An electric energy metering error prediction method and device and a storage medium

Abstract

The application discloses a kind of electric energy metering error prediction method, device and storage medium, wherein method includes: obtaining daily electricity data;According to daily electricity data, energy conservation balance formula is established, and energy conservation balance formula is simplified into improved energy conservation balance formula;The evolution number of class genetic algorithm is set to n, n electric energy meter is used to fit improved energy conservation balance formula at the n generation, and weighted least square method is used to solve improved energy conservation balance formula, and m electric energy meter with the greatest goodness of fit in solving result is selected to evolve next generation;After completing the last generation evolution, approximate relative error of the last generation is solved according to goodness of fit, and electric energy metering error is calculated according to approximate relative error.The application embodiment can effectively improve the accuracy of electric energy metering error prediction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric energy metering, in particular to an electric energy metering error prediction method, device and storage medium. BACKGROUND

[0002] At present, the existing electric energy metering error prediction method is usually based on an AMI (advanced metering infrastructure) calibration algorithm, and the electric energy metering error is calculated according to the electric energy metering data of a power distribution area. However, the existing electric energy metering error prediction method needs to obtain the data of the electric energy metering area in a way of assuming that the line loss rate is constant, and then predict the electric energy metering error, which leads to low accuracy of the existing electric energy metering error prediction. SUMMARY

[0003] The present application provides an electric energy metering error prediction method, device and storage medium to solve the technical problem of low accuracy of the existing electric energy metering error prediction.

[0004] One embodiment of the present application provides an electric energy metering error prediction method, comprising:

[0005] obtaining daily electricity consumption data, wherein the daily electricity consumption data comprises daily electricity consumption data of a power distribution area and daily electricity consumption data of low-voltage users;

[0006] establishing an energy conservation balance equation according to the daily electricity consumption data, and simplifying the energy conservation balance equation into an improved energy conservation balance equation;

[0007] setting the evolution generation number of a genetic algorithm to n, fitting the improved energy conservation balance equation with n electric energy meters at the n th generation, and solving the improved energy conservation balance equation by using a weighted least squares method, and selecting m electric energy meters with the largest fitting goodness from the solving results for evolution of the next generation;

[0008] after completing the evolution of the last generation, solving the approximate relative error of the last generation according to the fitting goodness, and calculating the electric energy metering error according to the approximate relative error.

[0009] Further, the improved energy conservation balance equation is:

[0010]

[0011] wherein dy is the loss of electricity consumption, φ j is the electricity consumption of the electric energy meter j, e j is the approximate relative error of the electric energy meter j, e0 is the fixed loss, U j is the voltage of the electric energy meter j, and β jkβ is a line loss coefficient of the electric energy meter j and the electric energy meter k com β is a line loss coefficient of the line loss general term.

[0012] Further, the electric energy meter measurement error is calculated according to the approximate relative error, and the calculation comprises:

[0013] The electric energy meter measurement error is:

[0014]

[0015] wherein e j (i) is the approximate relative error, e′ j (i) is the electric energy meter measurement error.

[0016] Further, the evolution generation number of the set genetic algorithm is n, n electric energy meters are used to fit the energy conservation balance formula at the n th generation, the weighted least square method is used to solve the energy conservation balance formula, and m electric energy meters with the largest fitting degree are selected for evolution of the next generation, and the calculation comprises:

[0017] The evolution generation number of the set genetic algorithm is 5, 1 electric energy meter is used to fit the energy conservation balance formula at the first generation evolution, P fitting formulas are obtained, the weighted least square method is used to solve the P fitting formulas, and the solving results are sorted from large to small according to the fitting degree value, and the top 10 electric energy meters are selected for evolution of the next generation, until the fifth generation evolution ends.

[0018] Further, the energy conservation balance formula is established according to the daily electric quantity data, and the calculation comprises:

[0019] The daily electric quantity data is denoised to obtain first pretreatment data;

[0020] Error values are deleted from the first pretreatment data to obtain second pretreatment data;

[0021] The energy conservation balance formula is established according to the second pretreatment data.

[0022] Further, the daily electric quantity data is denoised to obtain first pretreatment data, and the calculation comprises:

[0023] The null electric quantity in the transformer area electric energy meter is processed by 0 filling, and data exceeding a preset line loss threshold range in the daily electric quantity data is removed to obtain the first pretreatment data.

[0024] Further, error values are deleted from the first pretreatment data to obtain second pretreatment data, and the calculation comprises:

[0025] Judge whether there is any non-zero data of the electric energy meter in the first preprocessed data is less than 10% of the entire electric energy meter data, if yes, delete the non-zero data of the electric energy meter, and obtain second preprocessed data.

[0026] One embodiment of the present application provides an electric energy meter measurement error prediction device, comprising:

[0027] A data acquisition module is configured to acquire daily electricity consumption data, wherein the daily electricity consumption data comprises daily electricity consumption data of a transformer area and daily electricity consumption data of low-voltage users.

[0028] A formula simplification module is configured to establish an energy conservation balance formula according to the daily electricity consumption data, and simplify the energy conservation balance formula into an improved energy conservation balance formula.

[0029] A genetic solution module is configured to set the evolution generation number of a genetic algorithm to n, fit the improved energy conservation balance formula with n electric energy meters at the n-th generation, solve the improved energy conservation balance formula by using a weighted least square method, and select m electric energy meters with the best fitting degree in the solution result for evolution of the next generation.

[0030] A measurement error calculation module is configured to obtain an approximate relative error of the last generation by solving the fitting degree after completing the evolution of the last generation, and calculate the electric energy meter measurement error according to the approximate relative error.

[0031] One embodiment of the present application provides a computer readable storage medium, which comprises a stored computer program, wherein the computer readable storage medium controls a device where the computer readable storage medium is located to execute an electric energy meter measurement error prediction method as described above when the computer program runs.

[0032] The genetic algorithm is set to have n evolution generations, n electric energy meters are used to fit the improved energy conservation balance formula at the n-th generation, the improved energy conservation balance formula is solved by using a weighted least square method, m electric energy meters with the best fitting degree in the solution result are selected for evolution of the next generation, an approximate relative error of the last generation is obtained by solving the fitting degree after completing the evolution of the last generation, and the electric energy meter measurement error is calculated according to the approximate relative error, so that not only the interference caused by error-free electric energy meter data can be quickly filtered out, but also the weight of current data can be effectively improved and the weight of historical data can be reduced by using the solution of the weighted least square method, so that the solution result can adapt to the gradual change process of the electric energy meter error from small to large, and the accuracy of the electric energy meter measurement error prediction can be effectively improved. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1is a flowchart of an electric energy metering error prediction method provided by an embodiment of the present application;

[0034] Figure 2 is another flowchart of an electric energy metering error prediction method provided by an embodiment of the present application;

[0035] Figure 3 is a structural diagram of an electric energy metering error prediction device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0036] The technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0037] In the description of the present application, it should be understood that the terms "first", "second" are used only for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0038] In the description of the present application, it should be noted that, unless otherwise specified and limited, the terms "mounting", "connection" and "connection" should be understood in a broad sense, for example, it can be fixed connection, or detachable connection, or integral connection; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0039] Please refer to Figures 1-2 In the first embodiment of the present application, an electric energy metering error prediction method as shown in the figure is provided, which comprises: Figure 1

[0040] S1, obtaining daily electricity data, the daily electricity data comprising daily electricity data of a transformer area and daily electricity data of low-voltage users;

[0041] In the embodiment of the present application, the daily electricity data is obtained by means of household variable relationship topology extraction, wherein the extracted data days are set to the maximum of 1.8 times or 300 of the transformer area scale, so as to try to meet the full rank matrix of effective data column.

[0042] ​S2. Establish an energy conservation balance formula based on daily electricity consumption data, and simplify the energy conservation balance formula into an improved energy conservation balance formula.

[0043] In this embodiment of the invention, the energy conservation equilibrium formula is established as follows:

[0044] According to the physical topology of the transformer substation, all electricity users in the substation rely on this transformer for their electricity. The electricity metering device for the entire substation consists of a substation master meter and several user meters. The substation master meter is used to measure the total amount of electricity provided by the transformer, and the user meters are used to measure the electricity consumption of each user in the substation. The relationship between the substation master meter and the user meters forms a simple tree topology.

[0045] Based on the tree topology and the law of conservation of energy, the total mathematical relationship of the electricity meter readings can be obtained as follows: "Total power supply of the total meter in the distribution area" = "Sum of electricity consumption of each user" + "Line loss" + "Fixed losses such as the electricity meter's own energy consumption".

[0046] Assume there are P users (metering points) in the transformer area. Define y(i) as the power supply of the transformer area's main power meter during metering period i, define y′(i) as the actual consumption value of all users in the transformer area during metering period i, and define X... j (i) represents the actual consumption value of meter j in metering area i, then:

[0047]

[0048] Define e0(i) as the total fixed power loss of the transformer area within metering period i, and define e y (i) represents the line loss rate of the transformer substation within metering period i. Line loss is the remaining portion of the total power supply of the transformer substation after deducting the actual power consumption of all users and the fixed power loss of the substation. Therefore, we can obtain:

[0049]

[0050] Substituting formula (1) into formula (2) and rearranging the terms, we get:

[0051]

[0052] Further transformation yields:

[0053]

[0054] Formula (4) above is the equation for the total electricity metering relationship of this distribution area.

[0055] To more accurately estimate the actual line loss, this embodiment of the invention estimates the line loss using the quadratic term of the meter reading, based on Kirchhoff's laws.

[0056] The expression for line loss is:

[0057]

[0058] β jk It is proportional to the resistance term, and we will temporarily call it the line loss coefficient, i.e.

[0059]

[0060] β jk The number of terms is related to the number of quadratic combinations of the electricity meter, and there are a total of If P is 100, there will be 5050 quadratic terms, i.e., 5050 line loss coefficients. Since there are too many line loss coefficients, this embodiment of the invention estimates the M largest terms separately, while estimating the other terms using a unified term.

[0061] Where M is 15, and there are M individual estimated terms:

[0062]

[0063] Its corresponding 15 line loss coefficients β jk .

[0064] The format for a uniform item is:

[0065]

[0066] It corresponds to a line loss coefficient β com .

[0067] The simplified quadratic term has 16 line loss coefficients, meaning there are 16 more unknowns.

[0068] In summary, the energy conservation equilibrium formula is:

[0069]

[0070] For the energy conservation equations, we need to solve for P+17 variables, where P is the error of the electricity meter, 16 line loss coefficients, and fixed power loss.

[0071] Furthermore, by eliminating the constant term from the right-hand side of the above energy conservation equilibrium formula and simplifying, we obtain:

[0072]

[0073] In this embodiment of the invention, considering that the meter error in the line loss term is approximately 0 and the power factor is approximately 1, the above simplified formula is further simplified as follows:

[0074]

[0075] Where dy is the power loss, φ j Let e ​​be the amount of electricity measured by meter j. j Let e0 be the approximate relative error of the electricity meter j, and e0 be the fixed loss. j Let β be the voltage of the electricity meter j. jk Let β be the line loss coefficient for electricity meters j and k. com This is the line loss coefficient for the general line loss item.

[0076] S3. Set the number of generations of the genetic algorithm to n. In the nth generation, use n energy meters to fit the improved energy conservation balance formula, and use the weighted least squares method to solve the improved energy conservation balance formula. Select the m energy meters with the best fit from the solution results to evolve the next generation.

[0077] It is understandable that, generally speaking, only a small number of electricity meters (T) in a distribution area have errors, while the errors of other electricity meters tend to be zero. This embodiment of the invention, based on a simplified energy conservation balance formula, reduces the number of variables by assuming a value of T, from P+17 variables to T+17 variables, thus making the equations easier to solve. Specifically, the simplified energy conservation balance formula is transformed into:

[0078]

[0079] In this embodiment of the invention, a genetic algorithm is used to search for the range of suitable variables, and a weighted least squares method is used to solve for the optimal combination of m electricity meters.

[0080] S4. After completing the final generation of evolution, the approximate relative error of the final generation is obtained by solving the goodness of fit, and the metering error of the electricity meter is calculated based on the approximate relative error.

[0081] In one embodiment, the metering error of the electricity meter is calculated based on the approximate relative error, including:

[0082] The metering error of the electricity meter is:

[0083]

[0084] Among them, e j (i) represents the approximate relative error, e′ j (i) represents the metering error of the electricity meter.

[0085] In one embodiment, the evolutionary generation of the genetic algorithm is set to n. In the nth generation, n energy meters are used to fit the energy conservation balance formula, and the weighted least squares method is used to solve the energy conservation balance formula. From the solution results, the m energy meters with the highest goodness of fit are selected for the next generation of evolution, including:

[0086] The genetic algorithm is set to have 5 generations. In the first generation, one energy meter is used to fit the energy conservation balance formula to obtain P fitting formulas. The weighted least squares method is used to solve the P fitting formulas. The solution results are sorted from largest to smallest according to the goodness of fit. The top 10 energy meters are selected for the second generation, and so on, until the fifth generation is completed.

[0087] Specifically, when using one electricity meter (i.e., T=1), the solution variables for the energy conservation balance formula are [e j 、e0、β jk (1-15), β com The fitting result Y is the loss variable dy, and the matrix is ​​constructed according to the following formula:

[0088]

[0089] The weighted least squares method is used to solve the problem, and the goodness-of-fit R-squared is used to evaluate the solution process. 2 An evaluation was conducted, and the top 10 energy meters with the highest goodness of fit were used to evolve the second generation.

[0090] In the second generation, two energy meters (i.e., T=2) were used based on the genetic evolution results of the first 10 energy meters from the first generation. There are a total of 45 possible combinations.

[0091] The combination of every two energy meters j and h forms the following formula:

[0092]

[0093] The weighted least squares method is used to solve the problem, and the goodness-of-fit R is used as the criterion. 2 As a judging criterion, the meters were sorted from largest to smallest, and the top 10 pairs of meters were used to evolve the third generation.

[0094] In this way, the optimal 10 combinations of electricity meters are obtained in each generation for the next generation of evolution, until the fifth generation of evolution ends.

[0095] In one embodiment, an energy conservation balance formula is established based on daily electricity consumption data, including:

[0096] The daily electricity consumption data is denoised to obtain the first preprocessed data;

[0097] Error values ​​are removed from the first preprocessed data to obtain the second preprocessed data;

[0098] An energy conservation equilibrium formula is established based on the second preprocessed data.

[0099] In one embodiment, denoising the daily electricity consumption data to obtain first preprocessed data includes:

[0100] The null values ​​in the electricity meters of the distribution area are filled with zeros, and the data in the daily electricity data that exceed the preset line loss threshold are removed to obtain the first preprocessed data.

[0101] In this embodiment of the invention, the acquired daily electricity consumption data is data with a 100% collection rate of the distribution area. The control electricity consumption data of this data is padded with zeros, and a preset line loss threshold range is introduced. Data in the daily electricity consumption data that exceeds the preset line loss threshold range is removed, and data from low-power meters is retained, thereby making the acquired daily electricity consumption data more accurate.

[0102] In one embodiment, error value removal is performed on the first preprocessed data to obtain the second preprocessed data, including:

[0103] Determine whether there are any non-zero data from any electricity meter in the first preprocessed data that is less than 10% of the total electricity meter data. If so, delete the non-zero data from the electricity meter to obtain the second preprocessed data.

[0104] In this embodiment of the invention, deleting columns and rows from tables with too many zero values ​​can further improve the accuracy of daily electricity consumption data.

[0105] In an embodiment of the present invention,

[0106] Implementing the embodiments of the present invention has the following beneficial effects:

[0107] In this embodiment of the invention, the evolutionary generation of the genetic algorithm is set to n. In the nth generation, n energy meters are used to fit the improved energy conservation balance formula, and the weighted least squares method is used to solve the improved energy conservation balance formula. From the solution results, the m energy meters with the highest goodness of fit are selected for the next generation of evolution. After the last generation of evolution is completed, the approximate relative error of the last generation is obtained based on the goodness of fit. The metering error of the energy meter is calculated based on the approximate relative error. This not only can quickly filter out the interference brought by the data of error-free energy meters, but also, by using the weighted least squares method, it can effectively increase the weight of the current data and decrease the weight of the historical data. This allows the solution results to adapt to the gradual process of the energy meter error from small to large, thereby effectively improving the accuracy of the metering error prediction.

[0108] Please see Figure 3 Based on the same inventive concept as the above embodiments, one embodiment of the present invention provides an electricity meter metering error prediction device, comprising:

[0109] Data acquisition module 10 is used to acquire daily power consumption data, which includes daily power consumption data of the transformer area and daily power consumption data of low-voltage users;

[0110] Formula simplification module 20 is used to establish an energy conservation balance formula based on daily electricity data, and to simplify the energy conservation balance formula into an improved energy conservation balance formula.

[0111] The genetic solution module 30 is used to set the number of generations of the genetic algorithm to n. In the nth generation, n energy meters are used to fit the improved energy conservation balance formula, and the weighted least squares method is used to solve the improved energy conservation balance formula. In the solution results, the m energy meters with the highest goodness of fit are selected for the next generation.

[0112] The metering error calculation module 40 is used to obtain the approximate relative error of the last generation based on the goodness of fit after the last generation evolution is completed, and to calculate the metering error of the electricity meter based on the approximate relative error.

[0113] In one embodiment, the improved energy conservation equilibrium formula is:

[0114]

[0115] Where dy is the power loss, φ j Let e ​​be the amount of electricity measured by meter j. j Let e0 be the approximate relative error of the electricity meter j, and e0 be the fixed loss. j Let β be the voltage of the electricity meter j. jk Let β be the line loss coefficient for electricity meters j and k. com This is the line loss coefficient for the general line loss item.

[0116] In one embodiment, the measurement error calculation module 40 is used for:

[0117] The metering error of the electricity meter is:

[0118]

[0119] Among them, e j (i) represents the approximate relative error, e′ j (i) represents the metering error of the electricity meter.

[0120] In one embodiment, the genetic solver module 30 includes components for:

[0121] The genetic algorithm is set to have 5 generations. In the first generation, one energy meter is used to fit the energy conservation balance formula to obtain P fitting formulas. The weighted least squares method is used to solve the P fitting formulas. The solution results are sorted from largest to smallest according to the goodness of fit. The top 10 energy meters are selected for the second generation, and so on, until the fifth generation is completed.

[0122] In one embodiment, the apparatus further includes a data preprocessing module, used for:

[0123] The daily electricity consumption data is denoised to obtain the first preprocessed data;

[0124] Error values ​​are removed from the first preprocessed data to obtain the second preprocessed data;

[0125] An energy conservation equilibrium formula is established based on the second preprocessed data.

[0126] In one embodiment, denoising the daily electricity consumption data to obtain first preprocessed data includes:

[0127] The null values ​​in the electricity meters of the distribution area are filled with zeros, and the data in the daily electricity data that exceed the preset line loss threshold are removed to obtain the first preprocessed data.

[0128] In one embodiment, error value removal is performed on the first preprocessed data to obtain the second preprocessed data, including:

[0129] Determine whether there are any non-zero data from any electricity meter in the first preprocessed data that is less than 10% of the total electricity meter data. If so, delete the non-zero data from the electricity meter to obtain the second preprocessed data.

[0130] One embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the above-described method for predicting the metering error of an electricity meter.

[0131] The above are preferred embodiments of the present invention. It should be noted that, for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for predicting metering errors of an electric energy meter, characterized in that, The method comprises the following steps: obtaining daily electricity data, wherein the daily electricity data comprises daily electricity data of a transformer area and daily electricity data of low-voltage users; establishing an energy conservation balance formula according to the daily electricity data, and simplifying the energy conservation balance formula into an improved energy conservation balance formula; the improved energy conservation balance formula is: Wherein, dy is the loss of electricity, φ j is the electricity of the electric energy meter j, e j is the approximate relative error of the electric energy meter j, e0 is the fixed loss, U j is the voltage of the electric energy meter j, β jk is the line loss coefficient of the electric energy meter j and the electric energy meter k, β com is the line loss coefficient of the line loss general term; setting the evolutional generation number of a genetic algorithm as n, fitting the improved energy conservation balance formula by using n electric energy meters at the n-th generation, and solving the improved energy conservation balance formula by using a weighted least square method, and selecting m electric energy meters with the best fitting degree in the solving result for evolution of the next generation; after completing the evolution of the last generation, obtaining an approximate relative error of the last generation according to the fitting degree, and calculating an electric energy metering error according to the approximate relative error.

2. The method of claim 1, wherein, The method for calculating the electric energy metering error according to the approximate relative error comprises the following steps: the electric energy metering error is: where e j (i) is the approximate relative error, e′ j (i) is the electric energy metering error.

3. The method of claim 1, wherein, The method for setting the evolutional generation number of the genetic algorithm as n, fitting the energy conservation balance formula by using n electric energy meters at the n-th generation, and solving the energy conservation balance formula by using the weighted least square method, and selecting m electric energy meters with the best fitting degree in the solving result for evolution of the next generation comprises the following steps: setting the evolutional generation number of the genetic algorithm as 5, fitting the energy conservation balance formula by using one electric energy meter at the first generation to obtain P fitting formulas, sorting the solving result from large to small according to the fitting degree value after solving the P fitting formulas by using the weighted least square method, and selecting the first 10 electric energy meters for evolution of the next generation until the fifth generation evolution is completed.

4. The method of claim 1, wherein, The method for establishing the energy conservation balance formula according to the daily electricity data comprises the following steps: performing denoising processing on the daily electricity data to obtain first pretreatment data; performing error value deletion on the first pretreatment data to obtain second pretreatment data; establishing the energy conservation balance formula according to the second pretreatment data.

5. The method of claim 4, wherein, The method for performing denoising processing on the daily electricity data to obtain first pretreatment data comprises the following steps: performing 0 filling processing on null electricity in the transformer area electric energy meter, and deleting data exceeding a preset line loss threshold range in the daily electricity data to obtain the first pretreatment data.

6. The method of claim 5, wherein, The method for performing error value deletion on the first pretreatment data to obtain second pretreatment data comprises the following steps: judging whether non-zero data of any electric energy meter in the first pretreatment data is lower than 10% of the entire electric energy meter data, and if yes, deleting the non-zero data of the electric energy meter to obtain the second pretreatment data.

7. An electric energy metering error prediction device, characterized by, The method comprises the following steps: a data acquisition module is configured to obtain daily electricity data, wherein the daily electricity data comprises daily electricity data of a transformer area and daily electricity data of low-voltage users; a formula simplification module is configured to establish an energy conservation balance formula according to the daily electricity data, and simplify the energy conservation balance formula into an improved energy conservation balance formula; the improved energy conservation balance formula is: wherein dy is the loss power, φ j is the power of the electric energy meter j, e j is the approximate relative error of the electric energy meter j, e0 is the fixed loss, U j is the voltage of the electric energy meter j, β jk is the line loss coefficient of the electric energy meter j and the electric energy meter k, β com is the line loss coefficient of the line loss general term; The genetic solving module is configured to set the evolutional generation number of the genetic algorithm as n, fit the improved energy conservation balance formula by n electric energy meters at the n-th generation, and solve the improved energy conservation balance formula by using the weighted least square method, and select m electric energy meters with the best fitting goodness from the solving results for evolution of the next generation; The metrological error calculating module is configured to obtain the approximate relative error of the last generation according to the fitting goodness after completing the evolution of the last generation, and calculate the electric energy meter metrological error according to the approximate relative error.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored computer program, wherein the computer readable storage medium controls the device where the computer readable storage medium is located to execute the electric energy meter metrological error prediction method according to any one of claims 1 to 6 when the computer program runs.

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