner will reach the temperature balance point in the current trip according to the operating time and the target sample operating data;
[0123] The energy consumption prediction module 403 is used to determine a target energy consumption prediction mode according to the judgment result, and perform energy consumption prediction on the air conditioner according to the target energy consumption prediction mode;
[0124] In some embodiments of the present invention, the apparatus comprises:
[0125] A timing curve acquisition module, used to acquire a plurality of historical control parameter groups, and obtain a plurality of timing curves of temperature changes according to the plurality of historical control parameter groups;
[0126] A temperature balance point acquisition module, used for performing time series filtering on each of the timing curves and finding the temperature balance point of each of the timing curves;
[0127] A mapping relationship establishing module, used to obtain sample operation data corresponding to each of the historical control parameter groups according to each of the temperature balance points, and establish a mapping relationship;
[0128] The mapping relationship saving module is used to preset and save the multiple historical control parameter groups and the multiple sample operation data according to the mapping relationship.
[0129] In some embodiments of the present invention, the timing curve acquisition module includes:
[0130] A candidate historical control parameter group acquisition submodule is used to acquire a historical control parameter set, and obtain multiple candidate historical control parameter groups according to the start and stop status of the air conditioner in the historical control parameter set;
[0131] The historical control parameter group acquisition submodule is used to perform denoising processing on the multiple candidate historical control parameter groups to obtain multiple historical control parameter groups.
[0132] In some embodiments of the present invention, the temperature balance point acquisition module includes:
[0133] A temperature threshold window acquisition submodule is used to set a maximum temperature threshold and a minimum temperature threshold according to the target temperature in the historical control parameter group to obtain a temperature threshold window; wherein the maximum temperature threshold is a value greater than the target temperature, and the minimum temperature threshold is a value less than the target temperature;
[0134] A temperature balance point acquisition submodule is selected, which is used to obtain a candidate temperature balance point according to the temperature threshold window and the timing curve;
[0135] The temperature balance point acquisition submodule is used to perform a stationarity test on the candidate temperature balance point to obtain the temperature balance point.
[0136] In some embodiments of the present invention, the sample operation data includes energy consumption required for the temperature balance point, power after the temperature balance point, and time required for the temperature balance point;
[0137] Among them, the energy consumption required for the temperature balance point is the energy consumption required for the air conditioner to run to the temperature balance point, the time required for the temperature balance point is the time required for the air conditioner to run to the temperature balance point, and the power after the temperature balance point is the average power of the air conditioner running to the temperature balance point.
[0138] In some embodiments of the present invention, the mapping relationship establishing module includes:
[0139] A sample operation data acquisition submodule, for acquiring the energy consumption required for the temperature balance point, the time required for the temperature balance point, and the power after the temperature balance point according to the temperature balance point;
[0140] The mapping relationship establishing submodule is used to establish a mapping relationship between the historical control parameter group and the energy consumption required for the temperature balance point, the time required for the temperature balance point and the power after the temperature balance point.
[0141] In some embodiments of the present invention, the target sample operation data acquisition module 401 includes:
[0142] A real-time control parameter acquisition submodule, for acquiring real-time control parameters of the air conditioner in response to controlling the air conditioner in the automobile;
[0143] A target historical control parameter group determination submodule is used to perform similarity measurement between the real-time control parameter and a plurality of preset historical control parameter groups, and determine the target historical control parameter group according to the similarity measurement result;
[0144] The target sample operation data acquisition submodule is used to determine the target sample operation data corresponding to the target historical control parameter group.
[0145] In some embodiments of the present invention, the energy consumption prediction module 403 includes:
[0146] The energy consumption prediction submodule before the temperature balance point is used for predicting the energy consumption according to the energy consumption required for the temperature balance point, the operating time and the time required for the temperature balance point before the air conditioner reaches the temperature balance point;
[0147] The energy consumption prediction submodule after the temperature balance point is used for predicting the energy consumption according to the energy consumption required for the temperature balance point, the power after the temperature balance point, the operating time and the time required for the temperature balance point when the air conditioner has reached the temperature balance point.
[0148] Some embodiments of the present invention also provide an electronic device, including a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the energy consumption prediction method for the air conditioner in the car as described above.
[0149] Some embodiments of the present invention further provide a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned method for predicting energy consumption of an air conditioner in a car.
[0150] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.
[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.
[0152] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.
[0153] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
[0154] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0155] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0156] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0157] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.
[0158] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the above elements.
[0159] The above is a detailed introduction to the energy consumption prediction method and device for the air conditioner in a car. Specific examples are used in this article to illustrate the principle and implementation method of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for general technicians in this field, according to the idea of the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.
Claims
1. A method for predicting energy consumption of air conditioning in a car, characterized in that: The method comprises: In response to controlling the air conditioner in the car, target sample operation data is acquired; wherein the target sample operation data includes operation data corresponding to the temperature balance point; Determine the required operating time of the air conditioner according to the navigation information of the current trip, and judge whether the air conditioner will reach a temperature equilibrium point in the current trip according to the operating time and the target sample operating data; According to the judgment result, a target energy consumption prediction mode is determined, and energy consumption of the air conditioner is predicted according to the target energy consumption prediction mode.
2. The method according to claim 1, characterized in that Before acquiring the target sample operation data in response to controlling the air conditioner in the car, the method further includes: Acquire a plurality of historical control parameter groups, and obtain a plurality of time series curves of temperature changes according to the plurality of historical control parameter groups; Performing time series filtering on each of the time series curves, and finding the temperature equilibrium point of each of the time series curves; According to each of the temperature balance points, sample operation data corresponding to each of the historical control parameter groups is obtained, and a mapping relationship is established; The plurality of historical control parameter groups and the plurality of sample operation data are preset and saved according to the mapping relationship.
3. The method according to claim 2, characterized in that The obtaining of multiple historical control parameter groups includes: Acquire a historical control parameter set, and obtain multiple candidate historical control parameter groups according to the start / stop status of the air conditioner in the historical control parameter set; The plurality of candidate historical control parameter groups are subjected to denoising processing to obtain a plurality of the historical control parameter groups.
4. The method according to claim 2, characterized in that: The step of finding the temperature equilibrium point of each of the timing curves comprises: According to the target temperature in the historical control parameter group, a maximum temperature threshold and a minimum temperature threshold are set to obtain a temperature threshold window; wherein the maximum temperature threshold is a value greater than the target temperature, and the minimum temperature threshold is a value less than the target temperature; Obtaining a candidate temperature balance point according to the temperature threshold window and the timing curve; A stationarity test is performed on the candidate temperature balance point to obtain the temperature balance point.
5. The method according to claim 2, characterized in that: The sample operation data includes the energy consumption required for the temperature balance point, the power after the temperature balance point, and the time required for the temperature balance point; Among them, the energy consumption required for the temperature balance point is the energy consumption required for the air conditioner to run to the temperature balance point, the time required for the temperature balance point is the time required for the air conditioner to run to the temperature balance point, and the power after the temperature balance point is the average power of the air conditioner running to the temperature balance point.
6. The method according to claim 5, characterized in that The step of obtaining sample operation data corresponding to each of the historical control parameter groups according to each of the temperature balance points and establishing a mapping relationship includes: According to the temperature balance point, the energy consumption required for the temperature balance point, the time required for the temperature balance point, and the power after the temperature balance point are obtained; A mapping relationship between the historical control parameter group and the energy consumption required for the temperature balance point, the time required for the temperature balance point, and the power after the temperature balance point is established.
7. The method according to claim 2, characterized in that The step of obtaining target sample operation data in response to controlling the air conditioner in the automobile includes: In response to controlling the air conditioner in the automobile, acquiring real-time control parameters of the air conditioner; Performing similarity measurement between the real-time control parameter and a plurality of preset historical control parameter groups, and determining a target historical control parameter group according to the similarity measurement result; Determine the target sample operation data corresponding to the target historical control parameter group.
8. The method according to claim 5, characterized in that Determining the target energy consumption prediction mode according to the judgment result includes: The air conditioner has not reached the temperature equilibrium point, and energy consumption is predicted according to the energy consumption required for the temperature equilibrium point, the operating time, and the time required for the temperature equilibrium point; The air conditioner has reached the temperature balance point, and energy consumption is predicted based on the energy consumption required for the temperature balance point, the power after the temperature balance point, the operating time, and the time required for the temperature balance point.
9. An energy consumption prediction device for an air conditioner in a car, characterized in that: The device comprises: A target sample operation data acquisition module is used to acquire target sample operation data in response to the control of the air conditioner in the car; wherein the target sample operation data includes operation data corresponding to the temperature balance point; a temperature balance point judgment module, for determining the required operating time of the air conditioner according to the navigation information of the current trip, and judging whether the air conditioner will reach the temperature balance point in the current trip according to the operating time and the target sample operating data; The energy consumption prediction module is used to determine a target energy consumption prediction mode according to the judgment result, and perform energy consumption prediction on the air conditioner according to the target energy consumption prediction mode.
10. An electronic device, characterized in that: The invention comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the method for predicting energy consumption of an air conditioner in a car as claimed in any one of claims 1 to 8 is implemented.
11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for predicting energy consumption of an air conditioner in a vehicle according to any one of claims 1 to 8 is implemented.