Enterprise-level carbon emission calculation method, device and medium based on power data correlation coefficient interpolation

Abstract

The present invention provides a method, device and medium for calculating enterprise-level carbon emissions based on the interpolation of the correlation coefficient of electricity data. The method uses the correlation coefficient between energy consumption data as a reference standard, and interpolates and completes the missing energy consumption and industrial process data based on the electricity data, wherein the accuracy of the interpolation is measured by the Spearman correlation coefficient, and the interpolation result close to the true value is found and interpolated to the appropriate position. The calculation of the full carbon emissions of the enterprise is realized based on the final completed data. The estimation result obtained by this method can retain the correlation between various types of historical data to the greatest extent, which not only fully utilizes the quality advantages of the enterprise's electricity data, but also improves the accuracy of the estimated data, and provides a practical and feasible method for calculating the full carbon emissions of the enterprise.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission calculation technology, and in particular to an enterprise-level carbon emission calculation method, device and medium based on power data correlation coefficient interpolation. Background Art

[0002] Currently, the calculation methods for carbon emissions require enterprises to have complete historical data on energy consumption and industrial production processes. The main methods used include emission factor method, mass balance method and actual measurement method.

[0003] The emission factor method, based on the IPCC's basic carbon accounting equation, calculates greenhouse gas emissions by multiplying activity data (AD) by an emission factor (EF). This method is the most widely used and widely used. This method is subject to significant uncertainty due to factors such as technological advancement and process flow, and is primarily used when socioeconomic emission sources are relatively stable and natural emission sources are not complex.

[0004] The mass balance method calculates carbon emissions based on the difference between input and output carbon content. It is primarily suitable for reflecting actual emissions at the location where they occur and can distinguish differences between various types of facilities. This method is labor-intensive and requires the collection of detailed industrial process data and a comprehensive understanding of production techniques. It is primarily used in industries with a well-established data base.

[0005] The field measurement method, based on measured data from emission sources, aggregates relevant carbon emissions. This method, which includes both on-site and off-site measurements, directly reflects a company's actual emissions. This method consumes significant manpower and material resources, is costly, and requires representative samples. It is primarily used for natural emission sources in small areas where primary monitoring data is available.

[0006] However, in reality, in addition to electricity consumption data, the classification of other types of energy consumption or industrial process data among different enterprises varies, and the data quality is also uneven, which creates certain difficulties for corporate carbon verification and carbon inventory; among them, other types of energy consumption or industrial process data include coal, oil, water, gas, product output and other data.

[0007] Because companies have relatively complete electricity consumption data, some methods propose using electricity data to supplement historical data and then calculating the company's total carbon emissions using carbon emission factors. However, current data supplementation methods primarily split other energy consumption amounts proportionally based on the proportion of electricity data over time. While this yields other energy consumption amounts that are comparable in dimension to the electricity data, the energy consumption data estimated by this method is highly correlated with the electricity data. However, the degree of correlation between different data types and electricity data inevitably varies, leading to significant discrepancies between the estimated energy consumption data and actual conditions. Summary of the Invention

[0008] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0009] To this end, a first aspect of the present invention provides an enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients.

[0010] A second aspect of the present invention provides a computer device.

[0011] A third aspect of the present invention provides a computer-readable storage medium.

[0012] The present invention provides an enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation, comprising the following steps:

[0013] S1. Define the energy consumption or industrial process data group to be completed within the preset time period as P m , where the data set P m Contains m historical data sorted by time;

[0014] S2. Obtain known historical power data group Q within a preset time period n , where the data set Q n Contains n historical data; according to the data group P m The m time values ​​corresponding to each data in the data set Q are selected. n The corresponding power data in forms an ordered data group Q m ;

[0015] S3. Calculate data set P m and Q m The Spearman correlation coefficient r m ;

[0016] S4. In data group Q n Select one that does not belong to the data set Q m Data q t , the data q t Insert data group Q in chronological order m , forming a data set Q m+1 , refer to data set Q m+1 Interpolation position, in data set P m On the basis of the corresponding interpolation position, the data group P is formed. m+1 ;

[0017] S5. According to data group P m According to the values ​​of , several optional interpolation values ​​are determined to form an interpolation selection set S.

[0018] S6. Insert any data in the interpolation selection set into the data set P. m+1The interpolation position reserved in the data set P is calculated after interpolation. m+1 With data set Q m+1 The Pearlman correlation coefficient r′ k , insert all the data in the interpolation selection set into the data set P in sequence m+1 , get the corresponding Spearman correlation coefficient r′ k , forming the Spearman correlation coefficient set R′;

[0019] S7, select the Spearman correlation coefficient set R' and r m The closest element, with optional interpolation, as the data set P m The optimal interpolation of P m+1 The interpolation position reserved in the data set P is determined as m+1 ;

[0020] S8, P m+1 and Q m+1 As a new P m and Q m Repeat steps S3 to S8 to continue the data set P m Interpolate until P in the data set m and data set Q n The number of elements in is equal;

[0021] S9. Convert the completed energy consumption or industrial process data using the carbon emission factor to ultimately calculate the company's total carbon emissions.

[0022] The enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients according to the above technical solution of the present invention may also have the following additional technical features:

[0023] In the above technical solution, the amount of energy consumption or industrial process data to be completed within the preset time period is smaller than the amount of known historical power data within the preset time period, that is, m is smaller than n.

[0024] In the above technical solution, the known data of energy consumption or industrial process has corresponding historical power data at the same time node.

[0025] In the above technical solution, in step S4, in the data group Q n Select one that does not belong to the data set Q m Data q t When data q t It is the earliest data corresponding to the time node among the optional data.

[0026] In the above technical solution, in step S5, the data set P mAccording to the values ​​of , several optional interpolation values ​​are determined to form an interpolation selection set S, including:

[0027] P m Arrange them according to the value to form a new data group P' m ;

[0028] Calculate the data set P′ n The average value of every two adjacent data in , and the calculated average values ​​are used as optional interpolation values.

[0029] In the above technical solution, the optional interpolation in the interpolation selection set also includes the data set P′ m The first and last items in the final interpolation selection set S m+1 Contains m+1 elements.

[0030] In the above technical solution, the data set P′ m For data set P m Arrange in ascending order according to the value;

[0031] Interpolation selection set S m+1 The values ​​are sorted in ascending order.

[0032] In the above technical solution, the method for calculating the Spearman correlation coefficient includes:

[0033] r=1-(6×Σd 2 ) / (g×(g 2 -1))

[0034] Where r represents the Spearman correlation coefficient, ∑d 2 represents the sum of squares of the rank differences between two data sets, and g represents the number of elements in the data sets.

[0035] The present invention provides a computer device comprising a processor and a memory, wherein the memory stores a computer program. When the computer program is loaded and executed by the processor, an enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients as described in any one of the above technical solutions is implemented.

[0036] The present invention provides a computer-readable storage medium storing a program, which, when loaded by a processor, implements the enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients as described in any one of the above technical solutions.

[0037] In summary, due to the adoption of the above technical features, the beneficial effects of the present invention are:

[0038] This paper proposes a method for calculating a company's full carbon emissions by interpolating missing energy consumption and industrial process data based on electricity data, using the correlation coefficient between energy consumption data as a reference standard. This method maximizes the correlation between various historical data types, leveraging the advantages of the company's electricity data quality while improving the accuracy of the estimated data. This provides a practical and feasible approach for calculating a company's full carbon emissions.

[0039] Additional aspects and advantages of the invention will become apparent from the description which follows, or may be learned by practice of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments with reference to the following drawings, in which:

[0041] Figure 1 The present invention is a flowchart of an enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that, in the absence of conflict, the embodiments of the present application and the features therein can be combined with each other.

[0043] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0044] Refer to the following Figure 1 The following describes an enterprise-level carbon emission calculation method, device, and medium based on power data correlation coefficient interpolation according to some embodiments of the present invention.

[0045] Some embodiments of the present application provide an enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients.

[0046] like Figure 1 As shown, the first embodiment of the present invention proposes an enterprise-level carbon emissions calculation method based on interpolation of power data correlation coefficients, including the following steps S1 to S9. It should be noted that the order of steps S1 to S9 is only schematic, and those skilled in the art may adjust the order of the steps as needed, and steps of different orders may be executed simultaneously.

[0047] S1. Define the energy consumption or industrial process data group to be completed within the preset time period as Pm , where the data set P m Contains m historical data sorted by time, P m ={p1,p2,...,p m}.

[0048] Specifically, it is necessary to first determine the historical energy consumption or industrial process data items that need to be completed, and form a data group P based on the existing data (if there are any missing data) m It is understandable that the energy consumption or industrial process data here can be data on coal, oil, water, gas or product output, and such data are all correlated with the power data. m It is an ordered set sorted by time. The preset time period is the specified time period.

[0049] S2. Obtain known historical power data group Q within a preset time period n , where the data set Q n Contains n historical data; according to the data group P m The m time values ​​corresponding to each data in the data set Q are selected. n The corresponding power data in forms an ordered data group Q m , Q m ={q1,q2,...,q m}.

[0050] It is understandable that power data is more comprehensive, that is, compared with energy consumption or industrial process data, power data has more data and is more complete. The same above conditions should also be the basis for judging whether power data can be used to supplement energy consumption or industrial process data. n The number n of historical data indicates that energy consumption or industrial process data should also have n data. The present disclosure is a method for completing the difference between m and n, that is, in the present disclosure, n can be used as the upper limit of interpolation completion data.

[0051] Specifically, the amount of energy consumption or industrial process data to be completed within a preset time period is less than the amount of known historical power data within the preset time period, that is, m is less than n. Furthermore, the known energy consumption or industrial process data has corresponding historical power data at the same time point.

[0052] S3. Calculate data set P m and Q m Spearman correlation coefficient (SPEARMAN correlation coefficient) r m In this disclosure, the Spearman correlation coefficient is used to measure the correlation between energy consumption or industrial process data and power data, r m Represents the data set P m and Qm The initial correlation of the data is used as the benchmark for subsequent interpolation operations. The closer the Spearman correlation coefficient recalculated after interpolation is to the initial coefficient, the closer the interpolation is to the real data.

[0053] Specifically, the calculation method of the Spearman correlation coefficient includes:

[0054] r=1-(6×∑d 2 ) / (g×(g 2 -1))

[0055] Where r represents the Spearman correlation coefficient, ∑d 2 represents the sum of squares of the rank differences between two data sets, and g represents the number of elements in the data sets. The rank difference is the difference between two elements with the same rank in two arrays arranged in the same order.

[0056] S4. In data group Q n Select one that does not belong to the data set Q m Data q t , that is, q t ∈Q n ,and The data q t Insert data group Q in chronological order m , forming a data set Q m+1 , refer to data set Q m+1 Interpolation position, in data set P m On the basis of the corresponding interpolation position, the data group P is formed. m+1 .

[0057] It is understandable that the data q t When inserting data into the data group in time order, data q t The corresponding time node is earlier than the data group Q m When all the data in , the data q t Insert into data group Q m Similarly, data q t The corresponding time node is later than the data group Q m When all the data in , the data q t Insert into data group Q m The last column of the data set Q m Some of the corresponding time nodes are earlier than the data q t The corresponding time nodes are partially later than the data q t The corresponding time node, then the data q t Insert it into the appropriate position.

[0058] In a specific embodiment, to simplify the calculation, according to the execution logic of the computer, in the data group Q n Select one that does not belong to the data set Q m Data q t When data q t It is the earliest data corresponding to the time node among the optional data.

[0059] S5. According to data group P m According to the size of each value in, several optional interpolation values ​​are determined to form an interpolation selection set S.

[0060] The number of optional interpolation values ​​can be one or more, but should conform to the data set P m That is to say, the optional interpolation is determined by utilizing the numerical characteristics of the energy consumption or industrial process data items themselves. The optional interpolation can be selected by means of average value, random number selection within the interval, etc.

[0061] In some embodiments, step S5 includes:

[0062] P m Arrange them according to the value to form a new data group P' m ;

[0063] Calculate the data set P′ m The average value of every two adjacent data in , and the calculated average values ​​are used as optional interpolation values.

[0064] In a specific embodiment, in addition to the optional interpolation determined according to the average value of adjacent data, the optional interpolation in the interpolation selection set also includes the data group P′ m The first and last items in the final interpolation selection set S m+1 Contains m+1 elements.

[0065] It should be noted that, in the present disclosure, the arrangement order of each ordered series can be selected in ascending or descending order as needed.

[0066] In one embodiment, the data set P' m For data set P m Arrange in ascending order according to the value; interpolation selection set S m+1 The values ​​are also sorted in ascending order.

[0067] S6. Insert any data in the interpolation selection set into the data set P. m+1 The interpolation position reserved in the data set P is calculated after interpolation. m+1 With data set Q m+1 The Pearlman correlation coefficient r′ k , insert all the data in the interpolation selection set into the data set P in sequencem+1 , get the corresponding Spearman correlation coefficient r′ k , forming the Spearman correlation coefficient set R′.

[0068] Then the number of elements in the Spearman correlation coefficient set R′ is equal to the number of optional interpolations in the interpolation selection set. That is, the interpolation selection set is the above S m+1 When S m+1 Each number s k , k=1,2,…,m+1, put into data group P m+1 The interpolation position reserved in p t , and calculate P under the current k value m+1 With Q m+1 The Spearman correlation coefficient r′ k , all the results form the Spearman coefficient set R′ m+1 ={r′1,r′2,…,r′ m+1}.

[0069] S7, select the Spearman correlation coefficient set R' and r m The closest element, with optional interpolation, as the data set P m The optimal interpolation of P m+1 The interpolation position reserved in the data set P is determined as m+1 , that is, the optimal interpolation is closest to the real data. Inserting the optimal interpolation into the sequence corresponding to the appropriate time node can better restore the energy consumption or industrial process data.

[0070] S8, P m+1 and Q m+1 As a new P m and Q m Repeat steps S3 to S8 to continue the data set P m Interpolate until P in the data set m and data set Q n The number of elements in are equal.

[0071] S9. Convert the completed energy consumption or industrial process data using the carbon emission factor to ultimately calculate the company's total carbon emissions.

[0072] Specifically, the above steps can complete the completion of energy consumption or industrial process data. After completing all energy data related to the company's carbon emissions accounting, the company's total carbon emissions are calculated by converting it using the nationally published carbon emission factors.

[0073] Among them, the method of calculating carbon emissions based on the carbon emission factor is well known to those skilled in the art, and its specific process is not the improvement point of the present disclosure, so it will not be repeated here.

[0074] Other embodiments of the present invention provide a computer device comprising a processor and a memory, wherein the memory stores a computer program, and when the computer program is loaded and executed by the processor, an enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients as described in any of the above embodiments is implemented.

[0075] Still other embodiments of the present invention provide a computer-readable storage medium storing a program, which, when loaded by a processor, implements the enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients as described in any of the above embodiments.

[0076] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples.

[0077] Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for calculating enterprise-level carbon emissions based on interpolation of power data correlation coefficients, characterized in that: The following steps are involved: S1. Define the energy consumption or industrial process data group to be completed within the preset time period as P m , where the data set P m Contains m historical data sorted by time; S2. Obtain known historical power data group Q within a preset time period n , where the data set Q n Contains n historical data; according to the data group P m The m time values ​​corresponding to each data in the data set Q are selected. n The corresponding power data in forms an ordered data group Q m ; S3. Calculate data set P m and Q m The Spearman correlation coefficient r m ; S4. In data group Q n Select one that does not belong to the data set Q m Data q t , the data q t Insert data group Q in chronological order m , forming a data set Q m+1 , refer to data set Q m+1 Interpolation position, in data set P m On the basis of the corresponding interpolation position, the data group P is formed. m+1 ; S5. According to data group P m According to the values ​​of , several optional interpolation values ​​are determined to form an interpolation selection set S. S6. Insert any data in the interpolation selection set into the data set P. m+1 The interpolation position reserved in the data set P is calculated after interpolation. m+1 With data set Q m+1 The Pearlman correlation coefficient r′ k , insert all the data in the interpolation selection set into the data set P in sequence m+1 , get the corresponding Spearman correlation coefficient r′ k , forming the Spearman correlation coefficient set R′; S7, select the Spearman correlation coefficient set R' and r m The closest element, with optional interpolation, as the data set P m The optimal interpolation of P m+1 The interpolation position reserved in the data set P is determined as m+1 ; S8, P m+1 and Q m+1 As a new P m and Q m Repeat steps S3 to S8 to continue the data set P m Interpolate until P in the data set m and data set Q n The number of elements in is equal; S9. Convert the completed energy consumption or industrial process data using the carbon emission factor to ultimately calculate the company's total carbon emissions.

2. The enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to claim 1 is characterized in that: The amount of energy consumption or industrial process data to be completed within the preset time period is less than the amount of known historical power data within the preset time period, that is, m is less than n.

3. The enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to claim 1 is characterized in that: Known data on energy consumption or industrial processes have corresponding historical power data at the same time point.

4. The enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to claim 1 is characterized in that: In step S4, in the data set Q n Select one that does not belong to the data set Q m Data q t When data q t It is the earliest data corresponding to the time node among the optional data.

5. The enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to claim 1 is characterized in that: In step S5, the data set P m According to the values ​​of , several optional interpolation values ​​are determined to form an interpolation selection set S, including: P m Arrange them according to the value to form a new data group P' m ; Calculate the data set P′ m The average value of every two adjacent data in , and the calculated average values ​​are used as optional interpolation values.

6. The enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to claim 5 is characterized in that: The optional interpolation in the interpolation selection set also includes a data set P' m The first and last items in the final interpolation selection set S m+1 Contains m+1 elements.

7. The enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to claim 6 is characterized in that: Data set P′ m For data set P m Arrange in ascending order according to the value; Interpolation selection set S m+1 The values ​​are sorted in ascending order.

8. The enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to claim 1 is characterized in that: The calculation method of the Spearman correlation coefficient includes: r=1-(6×∑d 2 ) / (g×(g 2 -1)) Where r represents the Spearman correlation coefficient, ∑d 2 represents the sum of squares of the rank differences between two data sets, and g represents the number of elements in the data sets.

9. A computer device, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is loaded and executed by the processor, the enterprise-level carbon emission calculation method based on interpolation of power data correlation coefficients as described in any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium, characterized in that A program is stored, and when the program is loaded by a processor, the enterprise-level carbon emission calculation method based on power data correlation coefficient interpolation according to any one of claims 1 to 8 is implemented.

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