A home central air conditioner operation energy consumption wisdom monitoring and analyzing system

Abstract

The present application belongs to the technical field of intelligent monitoring of air conditioner operation energy consumption, and relates to a kind of intelligent monitoring and analysis system for household central air conditioner operation energy consumption.The present application accurately extracts the energy consumption data of air conditioner independent operation from the total power consumption curve of the family, determines the high energy consumption state based on historical benchmark, analyzes the correlation degree with key operation characteristics, generates a diagnosis priority in combination with outdoor temperature, and performs a short-term operation test verification on the primary root cause.Finally, an energy-saving control signal is generated based on the verification result.The problems of difficulty in accurately separating air conditioner energy consumption from total load, inability to locate high energy consumption root cause, and difficulty in implementing energy-saving recommendations in existing technologies are solved.The present application improves the accuracy of air conditioner energy consumption monitoring, accurately locates the high energy consumption root cause, and achieves effective closed-loop energy-saving optimization.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of intelligent monitoring of air conditioner operation energy consumption, and relates to a household central air conditioner operation energy consumption intelligent monitoring and analysis system. BACKGROUND

[0002] With the improvement of the living standard of residents and the increasingly stringent requirements for building energy saving, the large-scale application and high-load operation of household central airconditioners have led to increasingly prominent energy consumption problems. At present, the traditional energy consumption monitoring scheme mainly obtains the total power consumption data of a family through an intelligent electric meter or a power monitoring device to form a power consumption load curve. Some schemes attempt to combine the on-off signals of equipment or simple time rules to coarsely estimate the power consumption of specific electrical appliances. However, such methods have the following defects: first, the total power consumption load of a family is the result of the superposition of multiple electrical appliances, and the existing methods are difficult to accurately separate the real energy consumption of an air conditioner during independent operation from the complex fluctuation of the total power curve. Simply relying on the on-off signals or fixed time division is easy to misjudge due to the accidental start-stop of other high-power electrical appliances, leading to distorted air conditioner energy consumption data and making it impossible to serve as a reliable basis for subsequent analysis.

[0003] Secondly, the existing energy consumption evaluation is mostly based on simple threshold comparison or only focuses on the total power consumption, which are both one-sided and result-oriented evaluation methods, lack in-depth analysis of high energy consumption processes and causes, and make it difficult for the system to accurately determine whether a high energy consumption event is caused by poor external environment, low equipment energy efficiency or poor user habits, and to locate the root cause of the problem.

[0004] Finally, even if the energy consumption anomaly is identified, the existing scheme usually only provides an alarm for high energy consumption or a simple statistical report, at most gives a general suggestion such as checking the equipment or suggesting to increase the set temperature. Such suggestions lack targeted judgment of the main energy consumption reason in specific situations, and are unable to verify the correctness of the presumed cause through controllable and low-intrusive tests, leading to low feasibility and user adoption rate of the suggestions, and making it difficult for energy-saving measures to take effect. SUMMARY

[0005] In view of this, the household central air conditioner operation energy consumption intelligent monitoring and analysis system is proposed to solve the problems in the background.

[0006] The purpose of the application can be achieved by the following technical scheme: a household central air conditioner operation energy consumption intelligent monitoring and analysis system, comprising: an operation monitoring module: obtaining the total power consumption curve of a family and the air conditioner on-off signal in a monitoring period; based on the air conditioner on-off signal, dividing the air conditioner independent operation period from the total power consumption curve of the family, and extracting the average power and cumulative energy consumption of the air conditioner in the period.

[0007] The energy consumption evaluation module: based on the average power of the air conditioner and the cumulative energy consumption, whether the air conditioner is in a high energy consumption state is determined by comparison; when it is determined that the air conditioner is in a high energy consumption state, three operation characteristic values of the air conditioner in the corresponding period are extracted, including the actual operation energy efficiency ratio, the deviation value of the set temperature and the room temperature, and the daily cumulative operation time length of the air conditioner, and the correlation degrees of the three operation characteristic values and the high energy consumption state are calculated.

[0008] The diagnosis optimization module: based on the correlation degrees, a diagnosis priority list for the high energy consumption source is generated in combination with the current outdoor temperature; according to the diagnosis priority list, a preset short-term operation test is performed on the primary diagnosis source in the list, the diagnosis conclusion is verified based on the energy consumption change before and after the test, and an energy-saving regulation signal is generated accordingly.

[0009] Compared with the prior art, the beneficial effects of the present application are as follows: (1) The present application effectively solves the technical problem that the air conditioner independent operation period is difficult to accurately identify in the complex household electricity environment by using a dual criterion identification method based on the air conditioner start-stop signal and the power curve change characteristics. The method uses the comparison of the moving average of the first-order difference absolute value and the reference change rate, and combines the operation time length screening condition to improve the accuracy of the air conditioner energy consumption data extraction, avoid misjudgment caused by interference of other electrical appliances, and make the monitoring result have engineering application value.

[0010] (2) The present application solves the technical bottleneck that the high energy consumption reason is difficult to locate by constructing a dynamic evaluation mechanism based on historical energy consumption benchmarks and combining statistical correlation analysis of the three operation characteristic values and energy consumption indicators. The system can quantitatively evaluate the contribution of factors such as energy efficiency ratio reduction, improper temperature control setting or excessive use to energy consumption, and dynamically generate a diagnosis priority in combination with the outdoor temperature, so that the energy consumption anomaly analysis is improved from single threshold judgment to multi-dimensional correlation analysis, greatly improving the accuracy and scientificity of the diagnosis result.

[0011] (3) The present application overcomes the technical defect that the traditional scheme only provides general suggestions and is difficult to implement by designing a closed-loop optimization mechanism of short-term operation test and energy consumption change verification. The system performs controllable test on the primary diagnosis source, verifies the diagnosis conclusion based on the measured energy consumption change, dynamically adjusts the optimization strategy, and generates a targeted energy-saving regulation signal. This method improves the feasibility and user adoption rate of energy-saving measures, so that energy consumption optimization is truly converted into actual energy-saving effect. BRIEF DESCRIPTION OF DRAWINGS

[0012] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed for the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creating laborious work.

[0013] Figure 1 It is a home central air conditioner running energy consumption wisdom monitoring analysis system module connection diagram in the present application.

[0014] Figure 2 It is a flow chart of the method for dividing the air conditioner independent running period in the present application.

[0015] Figure 3 It is a content flow chart for determining whether the air conditioner is in a high energy consumption state in the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0017] Please refer to Figure 1 As shown in the figure, the present application provides a home central air conditioner running energy consumption wisdom monitoring analysis system, which comprises: a running monitoring module, an energy consumption evaluation module, a diagnosis optimization module, and the connection relationship between the modules is: the running monitoring module and the energy consumption evaluation module are connected, and the energy consumption evaluation module and the diagnosis optimization module are connected.

[0018] The running monitoring module: acquires the total power curve of the family in the monitoring period and the air conditioner start-stop signal; based on the air conditioner start-stop signal, the air conditioner independent running period is divided from the total power curve of the family, and the average power and the cumulative energy consumption of the air conditioner in the period are extracted.

[0019] Considering that the total power of the family contains the energy consumption of various loads such as air conditioners, lighting and household appliances, if the total power data is directly used for air conditioner energy consumption analysis, the data will be distorted due to the interference of other loads, and the energy consumption characteristics of the air conditioner during independent running cannot be accurately obtained.

[0020] At the same time, the air conditioner start-stop signal is the core basis for defining its running state, and based on this signal, the possible running period of the air conditioner can be preliminarily locked, which provides a premise for subsequent accurate division of the air conditioner independent running period.

[0021] In a specific embodiment, the power monitoring device deployed in the total power distribution circuit of the family acquires the total power discrete data of the family in the monitoring period, and forms the total power curve of the family after data preprocessing, and the air conditioner start-stop signal is acquired through the air conditioner controller or the special sensor, wherein the data preprocessing is a technology known to those skilled in the art, and no further limitation and elaboration is made.

[0022] Please refer to Figure 2As shown, based on the level change of the air conditioner start-stop signal, the starting time point and the ending time point of the air conditioner operation are determined, and a candidate operation period is generated.

[0023] In the candidate operation period, the first-order differential absolute value sequence of the total household power curve is calculated with a preset sampling interval of 1-5 minutes, which reflects the intensity of power change between adjacent sampling points and is used to quantify the fluctuation characteristics of the power load.

[0024] The moving average of the first-order differential absolute value sequence in the sliding time window is calculated to represent the local smoothing level of the total household power fluctuation in the current candidate period, and the arithmetic mean of the first-order differential absolute value of the total household power curve in the monitoring period is calculated as the reference change rate, which represents the overall average intensity of the household power load fluctuation in the monitoring period, providing a reference for judging whether the local fluctuation is smooth.

[0025] The length of the sliding time window is determined to be 3-10 minutes according to the fluctuation characteristics of the household power load.

[0026] If the moving average is less than the reference change rate, and the duration of the candidate operation period is greater than the average duration of all candidate operation periods generated based on the air conditioner start-stop signal in the monitoring period, it indicates that the total household power fluctuation is smooth and the air conditioner runs stably, excluding the interference caused by the short-term switching of other high-power electrical appliances or the frequent start-stop of the air conditioner, so it can be determined that the air conditioner is in a dominant power load position in this period, and the candidate operation period is confirmed as an independent operation period; otherwise, the operation period is a non-independent operation period.

[0027] In the confirmed independent operation period, based on the discrete sampling point sequence of the total household power curve, the arithmetic mean of all sampling point power values is calculated as the average power of the air conditioner, which is used to represent the typical power level of the air conditioner in this period, and the product of the discrete sampling point power value and the preset sampling interval is accumulated as the cumulative energy consumption, which is used to reflect the total energy consumption of the air conditioner in this period.

[0028] The energy consumption evaluation module: based on the average power of the air conditioner and the cumulative energy consumption, it is determined whether the air conditioner is in a high energy consumption state by comparison; when it is determined to be in a high energy consumption state, the actual operation energy efficiency ratio of the air conditioner in the corresponding period, the deviation value of the set temperature and the room temperature, and the daily cumulative operation time of the air conditioner are extracted as three operation characteristic values, and the correlation degree of each operation characteristic value with the high energy consumption state is calculated.

[0029] Considering that the energy consumption level of the household central air conditioner is affected by many factors such as operating conditions, set parameters, etc., it is not possible to determine whether it is in an unreasonable high energy consumption state by only using a single energy consumption data, so it is necessary to establish a relative reference for comparison and evaluation.

[0030] At the same time, when the high energy consumption state is confirmed, it is necessary to determine which operating characteristics are the key factors leading to high energy consumption, so as to provide direction for subsequent accurate diagnosis; in addition, the correlation degree between the operating characteristic value and the high energy consumption state directly determines the diagnosis priority, and the accuracy of the correlation degree calculation will affect the diagnosis efficiency and energy saving control effect, so the system needs to complete the high energy consumption state determination and correlation degree analysis.

[0031] Referring to Figure 3 In one embodiment, the determination of whether the air conditioner is in a high energy consumption state comprises: calculating the arithmetic mean of the average power of the air conditioner in all confirmed independent running periods in the monitoring period as a power reference, and the arithmetic mean of the cumulative energy consumption as an energy consumption reference.

[0032] The average power of the air conditioner in the current independent running period is compared with the power reference, and the cumulative energy consumption in the current independent running period is compared with the energy consumption reference.

[0033] When the average power of the air conditioner is greater than the power reference and the cumulative energy consumption is greater than the energy consumption reference, it indicates that the current air conditioner not only runs at a power higher than the typical level, but also consumes total power exceeding the normal range in this period, which is abnormal in both instantaneous intensity and cumulative total amount, so the air conditioner is determined to be in a high energy consumption state; otherwise, the air conditioner is determined to be in a non-high energy consumption state.

[0034] Further, the method for obtaining the correlation degree between each operating characteristic value and the high energy consumption state comprises: collecting all independent running periods in the monitoring period that are determined to be in a high energy consumption state to form a high energy consumption sample set.

[0035] For each sample in the high energy consumption sample set, the corresponding three operating characteristic values are extracted, i.e. the actual operating energy efficiency ratio, the deviation value of the set temperature and the room temperature, and the daily cumulative running time of the air conditioner in the period.

[0036] The three operating characteristic values together constitute the core dimension affecting the energy consumption of the air conditioner: the actual operating energy efficiency ratio directly reflects the performance decay or working condition efficiency of the device itself; the deviation value of the set temperature and the room temperature reflects the matching degree of the user's temperature control setting and actual demand, which directly affects the compressor load; the daily cumulative running time of the air conditioner represents the user's usage habit and the time accumulation of the system continuous load. The three factors analyze the root cause of high energy consumption from three aspects of device state, setting rationality and usage behavior.

[0037] The actual operation energy efficiency ratio is obtained by directly obtaining through an air conditioner communication interface or estimating the refrigerating capacity or heating capacity data in the independent operation period through measuring the temperature difference and air volume of the air inlet and outlet, simultaneously obtaining the cumulative power consumption in the period, and calculating the ratio of the refrigerating capacity or heating capacity to the cumulative power consumption.

[0038] Since high energy consumption can be manifested as high instantaneous power or accumulated energy consumption caused by long-time operation, a single indicator cannot comprehensively characterize, therefore, the statistical correlation coefficient between each operation characteristic value and each energy consumption representation indicator is calculated, the coefficient represents the strength and direction of the linear relationship between the operation characteristic value and the energy consumption indicator, and the greater the absolute value, the stronger the statistical correlation.

[0039] According to the absolute value of the statistical correlation coefficient, the correlation degree between each operation characteristic value and the high energy consumption state is determined. Specifically, for each operation characteristic value, the absolute value of the statistical correlation coefficient between the air conditioner average power and the cumulative energy consumption is obtained, and the statistical correlation coefficient can be calculated by Pearson correlation coefficient, Spearman rank correlation coefficient and other statistical methods, and no further limitation and elaboration is made.

[0040] For each operation characteristic value, the absolute values of the two statistical correlation coefficients corresponding thereto are compared, since the correlation degree aims to measure the maximum potential correlation strength between the characteristic and the high energy consumption state, in order not to underestimate its influence, the larger one is selected as the correlation degree between the operation characteristic value and the high energy consumption state.

[0041] The diagnosis optimization module generates a diagnosis priority list for the root cause of high energy consumption based on the correlation degrees and the current outdoor temperature, and performs a preset short-term operation test on the first diagnosis root cause in the list according to the diagnosis priority list, verifies the diagnosis conclusion based on the energy consumption change before and after the test, and generates an energy-saving control signal accordingly.

[0042] The outdoor temperature is a key environmental factor affecting the operation energy consumption of the air conditioner, which directly determines the heat load size and operation condition of the system, and then changes the influence weight of factors such as temperature setting deviation, equipment operation efficiency and use time on the total energy consumption, under different outdoor temperatures, the main root cause of high energy consumption can be different, if not combined with the outdoor temperature, the diagnosis can be directly sorted according to the correlation degree, which can reduce the diagnosis efficiency.

[0043] Meanwhile, the accuracy of the high energy consumption source needs to be verified by actual operation test, and the reliability of the diagnosis conclusion cannot be ensured based on the correlation degree analysis alone; in addition, the energy-saving control signal needs to have specific executable parameters to realize the precise optimization of the air conditioner operation state, so the diagnosis and optimization need to be completed through the closed-loop process of priority sorting, test verification and parameter solidification.

[0044] In a specific embodiment, the method for obtaining the diagnosis priority list is: collecting the current outdoor temperature through a temperature sensor, calculating the average outdoor temperature in the monitoring period as the reference outdoor temperature, and comparing the current outdoor temperature with the reference outdoor temperature.

[0045] If the current outdoor temperature is greater than the reference outdoor temperature, for example, the current temperature is higher than the average temperature in the monitoring period, it indicates that the air conditioning system is bearing a significantly increased external thermal load. In this working condition, since the load rate of the compressor is sensitive to the temperature setting, the deviation value of the set temperature from the indoor actual temperature becomes the dominant factor affecting the overall energy consumption, so the deviation value of the set temperature from the room temperature is placed at the top of the diagnosis priority list, and the remaining two operation characteristic values are sorted according to their correlation degrees from high to low.

[0046] If the current outdoor temperature is less than or equal to the reference outdoor temperature, it indicates that the outdoor environment temperature is in a normal or relatively mild range, and the high energy consumption of the air conditioner is more likely to be caused by low equipment operation efficiency or user usage habit problems, then the three operation characteristic values are sorted according to their correlation degrees from high to low to generate the diagnosis priority list, which is used to guide the system to sequentially check and verify the potential high energy consumption sources according to the possibility.

[0047] Further, the method for executing a preset short-term operation test on the first diagnosis source in the diagnosis priority list includes: identifying the first diagnosis source from the diagnosis priority list, and the first diagnosis source is one of the three operation characteristic values.

[0048] Based on the first diagnosis source, the corresponding test action is performed, as follows: if the first diagnosis source is the deviation value of the set temperature from the room temperature, it indicates that the preliminary diagnosis considers that the unreasonable temperature setting is the main cause of high energy consumption, then a preset temperature offset (usually +1℃ to +2℃ in the cooling mode, and usually -1℃ to -2℃ in the heating mode) is added or subtracted from the current air conditioner set temperature, and the operation is maintained within a preset test window.

[0049] If the first diagnosis source is the daily cumulative operation time of the air conditioner, it indicates that the preliminary diagnosis considers that the long operation time of the air conditioner is the main cause of high energy consumption, then the current daily cumulative operation time of the air conditioner is reduced by a preset time offset (usually 0.5 hours to 1 hour), and the operation is maintained within a preset test window.

[0050] If the primary diagnosis source is the actual operation energy efficiency ratio, indicating that the preliminary diagnosis recognizes that the low efficiency of the air conditioning equipment itself is the main cause of high energy consumption, the air conditioning operation mode is switched to the preset energy-saving mode (such as the low-frequency operation mode of the variable frequency air conditioner, the air supply mode, and the alternating operation mode of the cooling / heating mode), and maintained in the preset test window.

[0051] In the preset test window, based on the total power curve of the family, the average power in the air conditioning independent operation period is calculated as the test energy consumption benchmark.

[0052] The average power of the baseline period before the test is obtained as a comparison benchmark, and the baseline period before the test is an adjacent air conditioning independent operation period before the test.

[0053] It should be noted that the adjacent air conditioning independent operation period before the test meets the difference between the outdoor temperature of the preset test window and the preset range, and the indoor set temperature is the same. If it does not meet, the previous period is selected, until it meets. Wherein, the preset range can be set to within ±2℃, as other embodiments, the implementer can set it according to the actual situation.

[0054] The difference between the test energy consumption benchmark and the comparison benchmark is calculated as the energy consumption change before and after the test.

[0055] Further, the energy consumption change before and after the test is verified based on the diagnosis conclusion, and the energy-saving control signal is generated accordingly, including: extracting the calculated energy consumption change before and after the test, when the energy consumption change before and after the test is less than zero, indicating that the average operation power of the air conditioner indeed decreases after executing the adjustment for the primary diagnosis source, proving that the diagnosis source is effective, and the diagnosis conclusion of the primary diagnosis source is effective.

[0056] Based on the verified diagnosis conclusion, the adjustment parameters executed in the test window are solidified as permanent operation parameters, and the energy-saving control signal is generated.

[0057] The energy-saving control signal is a set of instructions containing specific control parameters, which are determined according to the type of the verified primary diagnosis source: if the primary diagnosis source is the deviation value of the set temperature and the room temperature, the specific control parameter is the set temperature value after adjustment in the test window.

[0058] If the primary diagnosis source is the daily cumulative operation time of the air conditioner, the specific control parameter is the upper limit value of the daily operation time after adjustment in the test window.

[0059] If the primary diagnosis source is the actual operation energy efficiency ratio, the specific control parameter is the preset energy-saving mode identifier switched in the test window.

[0060] The energy-saving regulation signal is sent to the air conditioner controller through a communication interface, and is used for continuously executing the specific regulation parameter.

[0061] Since the above short-term test has verified the energy-saving effect of the adjustment parameter under the current working condition, in order to persist the energy-saving effect, the instruction is generated and issued to guide the air conditioner to automatically run according to the optimized parameter under subsequent similar conditions.

[0062] When the energy consumption change before and after the test is greater than or equal to zero, it indicates that the adjustment measure taken does not produce the expected energy-saving effect, and the diagnosis root cause may not be the main cause of the current high energy consumption state, or the adjustment range is improper, and the diagnosis conclusion of the primary diagnosis root cause is invalid, and the primary diagnosis root cause is removed from the diagnosis priority list.

[0063] Based on the correlation degree of the remaining running characteristic values, the current outdoor temperature is combined to regenerate a diagnosis priority list as an updated diagnosis priority list, and the short-term running test is re-executed based on the updated diagnosis priority list.

[0064] It should be noted that if the updated diagnosis priority list is empty or all running characteristic values have been verified to be invalid, the diagnosis optimization process is stopped, and a prompt information is output.

[0065] In summary, the present application firstly divides the air conditioner independent running period accurately and extracts the average power and cumulative energy consumption based on the start-stop signal and power curve fluctuation through the running monitoring module; the high energy consumption state is determined based on the dynamic reference through the energy consumption evaluation module, and the correlation degree of the actual running energy efficiency ratio, the set temperature deviation and the daily cumulative running time correlation coefficient is analyzed; then the diagnosis priority list is generated in combination with the current outdoor temperature through the diagnosis optimization module, and the short-term running test is executed on the primary root cause to verify its effectiveness, and finally the energy-saving regulation instruction is generated and solidified according to the verification result.

[0066] The system realizes the whole process automation from accurate data extraction, intelligent state evaluation to closed-loop diagnosis of root causes and automatic generation of optimization strategies, and improves the accuracy of high energy consumption problem diagnosis and the effectiveness of regulation measures.

[0067] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part.

[0068] Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the examples disclosed herein can be implemented as electronic hardware, computer software, or combinations of both. The various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality, without limitation. Depending upon the implementation, the techniques disclosed herein can each be fully implemented, in one or more specific purposes machines, enclosures, devices, systems or semiconductors, software, and / or various combinations thereof. Such examples are merely illustrative and can not be limited to any specific combination of hardware and / or software.

[0069] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically independently, or two or more modules can be integrated in one module.

[0070] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical scope disclosed by the present application, which should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0071] Finally, the above merely provides the preferred embodiments of the present application, and is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present application should be covered by the protection scope of the present application.

Claims

1. A system for intelligently monitoring and analyzing the energy consumption of a household central air conditioner, characterized in that, The method comprises the following steps: a running monitoring module obtains a total household power curve and an air conditioner start-stop signal in a monitoring period; an air conditioner independent running period is divided from the total household power curve based on the air conditioner start-stop signal, and an air conditioner average power and a cumulative energy consumption in the period are extracted; an energy consumption evaluation module determines whether the air conditioner is in a high energy consumption state by comparing the air conditioner average power and the cumulative energy consumption; when it is determined that the air conditioner is in a high energy consumption state, three running characteristic values, including an actual running energy efficiency ratio of the air conditioner, a deviation value of a set temperature and a room temperature, and a daily cumulative running time of the air conditioner in the corresponding period, are extracted, and the correlation degrees of the three running characteristic values with the high energy consumption state are calculated; a diagnosis optimization module generates a diagnosis priority list for the high energy consumption source based on the correlation degrees and in combination with a current outdoor temperature, including: obtaining the current outdoor temperature and an average outdoor temperature in the monitoring period as a reference outdoor temperature, and comparing the current outdoor temperature with the reference outdoor temperature; if the current outdoor temperature is greater than the reference outdoor temperature, the deviation value of the set temperature and the room temperature is placed at the top of the diagnosis priority list, and the remaining two running characteristic values are sorted in descending order of the correlation degrees; if the current outdoor temperature is less than or equal to the reference outdoor temperature, the three running characteristic values are sorted in descending order of the correlation degrees to generate the diagnosis priority list; according to the diagnosis priority list, a preset short-term running test is performed on the first diagnosis source in the list, the diagnosis conclusion is verified based on the energy consumption change before and after the test, and an energy-saving control signal is generated accordingly.

2. The system of claim 1, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system for a household. The method for dividing the air conditioner independent running period comprises the following steps: based on the air conditioner start-stop signal, the start time point and the end time point of the air conditioner running are determined to generate a candidate running period; a first-order differential absolute value sequence of the total household power curve is calculated at a preset sampling interval in the candidate running period; a moving average of the first-order differential absolute value sequence in a sliding time window is calculated, and an arithmetic average of the first-order differential absolute value of the total household power curve in the monitoring period is calculated as a reference change rate; if the moving average is less than the reference change rate, and the duration of the candidate running period is greater than the average duration of all candidate running periods generated based on the air conditioner start-stop signal in the monitoring period, the candidate running period is confirmed as an independent running period.

3. The system of claim 1, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system. The method for extracting the air conditioner average power and the cumulative energy consumption in the period comprises the following steps: in the confirmed independent running period, an arithmetic average of all sampling point power values is calculated as the air conditioner average power based on a discrete sampling point sequence of the total household power curve; in the confirmed independent running period, a product of the discrete sampling point power value and the preset sampling interval is accumulated to calculate the cumulative energy consumption.

4. The system of claim 1, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system. The method for determining whether the air conditioner is in a high energy consumption state comprises the following steps: an arithmetic average of the air conditioner average power of all confirmed independent running periods in the monitoring period is calculated as a power reference, and an arithmetic average of the cumulative energy consumption is calculated as an energy consumption reference; the air conditioner average power of the current independent running period to be judged is compared with the power reference, and the cumulative energy consumption of the current independent running period is compared with the energy consumption reference; When the average power of the air conditioner is greater than the power reference and the cumulative energy consumption is greater than the energy consumption reference, it is determined that the air conditioner is in a high energy consumption state.

5. The system of claim 1, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system for a household. The method for obtaining the correlation degree between each operating characteristic value and the high energy consumption state comprises the following steps: Collect all independent operating periods determined to be in the high energy consumption state in a monitoring period to form a high energy consumption sample set; For each sample in the high energy consumption sample set, extract its corresponding three operating characteristic values, i.e., the actual operating energy efficiency ratio, the deviation value of the set temperature and the room temperature, and the daily cumulative operating time length of the air conditioner in the period; Respectively take the average power of the air conditioner and the cumulative energy consumption as energy consumption representation indexes, and calculate the statistical correlation coefficients between each operating characteristic value and each energy consumption representation index; Determine the correlation degree between each operating characteristic value and the high energy consumption state according to the absolute value of the statistical correlation coefficient.

6. The system of claim 5, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system. The method for determining the correlation degree between each operating characteristic value and the high energy consumption state according to the absolute value of the statistical correlation coefficient comprises the following steps: For each operating characteristic value, respectively obtain the absolute value of the statistical correlation coefficient thereof and the cumulative energy consumption, and the absolute value of the statistical correlation coefficient thereof and the average power of the air conditioner; For each operating characteristic value, compare the absolute values of the two statistical correlation coefficients corresponding thereto, and select the larger one as the correlation degree between the operating characteristic value and the high energy consumption state.

7. The system of claim 1, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system for a home. The method for executing a preset short-term operating test on the primary diagnosis root source in the diagnosis priority list comprises the following steps: Identify the primary diagnosis root source from the diagnosis priority list, wherein the primary diagnosis root source is one of the three operating characteristic values; Perform a corresponding test action based on the primary diagnosis root source, as follows: If the primary diagnosis root source is the deviation value of the set temperature and the room temperature, increase or decrease the preset temperature offset from the current air conditioner set temperature, and maintain operation within the preset test window; If the primary diagnosis root source is the daily cumulative operating time length of the air conditioner, reduce the current daily cumulative operating time length of the air conditioner by the preset time offset, and maintain operation within the preset test window; If the primary diagnosis root source is the actual operating energy efficiency ratio, switch the operating mode of the air conditioner to a preset energy-saving mode, and maintain operation within the preset test window; Within the preset test window, calculate the average power of the air conditioner in the independent operating period based on the total power consumption curve of the family, as a test energy consumption reference; Obtain the average power of the reference period before the test as a comparison reference, wherein the reference period before the test is an adjacent air conditioner independent operating period before the test; Calculate the difference between the test energy consumption reference and the comparison reference as the energy consumption change amount before and after the test.

8. The system of claim 1, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system for a home. The method for verifying the diagnosis conclusion based on the energy consumption change amount before and after the test, and generating an energy-saving control signal based thereon comprises the following steps: Extract the calculated energy consumption change amount before and after the test, and when the energy consumption change amount before and after the test is less than zero, determine that the diagnosis conclusion of the primary diagnosis root source is valid; Based on the verified diagnosis conclusion, solidify the adjustment parameters executed within the test window as permanent operating parameters, and generate an energy-saving control signal; When the energy consumption change amount before and after the test is greater than or equal to zero, determine that the diagnosis conclusion of the primary diagnosis root source is invalid, and remove the primary diagnosis root source from the diagnosis priority list; Based on the correlation degree of the residual operating characteristic value, and in combination with the current outdoor temperature, a diagnosis priority list is regenerated as an updated diagnosis priority list; The short-term operation test is re-executed based on the updated diagnosis priority list.

9. The system of claim 8, wherein the system comprises a central air conditioner operation energy consumption intelligent monitoring and analyzing system. The energy-saving regulation signal is a command set containing specific regulation parameters.

Images