A method and system for evaluating abnormal cooling and heating supply

Abstract

The present invention relates to a cooling and heating amount abnormality evaluation method and system, which determine the cooling and heating amount hourly data of indoor equipment within a set period T and the corresponding outdoor air enthalpy value hourly data; calculate the change consistency score between the cooling and heating amount hourly data and the outdoor air enthalpy value hourly data according to a preset change consistency scoring formula; judge whether the energy consumption of indoor equipment is abnormal according to the change consistency score between the two, so as to evaluate the cooling / heating capacity of the building's cold and heat sources, confirm whether it can match the changes in outdoor meteorological conditions, and give a quantitative evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy consumption management, and in particular to a method and system for evaluating abnormal cooling and heating supply. Background Art

[0002] China is currently in a stage of high energy consumption and relative energy shortage. The lack of energy and the increasing demand for energy in society have become one of the prominent contradictions that restrict social progress and development. The large-scale construction of cities has made the proportion of building energy consumption in the total social energy consumption reach 46%. Therefore, it is of great significance to monitor and reasonably use building energy consumption for the goal of building energy conservation planning. Among them, reducing energy consumption during operation through automatic diagnosis of abnormalities and control means is one of the important ways to achieve energy conservation.

[0003] Currently, many high-energy-consuming enterprises have realized the collection of energy consumption by item and accumulated a large amount of historical energy consumption data, which provides a basis for abnormal diagnosis of data. However, a large amount of historical energy consumption data also brings "data disaster", making it impossible for relevant managers to quickly and accurately find abnormal energy consumption problems from the data, which ultimately makes it difficult to monitor and manage building energy consumption.

[0004] The traditional monitoring method is generally that the relevant energy consumption management personnel set an energy consumption threshold based on experience to determine whether the energy consumption is abnormal. However, there are two problems with this method. First, the threshold cannot be accurately determined. If the threshold is too high or too low, it will directly affect the accuracy of the actual detection results. Secondly, the complexity of the building model. For commercial buildings, the operation of their energy consumption will be affected by many factors and change, such as seasons, regional environment, passenger flow and other characteristic factors. Therefore, simply relying on thresholds for diagnosis cannot adapt to complex dynamic energy consumption models. Therefore, in order to solve the limitations of the threshold method, some more intelligent diagnostic methods have been proposed: Tang Guizhong et al. used real-time data collection technology to achieve real-time statistics of data, and used statistical methods to diagnose abnormal energy consumption; Li et al. used the outlier diagnosis method to propose abnormal data points of energy consumption and classified and predicted historical energy consumption data. Although the above methods have solved the limitation of the threshold method, there are still the following problems: 1. The above methods are all based on static analysis of energy consumption based on historical data, and cannot accurately diagnose energy consumption anomalies in real time; 2. Due to the use of a more complex calculation model, the computational consumption of data will also increase, and it cannot be adapted to historical energy consumption data under conditions of massive data; 3. Although the anomalies can be diagnosed and analyzed according to certain conditions, the diagnostic results are too rough, the results are not quantified, and different control measures cannot be taken for different diagnostic results.

[0005] Therefore, it is necessary to propose a more efficient and widely applicable cooling / heating abnormality diagnosis method. Summary of the invention

[0006] In order to overcome the deficiencies of the above-mentioned prior art and to address the above-mentioned problems, the present invention proposes a cooling / heating capacity abnormality evaluation method, which can evaluate the cooling / heating capacity of a building's cold / heat source, confirm whether it can match changes in outdoor meteorological conditions, and give a quantitative evaluation.

[0007] One object of the present invention is to provide a method for evaluating abnormal cooling and heating supply, the method comprising:

[0008] Determine the hourly data X(t) of the cooling and heating capacity of the indoor equipment within the set period T, and the corresponding hourly data Y(t) of the outdoor air enthalpy value, where X(t) and Y(t) are both 24×T-dimensional vectors;

[0009] According to the preset change consistency scoring formula, the change consistency score between the hourly data X(t) of cooling and heating quantity and the hourly data Y(t) of outdoor air enthalpy value is calculated;

[0010] Whether the energy consumption of indoor equipment is abnormal is determined based on the change consistency score between the two.

[0011] Optionally, according to a preset change consistency scoring formula, the change consistency score between the hourly data X(t) of cooling and heating amount and the hourly data Y(t) of outdoor air enthalpy value is calculated, including:

[0012] ;

[0013] In the formula, is the correlation coefficient between X(t) and Y(t), Cov(X,Y) is the covariance between X(t) and Y(t), Var[X] is the variance of X(t), and Var[Y] is the variance of Y(t);

[0014] When the correlation coefficient is positive, it means that the variables are positively correlated, and the closer the correlation coefficient is to 1, the greater the correlation;

[0015] When the correlation coefficient is negative, it means that the variables are negatively correlated;

[0016] When the correlation coefficient is 0, it means that there is no linear relationship between the variables;

[0017] When r >=0, score=r×10;

[0018] When r < 0, score = r×(-10);

[0019] Among them, score is the change consistency score between the two.

[0020] Optionally, judging whether the energy consumption of indoor equipment is abnormal based on the change consistency score between the two, including:

[0021] In the cooling season, if the correlation coefficient is positive, that is, r >= 0, score = r × 10, and score ≥ 8, it means that the cooling capacity does not increase with the increase of outdoor enthalpy value, thus judging that the cooling capacity of the equipment is abnormal;

[0022] During the heating season, if the correlation coefficient is negative, that is, r < 0, score = r×(-10), and score≤-8, it means that the heating capacity does not increase as the outdoor enthalpy decreases, thus judging that the heating capacity of the equipment is abnormal.

[0023] Optionally, according to a preset change consistency scoring formula, the change consistency score between the hourly data X(t) of cooling and heating amount and the hourly data Y(t) of outdoor air enthalpy value is calculated, including:

[0024] Calculate the normalized operating curve Y(t) of the outdoor enthalpy hourly data Y(t) s , the specific formula is as follows:

[0025] when Greater than or equal to 0, then

[0026] ;

[0027] when If both are less than 0, then

[0028] ;

[0029] in, is the value of the Y(t) sequence at the i-th time point,

[0030] y L is the lower limit of enthalpy, y L = quantile( Y ,0.05),

[0031] y U is the upper limit of enthalpy value, y U = quantile( Y ,0.95),

[0032] i is the i-th time point, and the total number of time points is t;

[0033] Calculate the normalized operation curve X(t) of the hourly data X(t) of the cooling and heating supply of indoor equipments , the specific formula is as follows:

[0034] when Greater than or equal to 0, then

[0035] ;

[0036] when If both are less than 0, then

[0037] ;

[0038] in, is the value of X(t) at the i-th time point,

[0039] x L is the lower limit of heat supply, x L = quantile( X ,0.05),

[0040] x U The upper limit of heat supply is x U = quantile( X ,0.95),

[0041] i is the i-th time point, and the total number of time points is t;

[0042] Single point volatility difference D, where is the volatility difference at the i-th time point,

[0043] ;

[0044] Calculate the cumulative volatility deviation cd:

[0045] CD = sign( d 1+ d 2 +…+ d T ) . (| d 1| + |d 2| +…+ | d T | )

[0046] The single point deviation threshold With sensitivity Related

[0047] ;

[0048] Calculating volatility deviation , where T is the total duration of the sequence;

[0049] ;

[0050] in, It is the sensitivity, which can be adjusted by yourself, and the value is between 0 and 1; is the single point deviation threshold, calculated by the above formula; The integer is 0.05 times the total time length of the selected sample; The integer is 0.4 times the total time length of the selected samples.

[0051] Optionally, judging whether the indoor equipment energy consumption is abnormal according to the change consistency score between the two includes: judging whether the fluctuation trend is consistent, if | CD | > CD B , then alarm_cd = 1, which means an abnormality has occurred.

[0052] Optionally, determining the hourly data X(t) of the cooling and heating amount of the indoor equipment within the set period T includes:

[0053] The hourly data X(t) of cooling and heating supply is collected through the cooling and heating meters of the target building, and / or the hourly data X(t) of cooling and heating supply is obtained through historical data.

[0054] Optionally, determining the corresponding outdoor air enthalpy value hourly data Y(t) includes:

[0055] Get outdoor air humidity RH hourly i , outdoor air temperature value T i , and then calculate the outdoor air humidity D i , and according to the outdoor air humidity D i , the outdoor air temperature value T i Calculate the hourly value of outdoor air enthalpy Y(i), where i=1, 2, 3....t, specifically including:

[0056] ;

[0057] ;

[0058] Calculate the values ​​y(1), y(2), y(3)..., y(t) corresponding to y(i) when i=1, 2, 3...t, which constitute the hourly data Y(t) of outdoor air enthalpy.

[0059] Another object of the present invention is to provide a cooling and heating abnormality evaluation system for a target building, comprising:

[0060] The data acquisition module is used to determine the hourly data X(t) of the cooling and heating capacity of the indoor equipment within a set period T, and the corresponding hourly data Y(t) of the outdoor air enthalpy value, where X(t) and Y(t) are both 24×T-dimensional vectors;

[0061] A scoring calculation module is used to calculate the change consistency score between the hourly data X(t) of cooling and heating amount and the hourly data Y(t) of outdoor air enthalpy value according to a preset change consistency scoring formula;

[0062] The abnormality judgment module is used to judge whether the energy consumption of indoor equipment is abnormal based on the change consistency score between the two.

[0063] Another object of the present invention is to provide a computer-readable storage medium, comprising: a memory for storing computer instructions; a controller for calling and executing the computer instructions stored in the memory to implement any method in the above technical solutions.

[0064] The cooling / heating supply anomaly diagnosis method and device proposed in the present invention can assist project / building management personnel in analyzing and judging whether the actual cooling / heating conditions of the cooling / heating station are consistent with the building load demand, and help property engineers confirm whether the target building is operating related operations according to the existing operating strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure. In the drawings:

[0066] Picture 1 A schematic flow chart of a cooling and heating amount abnormality evaluation method according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments.

[0068] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0069] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0070] like Picture 1 As shown, one embodiment of the present invention is to provide a method for evaluating abnormal cooling and heating supply, the method comprising:

[0071] Determine the hourly data X(t) of the cooling and heating capacity of the indoor equipment within the set period T, and the corresponding hourly data Y(t) of the outdoor air enthalpy value, where X(t) and Y(t) are both 24×T-dimensional vectors;

[0072] According to the preset change consistency scoring formula, the change consistency score between the hourly data X(t) of cooling and heating quantity and the hourly data Y(t) of outdoor air enthalpy value is calculated;

[0073] Whether the energy consumption of indoor equipment is abnormal is determined based on the change consistency score between the two.

[0074] In one embodiment, according to a preset change consistency scoring formula, the change consistency score between the hourly data X(t) of cooling and heating amount and the hourly data Y(t) of outdoor air enthalpy value is calculated, including:

[0075] ;

[0076] In the formula, is the correlation coefficient between X(t) and Y(t), Cov(X,Y) is the covariance between X(t) and Y(t), Var[X] is the variance of X(t), and Var[Y] is the variance of Y(t);

[0077] When the correlation coefficient is positive, it means that the variables are positively correlated, and the closer the correlation coefficient is to 1, the greater the correlation;

[0078] When the correlation coefficient is negative, it means that the variables are negatively correlated;

[0079] When the correlation coefficient is 0, it means there is no linear relationship between the variables.

[0080] When r >=0, score=r×10;

[0081] When r < 0, score = r×(-10);

[0082] Among them, score is the change consistency score between the two.

[0083] Specifically, when the correlation coefficient is positive, it means that there is a positive correlation between the variables, which means that the changing trends of the cooling and heating supply and the outdoor air enthalpy value are consistent, and the closer the correlation coefficient is to 1, the greater the correlation, and the higher the consistency of the changing trends of the cooling and heating supply and the outdoor air enthalpy value; when the correlation coefficient is negative, it means that there is a negative correlation between the variables, which means that the changing trends of the cooling and heating supply and the outdoor air enthalpy value are opposite; when the correlation coefficient is 0, it means that there is no linear relationship between the variables, indicating that the changing trends of the cooling and heating supply and the outdoor air enthalpy value are inconsistent, and there is no correlation between the two.

[0084] In one embodiment, judging whether the indoor equipment energy consumption is abnormal based on the change consistency score between the two includes:

[0085] In the cooling season, if the correlation coefficient is positive, that is, r>=0, score=r×10, and score≥8 (can be adjusted according to the requirements for cooling effect), it means that the cooling capacity has not increased with the increase of outdoor enthalpy value, thus judging that the cooling capacity of the equipment is abnormal.

[0086] In the heating season, if the correlation coefficient is negative, that is, r < 0, score = r×(-10), and score ≤ -8 (can be adjusted according to the requirements for cooling effect), it means that the heating amount has not increased with the decrease of outdoor enthalpy value, thus judging that the heating capacity of the equipment is abnormal.

[0087] In another embodiment, the change consistency score between the hourly data X(t) of cooling and heating amount and the hourly data Y(t) of outdoor air enthalpy is calculated according to a preset change consistency score formula, including:

[0088] Calculate the normalized operating curve Y(t) of the outdoor enthalpy hourly data Y(t) s , the specific formula is as follows:

[0089] when Greater than or equal to 0, then

[0090] ;

[0091] when If both are less than 0, then

[0092] ;

[0093] in, is the value of the Y(t) sequence at the i-th time point,

[0094] y L is the lower limit of enthalpy, y L = quantile(Y ,0.05),

[0095] y U is the upper limit of enthalpy value, y U = quantile( Y ,0.95),

[0096] i is the i-th time point, and the total number of time points is t;

[0097] Calculate the normalized operation curve X(t) of the hourly data X(t) of the cooling and heating supply of indoor equipment s , the specific formula is as follows:

[0098] when Greater than or equal to 0, then

[0099] ;

[0100] when If both are less than 0, then

[0101] ;

[0102] in, is the value of X(t) at the i-th time point,

[0103] x L is the lower limit of heat supply, x L = quantile( X ,0.05),

[0104] x U The upper limit of heat supply is x U = quantile( X ,0.95),

[0105] i is the i-th time point, and the total number of time points is t;

[0106] The single point volatility difference D, where is the volatility difference at the i-th time point,

[0107] ;

[0108] Calculate the cumulative volatility deviation cd:

[0109] CD = sign( d 1+ d 2 +…+d T ) . (| d 1| + |d 2| +…+ | d T | )

[0110] The single point deviation threshold With sensitivity Related

[0111] ;

[0112] Calculating volatility deviation , where T is the total duration of the sequence;

[0113] ;

[0114] in, It is the sensitivity, which can be adjusted by yourself, and the value is between 0 and 1; is the single point deviation threshold, calculated by the above formula; The integer is 0.05 times the total time length of the selected sample; The integer is 0.4 times the total time length of the selected samples.

[0115] In one embodiment, judging whether the indoor equipment energy consumption is abnormal according to the change consistency score between the two includes: judging whether the fluctuation trend is consistent, if | CD | > CD B , then alarm_cd = 1, which means an abnormality has occurred.

[0116] In one embodiment, determining the hourly data X(t) of the cooling and heating amount of the indoor equipment within a set period T includes:

[0117] The hourly data X(t) of cooling and heating supply is collected through the cooling and heating meters of the target building, and / or the hourly data X(t) of cooling and heating supply is obtained through historical data.

[0118] In one embodiment, determining the corresponding outdoor air enthalpy value hourly data Y(t) includes:

[0119] Get outdoor air humidity RH hourly i , outdoor air temperature value T i , and then calculate the outdoor air humidity D i , and according to the outdoor air humidity D i , the outdoor air temperature value T iCalculate the hourly value of outdoor air enthalpy Y(i), where i=1, 2, 3....t, specifically including:

[0120] ;

[0121] ;

[0122] Calculate the values ​​y(1), y(2), y(3)..., y(t) corresponding to y(i) when i=1, 2, 3...t, which constitute the hourly data Y(t) of outdoor air enthalpy.

[0123] Another embodiment of the present invention provides a cooling and heating abnormality evaluation system for a target building, comprising:

[0124] The data acquisition module is used to determine the hourly data X(t) of the cooling and heating capacity of the indoor equipment within a set period T, and the corresponding hourly data Y(t) of the outdoor air enthalpy value, where X(t) and Y(t) are both 24×T-dimensional vectors;

[0125] A scoring calculation module is used to calculate the change consistency score between the hourly data X(t) of cooling and heating amount and the hourly data Y(t) of outdoor air enthalpy value according to a preset change consistency scoring formula;

[0126] The abnormality judgment module is used to judge whether the energy consumption of indoor equipment is abnormal based on the change consistency score between the two.

[0127] Yet another embodiment of the present invention provides a computer-readable storage medium, comprising: a memory for storing computer instructions; and a controller for calling and executing the computer instructions stored in the memory to implement any one of the methods in the above technical solutions.

[0128] In summary, the present invention proposes a method for evaluating abnormal cooling and heating amounts, which innovatively introduces a scoring calculation for the consistency of the changing characteristics of cooling and heating amounts and outdoor enthalpy values. For a given hourly cooling / heating amount of a cooling and heating source, the degree of fit between the cooling / heating amount of the equipment and the changing law of the outdoor enthalpy value is evaluated. The scoring range is 0-10, with a full score of 10. The higher the score, the higher the degree of fit between the cooling / heating amount of the equipment and the changing law of the outdoor enthalpy value. This makes it possible to evaluate the cooling / heating capacity of the building's cooling and heating sources, confirm whether they can match the changes in outdoor meteorological conditions, and give a quantitative evaluation.

[0129] It should be noted that, in this article, relational terms such as "first" and "second" 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 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 device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

Claims

1. A method for evaluating abnormal cooling and heating of a target building, characterized in that: The method comprises: Determine the hourly data X(t) of the cooling and heating capacity of the indoor equipment within the set period T, and the corresponding hourly data Y(t) of the outdoor air enthalpy value, where X(t) and Y(t) are both 24×T-dimensional vectors; According to the preset change consistency scoring formula, the change consistency score between the hourly data X(t) of cooling and heating quantity and the hourly data Y(t) of outdoor air enthalpy value is calculated; Judging whether the energy consumption of indoor equipment is abnormal according to the change consistency score between the two; The calculation of the change consistency score between the hourly data X(t) of the cooling and heating amount and the hourly data Y(t) of the outdoor air enthalpy value according to the preset change consistency score formula includes: ； In the formula, is the correlation coefficient between X(t) and Y(t), Cov(X,Y) is the covariance between X(t) and Y(t), Var[X] is the variance of X(t), and Var[Y] is the variance of Y(t); When the correlation coefficient is positive, it means that the variables are positively correlated, and the closer the correlation coefficient is to 1, the greater the correlation; When the correlation coefficient is negative, it means that the variables are negatively correlated; When the correlation coefficient is 0, it means that there is no linear relationship between the variables; When r >=0, score=r×10; When r < 0, score = r×(-10); Among them, score is the change consistency score between the two; Wherein, judging whether the energy consumption of indoor equipment is abnormal according to the change consistency score between the two includes: In the cooling season, if the correlation coefficient is positive, that is, r >= 0, score = r × 10, and score ≥ 8, it means that the cooling capacity does not increase with the increase of outdoor enthalpy value, thus judging that the cooling capacity of the equipment is abnormal; During the heating season, if the correlation coefficient is negative, that is, r < 0, score = r×-10, and score≤-8, it means that the heating capacity does not increase with the decrease of outdoor enthalpy value, thus judging that the heating capacity of the equipment is abnormal.

2. The method for evaluating abnormal cooling and heating of a target building according to claim 1, characterized in that: According to the preset change consistency scoring formula, the change consistency score between the hourly data X(t) of cooling and heating quantity and the hourly data Y(t) of outdoor air enthalpy value is calculated, including: Calculate the normalized operating curve Y(t) of the outdoor enthalpy hourly data Y(t) s , the specific formula is as follows: when Greater than or equal to 0, then ； when If both are less than 0, then ； in, is the value of the Y(t) sequence at the i-th time point, y L is the lower limit of enthalpy, y L = quantile( Y ,0.05), y U is the upper limit of enthalpy value, y U = quantile( Y ,0.95), i is the i-th time point, and the total number of time points is t; Calculate the normalized operation curve X(t) of the hourly data X(t) of the cooling and heating supply of indoor equipment s , the specific formula is as follows: when Greater than or equal to 0, then ； when If both are less than 0, then ； in, is the value of X(t) at the i-th time point, x L is the lower limit of heat supply, x L = quantile( X ,0.05), x U The upper limit of heat supply is x U = quantile( X ,0.95), i is the i-th time point, and the total number of time points is t; Single point volatility difference D, where is the volatility difference at the i-th time point, ； Calculate the cumulative volatility deviation cd , the cumulative fluctuation deviation cd represents the change consistency score of X(t) and Y(t): cd = sign( d 1+ d 2 +…+ d T ) . (| d 1| + |d 2| +…+ | d T | ) The single point deviation threshold With sensitivity Related ； Calculating volatility deviation , where T is the total duration of the sequence; ； in, It is the sensitivity, which can be adjusted by yourself, and the value is between 0 and 1; is the single point deviation threshold, calculated by the above formula; The integer is 0.05 times the total time length of the selected sample; The integer is 0.4 times the total time length of the selected samples.

3. The method for evaluating abnormal cooling and heating of a target building according to claim 2, characterized in that: Judging whether the indoor equipment energy consumption is abnormal based on the change consistency score between the two, including: judging whether the fluctuation trend is consistent, if | cd | > cd B , then alarm_cd = 1, which means an abnormality has occurred.

4. The method for evaluating abnormal cooling and heating of a target building according to any one of claims 1 to 3, characterized in that: Determine the hourly data X(t) of the cooling and heating capacity of the indoor equipment within the set period T, including: The hourly data X(t) of cooling and heating supply is collected through the cooling and heating meters of the target building, and / or the hourly data X(t) of cooling and heating supply is obtained through historical data.

5. The method for evaluating abnormal cooling and heating of a target building according to claim 4, characterized in that: The step of determining the corresponding outdoor air enthalpy value hourly data Y(t) comprises: Get outdoor air humidity RH hourly i , outdoor air temperature value T i , and then calculate the outdoor air humidity D i , and according to the outdoor air humidity D i , the outdoor air temperature value T i Calculate the hourly value of outdoor air enthalpy Y(i), where i is the i-th time point, the total number of time points is t, i=1, 2, 3....t, specifically including: ； Calculate the values ​​y(1), y(2), y(3)..., y(t) corresponding to y(i) when i=1, 2, 3...t, which constitute the hourly data Y(t) of outdoor air enthalpy.

6. A cooling and heating abnormality evaluation system for a target building, characterized in that: include: The data acquisition module is used to determine the hourly data X(t) of the cooling and heating capacity of the indoor equipment within a set period T, and the corresponding hourly data Y(t) of the outdoor air enthalpy value, where X(t) and Y(t) are both 24×T-dimensional vectors; A scoring calculation module is used to calculate the change consistency score between the hourly data X(t) of cooling and heating amount and the hourly data Y(t) of outdoor air enthalpy value according to a preset change consistency scoring formula; An abnormality judgment module, used to judge whether the energy consumption of indoor equipment is abnormal according to the change consistency score between the two; The calculation of the change consistency score between the hourly data X(t) of the cooling and heating amount and the hourly data Y(t) of the outdoor air enthalpy value according to the preset change consistency score formula includes: ； In the formula, is the correlation coefficient between X(t) and Y(t), Cov(X,Y) is the covariance between X(t) and Y(t), Var[X] is the variance of X(t), and Var[Y] is the variance of Y(t); When the correlation coefficient is positive, it means that the variables are positively correlated, and the closer the correlation coefficient is to 1, the greater the correlation; When the correlation coefficient is negative, it means that the variables are negatively correlated; When the correlation coefficient is 0, it means that there is no linear relationship between the variables; When r >=0, score=r×10; When r < 0, score = r×(-10); Among them, score is the change consistency score between the two; Wherein, judging whether the energy consumption of indoor equipment is abnormal according to the change consistency score between the two includes: In the cooling season, if the correlation coefficient is positive, that is, r >= 0, score = r × 10, and score ≥ 8, it means that the cooling capacity does not increase with the increase of outdoor enthalpy value, thus judging that the cooling capacity of the equipment is abnormal; During the heating season, if the correlation coefficient is negative, that is, r < 0, score = r×-10, and score≤-8, it means that the heating capacity does not increase with the decrease of outdoor enthalpy value, thus judging that the heating capacity of the equipment is abnormal.

7. A computer-readable storage medium, characterized in that: include: Memory, for storing computer instructions; A controller, configured to call and execute computer instructions stored in the memory to implement the method according to any one of claims 1 to 5.

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