Enterprise process-level carbon emission accounting method and device based on electric power data

Abstract

The invention discloses an enterprise process-level carbon emission accounting method and device based on electric power data, and can be applied to the technical field of carbon emission accounting. The method comprises the following steps: taking power consumption data of a target enterprise in a target time period as input, and predicting the main product yield of the target enterprise in the target time period by using a prediction model of the main product yield of the target enterprise; based on the main product yield of the target enterprise in the target time period and the energy consumption intensity and the material consumption intensity of the different processes of the target enterprise, calculating first carbon emission of the different processes of the target enterprise in the target time period; and correcting the first carbon emissions of the different processes of the target enterprise in the target time period to obtain the carbon emissions of the different processes of the target enterprise in the target time period.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission accounting, in particular to an enterprise process-level carbon emission accounting method and device based on power data. BACKGROUND

[0002] Industrial carbon emissions account for more than 70% of total carbon emissions. Improving the carbon emission accounting method of industrial enterprises is an important prerequisite for developing scientific and effective carbon emission reduction measures.

[0003] There are three main methods for carbon emission accounting: emission factor method, material balance method, and measurement method. Among them, the emission factor method and the material balance method are based on energy activity and industrial production process statistics, and the accounting results are usually monthly and annual. Although the measurement method is highly accurate, the measurement point is often located at the end of the discharge port, and cannot reflect the emission characteristics of the process.

[0004] It can be seen that the existing traditional carbon emission accounting method cannot meet the requirements of enterprises for timeliness and data quality at the same time. SUMMARY

[0005] To solve at least part of the above technical problems, the embodiments of the present application provide an enterprise process-level carbon emission accounting method and device based on power data.

[0006] The enterprise process-level carbon emission accounting method based on power data provided by the embodiments of the present application comprises: taking the power consumption data of a target enterprise in a target period as input, using a prediction model of the main product output of the target enterprise to predict the main product output of the target enterprise in the target period, wherein the prediction model is constructed based on the historical power consumption data and the main product capacity data of the target enterprise; based on the main product output of the target enterprise in the target period, the energy consumption intensity and the material consumption intensity of different processes of the target enterprise, the first carbon emission of different processes of the target enterprise in the target period is calculated; the first carbon emission of different processes of the target enterprise in the target period is corrected to obtain the carbon emission of different processes of the target enterprise in the target period.

[0007] In some embodiments, before taking the power consumption data of the target enterprise in the target period as input, using the prediction model of the main product output of the target enterprise to predict the main product output of the target enterprise in the target period, the method further comprises: obtaining the power consumption data and the main product capacity data recorded at a preset time interval within the historical period of the target enterprise; based on the number of production states of the target enterprise, the power consumption data is clustered into a corresponding number of power consumption intervals; a fitting function between power consumption and production load is established for each power consumption interval, and the fitting function is used as the prediction model of the main product output of the target enterprise.

[0008] In some embodiments, the production state comprises at least one of: a maintenance state, a stable production state, a high-load production state, and a low-load production state.

[0009] In some embodiments, the establishing of the fitting function between the power consumption and the production load for each power consumption interval comprises: selecting a maximum value of historical power consumption as a reference power consumption from the power consumption interval in the normal production state, and defining a production load corresponding to the reference power consumption as a reference load; and establishing the fitting function between the power consumption and the production load for each power consumption interval based on the reference power consumption and the reference load.

[0010] In some embodiments, the normal production state comprises at least one of: a stable production state and a high-load production state.

[0011] In some embodiments, the method further comprises: obtaining energy consumption, raw material consumption and auxiliary material consumption required for production of the main product of the industry unit in which the target enterprise is located, the energy comprising at least one of: coal, diesel, electricity and heat; and determining energy consumption intensity and material consumption intensity of different processes of the target enterprise based on the energy consumption, raw material consumption and auxiliary material consumption required for production of the main product of the industry unit in which the target enterprise is located.

[0012] In some embodiments, the process carbon emission amount of the target enterprise due to fossil fuel combustion is calculated by the following formula:

[0013]

[0014] In the formula, E represents the CO2 emission amount of fuel combustion of enterprise i; T represents the energy consumption intensity of fuel m of enterprise i; C represents the product yield of enterprise i; Q represents the average low calorific value of fuel m; U represents the unit calorific value carbon content of fuel m. com,i i,m i m m

[0015] In some embodiments, the process carbon emission amount of the target enterprise due to thermal decomposition of raw materials and auxiliary materials is calculated by the following formula:

[0016]

[0017] In the formula, E represents the CO2 emission amount of the production process of enterprise i; O represents the consumption amount of raw materials and auxiliary materials v of enterprise i; EF represents the CO2 emission factor of raw materials and auxiliary materials v. pro,i i,v v

[0018] ​​​​​​​​In some embodiments, the carbon emissions from the electricity and heat processes of the target enterprise are calculated using the following formula:

[0019]

[0020] In the formula: E ele+hot,i The CO2 emissions from the external purchase of electricity and heat by company i; A i,el Net purchased electricity volume representing company i; EF el CO2 emission factor representing purchased electricity; A i,hot Net purchased heat for company i; EF hot CO2 emission factor representing purchased heat.

[0021] In some embodiments, the process carbon emissions of the target enterprise's carbon sequestration products are calculated using the following formula:

[0022]

[0023] In the formula: R i Representative companies i CO2 emissions offset by carbon sequestration products; A i,cc The carbon sequestration output of company i; EF cc CO2 emission factor representing carbon sequestration products.

[0024] In some embodiments, the step of correcting the first carbon emissions of different processes of the target enterprise during the target period to obtain the carbon emissions of different processes of the target enterprise during the target period includes:

[0025] A carbon emission correction factor is determined based on the target company’s first total carbon emissions in the previous year calculated using the carbon emission factor method and the target company’s second total carbon emissions in the previous year calculated using the target company’s electricity data.

[0026] The carbon emission correction coefficient is used to correct the first carbon emission of different processes of the target enterprise during the target period, so as to obtain the carbon emission of different processes of the target enterprise during the target period.

[0027] In some embodiments, the first total carbon emissions of the target enterprise in the previous year calculated using the carbon emission factor method includes: the first total carbon emissions of the target enterprise in the previous year calculated using the carbon emission factor method based on the target enterprise's total energy and material consumption in the previous year.

[0028] In some embodiments, the second total carbon emissions of the target enterprise in the previous year, calculated using the target enterprise's electricity data, includes:

[0029] inputting hourly electricity consumption data of the target enterprise in the previous year, and predicting the hourly main product output of the target enterprise in the previous year by using a prediction model of the main product output of the target enterprise;

[0030] calculating the first carbon emission of each process of the target enterprise in the target period based on the main product output of the target enterprise in the target period, energy consumption intensity and material consumption intensity of each process of the target enterprise;

[0031] calculating the total first carbon emission of the target enterprise in the target period based on the first carbon emission of each process of the target enterprise in the target period.

[0032] In some embodiments, the length of the target period is 1 hour.

[0033] The embodiments of the present application also provide an enterprise process-level carbon emission accounting device based on electricity data, comprising:

[0034] a prediction module configured to input electricity consumption data of a target enterprise in a target period, and predict the main product output of the target enterprise in the target period by using a prediction model of the main product output of the target enterprise, wherein the prediction model is constructed based on historical electricity consumption data and main product capacity data of the target enterprise;

[0035] a calculation module configured to calculate the first carbon emission of each process of the target enterprise in the target period based on the main product output of the target enterprise in the target period, energy consumption intensity and material consumption intensity of each process of the target enterprise;

[0036] a correction module configured to correct the first carbon emission of each process of the target enterprise in the target period, to obtain the carbon emission of each process of the target enterprise in the target period.

[0037] In some embodiments, the device further comprises:

[0038] a first acquisition module configured to acquire electricity consumption data and main product capacity data recorded at a preset time interval within a historical period of the target enterprise;

[0039] a clustering module configured to cluster the electricity consumption data into a corresponding number of electricity intervals based on the number of production states of the target enterprise;

[0040] a building module configured to build a fitting function between electricity consumption and production load for each of the electricity intervals, and use the fitting function as the prediction model of the main product output of the target enterprise.

[0041] In some embodiments, the production state includes at least one of the following: maintenance state, stable production state, high-load production state, and low-load production state.

[0042] In some embodiments, the establishment module is specifically used for:

[0043] From the power consumption range under normal production conditions, the maximum historical power consumption value is selected as the benchmark power consumption value, and the production load corresponding to the benchmark power consumption value is defined as the benchmark load.

[0044] Based on the benchmark electricity consumption and benchmark load, a fitting function between electricity consumption and production load is established for each of the electricity consumption intervals.

[0045] In some embodiments, the apparatus further includes:

[0046] The second acquisition module is used to acquire the energy consumption, raw material consumption and auxiliary material consumption required for the production of the main products of the target enterprise in the industry unit. The energy includes at least one of the following: coal, diesel, electricity and heat.

[0047] The determination module is used to determine the energy intensity and material intensity of different processes of the target enterprise based on the energy consumption, raw material consumption and auxiliary material consumption required for the production of the main products of the target enterprise in the industry unit.

[0048] In some embodiments, the calculation module calculates the carbon emissions from the fossil fuel combustion process of the target enterprise using the following formula:

[0049]

[0050] In the formula: E com,i The CO2 emissions from fuel combustion of company i; T i,m The energy intensity of fuel m represents the enterprise's fuel consumption; C i Q represents the product output of company i; m U represents the average lower heating value of fuel m; m The carbon content per unit calorific value represents the fuel m.

[0051] In some embodiments, the calculation module calculates the carbon emissions of the target enterprise's raw materials and auxiliary materials undergoing thermal decomposition using the following formula:

[0052]

[0053] In the formula: E pro,i CO2 emissions from enterprise i's production process; O i,v EF represents the consumption of raw materials and auxiliary materials v by enterprise i; v CO2 emission factor representing raw materials and auxiliary materials v.

[0054] In some embodiments, the calculation module calculates the carbon emissions from the target enterprise's electricity and heat processes using the following formula:

[0055]

[0056] In the formula: E ele+hot,i The CO2 emissions from the external purchase of electricity and heat by company i; A i,el Net purchased electricity volume representing company i; EF el CO2 emission factor representing purchased electricity; A i,hot Net purchased heat for company i; EF hot CO2 emission factor representing purchased heat.

[0057] In some embodiments, the calculation module calculates the process carbon emissions of the target enterprise's carbon sequestration products using the following formula:

[0058]

[0059] In the formula: R i Representative companies i CO2 emissions offset by carbon sequestration products; A i,cc The carbon sequestration output of company i; EF cc CO2 emission factor representing carbon sequestration products.

[0060] In some embodiments, the correction module is specifically used for:

[0061] A carbon emission correction factor is determined based on the target company’s first total carbon emissions in the previous year calculated using the carbon emission factor method and the target company’s second total carbon emissions in the previous year calculated using the target company’s electricity data.

[0062] The carbon emission correction coefficient is used to correct the first carbon emission of different processes of the target enterprise during the target period, so as to obtain the carbon emission of different processes of the target enterprise during the target period.

[0063] In some embodiments, the correction module is further configured to: calculate the target enterprise's first total carbon emissions in the previous year using the carbon emission factor method, based on the target enterprise's total energy and material consumption in the previous year.

[0064] In some embodiments, the correction module is further configured to: take the target enterprise's hourly electricity consumption data in the previous year as input, and use a prediction model of the target enterprise's main product output in the previous year to predict the target enterprise's hourly main product output in the previous year; calculate the second carbon emissions of the target enterprise's different processes in the previous year based on the target enterprise's hourly main product output, the energy intensity and material intensity of the target enterprise's different processes; and calculate the target enterprise's total second carbon emissions in the previous year based on the second carbon emissions of the target enterprise's different processes in the previous year.

[0065] This application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the methods described in any of the above embodiments.

[0066] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0067] This application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the methods described in any of the above embodiments.

[0068] The enterprise process-level carbon emission accounting method and apparatus based on electricity data provided in this application have established an enterprise process-level carbon emission accounting method based on the advantages of 100% coverage, high data quality and high time frequency (15 minutes) of electricity data. This method can effectively make up for the shortcomings of existing carbon emission accounting guidelines in terms of timeliness and enterprise coverage. While providing enterprises with high-resolution carbon emission technology, it also provides an efficient means of monitoring enterprise carbon emission status. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0070] Figure 1 This is a flowchart illustrating an enterprise process-level carbon emission accounting method based on electricity data, provided in an embodiment of this application.

[0071] Figure 2This is a partial flowchart illustrating a method for calculating enterprise process-level carbon emissions based on electricity data, as provided in an embodiment of this application.

[0072] Figure 3 This is a partial flowchart illustrating a method for calculating enterprise process-level carbon emissions based on electricity data, as provided in an embodiment of this application.

[0073] Figure 4 This is a partial flowchart illustrating a method for calculating enterprise process-level carbon emissions based on electricity data, as provided in an embodiment of this application.

[0074] Figure 5 This is a partial flowchart illustrating a method for calculating enterprise process-level carbon emissions based on electricity data, as provided in an embodiment of this application.

[0075] Figure 6 This is a partial flowchart illustrating a method for calculating enterprise process-level carbon emissions based on electricity data, as provided in an embodiment of this application.

[0076] Figure 7 This is a schematic diagram of a corporate process-level carbon emission accounting device based on electricity data, provided in an embodiment of this application.

[0077] Figure 8 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments and their descriptions are used to explain this application, but are not intended to limit this application. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily arranged.

[0079] The terms “first,” “second,” etc., used in this document are not intended to specifically refer to order or sequence, nor are they used to limit this application; they are merely used to distinguish elements or operations described using the same technical terms.

[0080] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0081] The use of “and / or” in this document includes any or all of the items mentioned.

[0082] Figure 1 This is a flowchart illustrating an enterprise process-level carbon emission accounting method based on electricity data, provided in an embodiment of this application. Figure 1As shown in the embodiment of this application, a method for calculating enterprise process-level carbon emissions based on electricity data is provided, including:

[0083] S1. Using the target company's electricity consumption data during the target period as input, the target company's main product output during the target period is predicted using a prediction model of the target company's main product output. The prediction model is constructed based on the target company's historical electricity consumption data and main product capacity data.

[0084] Specifically, the target time period can be 1 hour.

[0085] S2. Based on the main product output of the target enterprise in the target period, the energy consumption intensity and material consumption intensity of different processes of the target enterprise, calculate the first carbon emissions of different processes of the target enterprise in the target period.

[0086] S3. Correct the first carbon emission amount of different processes of the target enterprise in the target period to obtain the carbon emission amount of different processes of the target enterprise in the target period.

[0087] The enterprise-level carbon emission accounting method based on electricity data provided in this application utilizes high-frequency electricity data to predict product output and combines it with process-level energy and material consumption intensity to achieve fine-grained carbon emission estimation. Furthermore, by introducing carbon emission data based on actual energy and material consumption for dynamic correction, the accuracy and reliability of the calculation are significantly improved. This method enables enterprise-wide, high-temporal-resolution (e.g., hourly) process-level carbon emission monitoring, overcoming the shortcomings of existing methods in terms of timeliness and coverage, and providing efficient and accurate online carbon emission accounting capabilities.

[0088] like Figure 2 As shown, in some embodiments, before using the target enterprise's electricity consumption data during a target period as input and employing a prediction model of the target enterprise's main product output during the target period to predict the target enterprise's main product output, the method further includes:

[0089] S01. Obtain the electricity consumption data and main product production capacity data of the target enterprise recorded at preset time intervals during the historical period;

[0090] S02. Based on the number of production statuses of the target enterprise, the electricity consumption data is clustered into a corresponding number of electricity consumption intervals;

[0091] S03. Establish a fitting function between electricity consumption and production load for each of the aforementioned electricity consumption intervals, and use the fitting function as a prediction model for the output of the main products of the target enterprise.

[0092] Specifically, historical electricity consumption data and main product capacity data of the target enterprise are obtained; and based on literature review or on-site investigation, the possible production states corresponding to the production of products in the target enterprise's industry are obtained, such as maintenance state, stable production state, high load production state, and low load production state; based on the number of production states of the target enterprise, the historical electricity consumption data of the target enterprise is clustered into a corresponding number of electricity consumption intervals; and a fitting function between electricity consumption and production load is established for each of the electricity consumption intervals, thereby completing the construction of the target enterprise's main product output prediction model based on electricity data.

[0093] like Figure 3 As shown, in some embodiments, establishing a fitting function between electricity consumption and production load for each of the electricity consumption zones includes:

[0094] S031. From the power consumption range under normal production conditions, select the maximum historical power consumption as the benchmark power consumption, and define the production load corresponding to the benchmark power consumption as the benchmark load.

[0095] S032. Based on the benchmark electricity consumption and benchmark load, establish a fitting function between electricity consumption and production load for each of the electricity consumption intervals.

[0096] Specifically, the maximum electricity consumption under normal production conditions (stable production, high-load production, and low-load production are all considered normal production conditions) can be set to correspond to 100% production load. Based on this, the production load corresponding to other electricity consumption can be determined, and a fitting function between electricity consumption and production load can be established for each of the aforementioned electricity consumption intervals.

[0097] like Figure 4 As shown, in some embodiments, the method further includes:

[0098] S04. Obtain the energy consumption, raw material consumption, and auxiliary material consumption required for the production of the main products of the target enterprise in the industry unit. The energy includes at least one of the following: coal, diesel, electricity, and heat.

[0099] S05. Based on the energy consumption, raw material consumption, and auxiliary material consumption required for the production of the main products of the target enterprise in the industry, determine the energy intensity and material consumption intensity of different processes of the target enterprise.

[0100] Specifically, based on statistical yearbooks and other literature reviews or on-site surveys, the energy consumption (coal, diesel, electricity, heat, etc.) required for the production of the target company's main products per unit in the industry can be obtained, as well as the raw material and auxiliary material consumption required for the production of the target company's main products per unit in the industry. The average energy consumption (coal, diesel, electricity, heat, etc.) required for the production of the target company's main products per unit in the industry is defined as the energy intensity of the main products; the average raw material and auxiliary material consumption required for the production of the target company's main products per unit in the industry is defined as the material intensity of the main products.

[0101] Currently, when the energy consumption of coal, diesel, electricity, heat, etc., of the target enterprise's main products, as well as the consumption of raw materials and auxiliary materials of the main products, can be directly obtained, these data can be directly acquired. The energy consumption of coal, diesel, electricity, heat, etc., of the target enterprise's main products, as well as the consumption of raw materials and auxiliary materials of the main products, constitute the energy and material consumption intensity of the target enterprise's main products (each main product corresponds to one process).

[0102] In some embodiments, the carbon emissions from the fossil fuel combustion process of the target enterprise are calculated using the following formula:

[0103]

[0104] In the formula: E com,i The CO2 emissions from fuel combustion of company i; T i,m The energy intensity of fuel m represents the enterprise's fuel consumption; C i Q represents the product output of company i; m U represents the average lower heating value of fuel m; m The carbon content per unit calorific value represents the fuel m.

[0105] In some embodiments, the carbon emissions from the thermal decomposition of raw materials and auxiliary materials of the target enterprise are calculated using the following formula:

[0106]

[0107] In the formula: E pro,i CO2 emissions from enterprise i's production process; O i,v EF represents the consumption of raw materials and auxiliary materials v by enterprise i; v CO2 emission factor representing raw materials and auxiliary materials v.

[0108] In some embodiments, the carbon emissions from the electricity and heat processes of the target enterprise are calculated using the following formula:

[0109]

[0110] In the formula: Eele+hot,i The CO2 emissions from the external purchase of electricity and heat by company i; A i,el Net purchased electricity volume representing company i; EF el CO2 emission factor representing purchased electricity; A i,hot Net purchased heat for company i; EF hot CO2 emission factor representing purchased heat.

[0111] In some embodiments, the process carbon emissions of the target enterprise's carbon sequestration products are calculated using the following formula:

[0112]

[0113] In the formula: R i The CO2 emissions offset by the carbon sequestration products of company A; i,cc The carbon sequestration output of company i; EF cc CO2 emission factor representing carbon sequestration products.

[0114] like Figure 5 As shown, in some embodiments, the step of correcting the first carbon emissions of different processes of the target enterprise during the target period to obtain the carbon emissions of different processes of the target enterprise during the target period includes:

[0115] S31. Based on the target company’s first total carbon emissions in the previous year calculated using the carbon emission factor method and the target company’s second total carbon emissions in the previous year calculated using the target company’s electricity data, determine a carbon emission correction coefficient.

[0116] S32. The carbon emission correction coefficient is used to correct the first carbon emission of different processes of the target enterprise in the target period, so as to obtain the carbon emission of different processes of the target enterprise in the target period.

[0117] In some embodiments, the first total carbon emissions of the target enterprise in the previous year calculated using the carbon emission factor method includes: the first total carbon emissions of the target enterprise in the previous year calculated using the carbon emission factor method based on the target enterprise's total energy and material consumption in the previous year.

[0118] Specifically, based on literature review or on-site investigation, the energy and material consumption of enterprises in the target industry in the previous year can be obtained, and the total primary carbon emissions of the target enterprise in the previous year can be calculated based on accounting guidelines (such as the "Requirements for Greenhouse Gas Emission Accounting and Reporting" (GB / T 32151)).

[0119] like Figure 6As shown, in some embodiments, the second total carbon emissions of the target enterprise in the previous year, calculated using the target enterprise's electricity data, includes:

[0120] S311. Using the target company's hourly electricity consumption data in the previous year as input, predict the target company's hourly output of its main products in the previous year using the prediction model of the target company's main product output.

[0121] S312. Based on the target enterprise's main product output per hour, the energy intensity and material intensity of different processes of the target enterprise, calculate the second carbon emissions per hour of different processes of the target enterprise in the previous year.

[0122] S313. Based on the second carbon emissions per hour of different processes of the target enterprise in the previous year, calculate the total second carbon emissions of the target enterprise in the previous year.

[0123] Specifically, the calculation processes for steps S311 to S312 are similar to those for steps S1 and S2 described above, and will not be repeated here. In step S313, the second carbon emissions per hour for different processes of the target enterprise in the previous year are calculated to obtain the total second carbon emissions of the target enterprise in the previous year.

[0124] Then calculate the correction factor: Correction factor = Second total carbon emissions / First total carbon emissions; Use the correction factor to correct the total carbon emissions calculated in step S2 to obtain the corrected carbon emissions of the target enterprise's processes: Corrected process carbon emissions = First carbon emissions of each process of the target enterprise in the target period × Correction factor.

[0125] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the following detailed explanation will be given using steel company A as an example.

[0126] Step 1: Construct a forecasting model for Company A's main products based on electricity data.

[0127] S101. Obtain the hourly electricity consumption data of Enterprise A for 2022-2023, as well as its sintered ore, pig iron, and crude steel production capacity;

[0128] S102. Based on on-site investigation, it was found that Company A has three production states (maintenance state, stable production state, and high-load production state).

[0129] S103. Set the number of clusters to 3, and use K-means to cluster the hourly electricity consumption data for 2022;

[0130] S104. Set the maximum electricity consumption under high-load production conditions to correspond to 100% production load of enterprise A. Calculate the production load corresponding to the hourly electricity consumption in sequence, and fit the electricity consumption and production load functions under the three clustering results respectively.

[0131] S105. Using hourly data from 2023 as input, estimate the production load of enterprise A corresponding to the electricity consumption.

[0132] Specifically, calculate the hourly production capacity of company A: hourly production capacity = production capacity / 330 days / 24 hours; multiply the hourly production load of company A by the hourly production capacity to obtain the hourly output of sintered ore, pig iron and crude steel of company A.

[0133] Step Two: Obtain the energy and material consumption intensity of Company A's main products.

[0134] S201. Collect data on the annual energy, raw material, and auxiliary material consumption of Enterprise A's sintering, pig iron, and crude steel production processes through surveys.

[0135] S202. Calculate the ratio of annual energy, raw material, and auxiliary material consumption to annual sintered ore, pig iron, and crude steel production to obtain the energy intensity and material intensity of different processes of enterprise A.

[0136] Step 3: Calculate the carbon emission level of Enterprise A's process.

[0137] S301. Based on formulas 1 to 4, calculate the hourly carbon emissions of enterprise A's sintering ore production process (sintering), pig iron production process (blast furnace), and crude steel production process (converter) in 2023. The corresponding carbon emission factors can be obtained from "Greenhouse Gas Emissions Accounting and Reporting Requirements Part 5: Iron and Steel Enterprises" (GB / T 32151.5—2015).

[0138] S302. Sum the hourly carbon emissions of each process of Company A to obtain the annual carbon emission result of Company A in 2023.

[0139] Step 4: Correction of Carbon Emission Results

[0140] S401. Using the total annual energy and material consumption data of Company A in 2023 as input, calculate the annual carbon emissions of Company A based on the accounting method of "Greenhouse Gas Emissions Accounting and Reporting Requirements Part 5: Iron and Steel Enterprises" (GB / T 32151.5—2015);

[0141] S402. Calculate the ratio of the calculation result in step S401 to the calculation result in step S302, and define it as the carbon emission correction factor.

[0142] S403. Multiply the carbon emission correction factor by the hourly carbon emission result from step S301 to correct the carbon emission result. In actual calculations, the hourly carbon emission result for the target year can be corrected using the correction factor from the previous year.

[0143] Therefore, the enterprise process-level carbon emission accounting method based on electricity data provided in this application can overcome the constraints of statistical data updates being 1-2 years behind, only having annual or monthly data, and mostly remaining at the industry level. It effectively makes up for the shortcomings of existing carbon emission accounting guidelines in terms of timeliness and enterprise coverage. While providing enterprises with high-resolution carbon emission technology, it also provides an efficient means of monitoring enterprise carbon emission status, and provides a practical carbon monitoring technology for enterprises that are difficult to cover by automatic carbon emission monitoring systems.

[0144] Based on the same inventive concept, this application also provides an enterprise process-level carbon emission accounting device based on electricity data.

[0145] Figure 7 This is a schematic diagram of a corporate process-level carbon emission accounting device based on electricity data, provided in an embodiment of this application. Figure 7 As shown, the device includes:

[0146] The prediction module 21 is used to take the electricity consumption data of the target enterprise in the target period as input, and use the prediction model of the output of the target enterprise's main products in the target period to predict the output of the target enterprise's main products in the target period. The prediction model is constructed based on the target enterprise's historical electricity consumption data and main product capacity data.

[0147] Calculation module 22 is used to calculate the first carbon emissions of different processes of the target enterprise during the target period based on the main product output of the target enterprise during the target period, the energy consumption intensity and material consumption intensity of different processes of the target enterprise;

[0148] The correction module 23 is used to correct the first carbon emission amount of different processes of the target enterprise in the target period, so as to obtain the carbon emission amount of different processes of the target enterprise in the target period.

[0149] The enterprise-level carbon emission accounting device based on electricity data provided in this application uses high-frequency electricity data to predict product output and combines it with process-level energy and material consumption intensity to achieve fine-grained carbon emission estimation. Furthermore, by introducing carbon emission data based on actual energy and material consumption for dynamic correction, the accuracy and reliability of the calculation are significantly improved. It enables enterprise-wide, high-time-resolution (e.g., hourly) process-level carbon emission monitoring, overcoming the shortcomings of existing methods in terms of timeliness and coverage, and providing efficient and accurate online carbon emission accounting capabilities.

[0150] In some embodiments, the apparatus further includes:

[0151] The first acquisition module is used to acquire the electricity consumption data and main product production capacity data of the target enterprise recorded at preset time intervals during a historical period.

[0152] The clustering module is used to cluster the electricity consumption data into a corresponding number of electricity consumption intervals based on the number of production statuses of the target enterprise.

[0153] A module is established to create a fitting function between electricity consumption and production load for each of the aforementioned electricity consumption intervals, and the fitting function is used as a prediction model for the output of the main products of the target enterprise.

[0154] In some embodiments, the production state includes at least one of the following: maintenance state, stable production state, high-load production state, and low-load production state.

[0155] In some embodiments, the establishment module is specifically used for:

[0156] From the power consumption range under normal production conditions, the maximum historical power consumption value is selected as the benchmark power consumption value, and the production load corresponding to the benchmark power consumption value is defined as the benchmark load.

[0157] Based on the benchmark electricity consumption and benchmark load, a fitting function between electricity consumption and production load is established for each of the electricity consumption intervals.

[0158] In some embodiments, the apparatus further includes:

[0159] The second acquisition module is used to acquire the energy consumption, raw material consumption and auxiliary material consumption required for the production of the main products of the target enterprise in the industry unit. The energy includes at least one of the following: coal, diesel, electricity and heat.

[0160] The determination module is used to determine the energy intensity and material intensity of different processes of the target enterprise based on the energy consumption, raw material consumption and auxiliary material consumption required for the production of the main products of the target enterprise in the industry unit.

[0161] In some embodiments, the calculation module calculates the carbon emissions from the fossil fuel combustion process of the target enterprise using the following formula:

[0162]

[0163] In the formula: E com,i The CO2 emissions from fuel combustion of company i; T i,m The energy intensity of fuel m represents the enterprise's fuel consumption; C i Q represents the product output of company i; mU represents the average lower heating value of fuel m; m The carbon content per unit calorific value represents the fuel m.

[0164] In some embodiments, the calculation module calculates the carbon emissions of the target enterprise's raw materials and auxiliary materials undergoing thermal decomposition using the following formula:

[0165]

[0166] In the formula: E pro,i CO2 emissions from enterprise i's production process; O i,v EF represents the consumption of raw materials and auxiliary materials v by enterprise i; v CO2 emission factor representing raw materials and auxiliary materials v.

[0167] In some embodiments, the calculation module calculates the carbon emissions from the target enterprise's electricity and heat processes using the following formula:

[0168]

[0169] In the formula: E ele+hot,i The CO2 emissions from the external purchase of electricity and heat by company i; A i,el Net purchased electricity volume representing company i; EF el CO2 emission factor representing purchased electricity; A i,hot Net purchased heat for company i; EF hot CO2 emission factor representing purchased heat.

[0170] In some embodiments, the calculation module calculates the process carbon emissions of the target enterprise's carbon sequestration products using the following formula:

[0171]

[0172] In the formula: R i Representative companies i CO2 emissions offset by carbon sequestration products; A i,cc The carbon sequestration output of company i; EF cc CO2 emission factor representing carbon sequestration products.

[0173] In some embodiments, the correction module is specifically used for:

[0174] A carbon emission correction factor is determined based on the target company’s first total carbon emissions in the previous year calculated using the carbon emission factor method and the target company’s second total carbon emissions in the previous year calculated using the target company’s electricity data.

[0175] The carbon emission correction coefficient is used to correct the first carbon emission of different processes of the target enterprise during the target period, so as to obtain the carbon emission of different processes of the target enterprise during the target period.

[0176] In some embodiments, the correction module is further configured to: calculate the target enterprise's first total carbon emissions in the previous year using the carbon emission factor method, based on the target enterprise's total energy and material consumption in the previous year.

[0177] In some embodiments, the correction module is further configured to: take the target enterprise's hourly electricity consumption data in the previous year as input, and use a prediction model of the target enterprise's main product output in the previous year to predict the target enterprise's hourly main product output in the previous year; calculate the second carbon emissions of the target enterprise's different processes in the previous year based on the target enterprise's hourly main product output, the energy intensity and material intensity of the target enterprise's different processes; and calculate the target enterprise's total second carbon emissions in the previous year based on the second carbon emissions of the target enterprise's different processes in the previous year.

[0178] The embodiments of the apparatus provided in this application can be used to execute the processing flow of the above method embodiments, and will not be repeated here. Please refer to the detailed description of the above method embodiments.

[0179] Figure 8 This is a schematic diagram of the physical structure of an electronic device provided in an embodiment of this application, as shown below. Figure 8 As shown, the electronic device may include a processor 301, a communications interface 302, a memory 303, and a communication bus 304, wherein the processor 301, the communications interface 302, and the memory 303 communicate with each other via the communication bus 304. The processor 301 may call logical instructions in the memory 303 to execute the methods described in any of the above embodiments.

[0180] Furthermore, the logical instructions in the aforementioned memory 303 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0181] This embodiment discloses a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer can perform the methods provided in the above-described method embodiments.

[0182] This embodiment provides a computer-readable storage medium storing a computer program that causes the computer to perform the methods provided in the above-described method embodiments.

[0183] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0184] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0185] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0186] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0187] In the description of this specification, the references to terms such as "an embodiment," "a specific embodiment," "some embodiments," "for example," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0188] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for calculating enterprise process-level carbon emissions based on electricity data, characterized in that, include: Using the target company's electricity consumption data during the target period as input, the target company's main product output is predicted using a prediction model based on the target company's historical electricity consumption data and main product capacity data. Based on the target company's main product output during the target period, the energy intensity and material intensity of different processes of the target company, calculate the first carbon emissions of different processes of the target company during the target period; The carbon emissions of different processes of the target enterprise during the target period are corrected to obtain the carbon emissions of different processes of the target enterprise during the target period.

2. The method according to claim 1, characterized in that, Before using the electricity consumption data of the target enterprise during the target period as input and predicting the output of the target enterprise's main products during the target period using a prediction model of the target enterprise's main product output, the method further includes: Obtain the target company's electricity consumption data and main product production capacity data recorded at preset time intervals during a historical period; Based on the number of production statuses of the target enterprises, the electricity consumption data is clustered into a corresponding number of electricity consumption intervals; A fitting function is established for the relationship between electricity consumption and production load for each of the aforementioned electricity consumption zones, and the fitting function is used as a prediction model for the output of the main products of the target enterprise.

3. The method according to claim 2, characterized in that, The production status includes at least one of the following: maintenance status, stable production status, high-load production status, and low-load production status.

4. The method according to claim 3, characterized in that, The step of establishing a fitting function between electricity consumption and production load for each of the aforementioned electricity consumption zones includes: From the power consumption range under normal production conditions, the maximum historical power consumption value is selected as the benchmark power consumption value, and the production load corresponding to the benchmark power consumption value is defined as the benchmark load. Based on the benchmark electricity consumption and benchmark load, a fitting function between electricity consumption and production load is established for each of the electricity consumption intervals.

5. The method according to claim 4, characterized in that, The normal production state includes at least one of the following: stable production state and high-load production state.

6. The method according to claim 1, characterized in that, The method further includes: The energy consumption, raw material consumption, and auxiliary material consumption required for the production of the main products of the target enterprise in the industry are obtained. The energy includes at least one of the following: coal, diesel, electricity, and heat. Based on the energy consumption, raw material consumption, and auxiliary material consumption required for the production of the main products of the target enterprise in its industry, the energy intensity and material consumption intensity of different processes of the target enterprise are determined.

7. The method according to claim 1, characterized in that, The carbon emissions from the fossil fuel combustion process of the target enterprise are calculated using the following formula: In the formula: E com,i The CO2 emissions from fuel combustion of company i; T i,m The energy intensity of fuel m represents the enterprise's fuel consumption; C i Q represents the product output of company i; m U represents the average lower heating value of fuel m; m The carbon content per unit calorific value represents the fuel m.

8. The method according to claim 1, characterized in that, The carbon emissions from the thermal decomposition of raw materials and auxiliary materials of the target enterprise are calculated using the following formula: In the formula: E pro,i CO2 emissions from enterprise i's production process; O i,v EF represents the consumption of raw materials and auxiliary materials v by enterprise i; v CO2 emission factor representing raw materials and auxiliary materials v.

9. The method according to claim 1, characterized in that, The carbon emissions from the electricity and heat processes of the target enterprise are calculated using the following formula: In the formula: E ele+hot,i The CO2 emissions from the external purchase of electricity and heat by company i; A i,el Net purchased electricity volume representing company i; EF el CO2 emission factor representing purchased electricity; A i,hot Net purchased heat for company i; EF hot CO2 emission factor representing purchased heat.

10. The method according to claim 1, characterized in that, The carbon emissions from the carbon sequestration process of the target company's carbon sequestration products are calculated using the following formula: In the formula: R i Representative companies i CO2 emissions offset by carbon sequestration products; A i,cc The carbon sequestration output of company i; EF cc CO2 emission factor representing carbon sequestration products.

11. The method according to claim 1, characterized in that, The carbon emissions of different processes of the target enterprise during the target period are corrected to obtain the carbon emissions of different processes of the target enterprise during the target period, including: A carbon emission correction factor is determined based on the target company’s first total carbon emissions in the previous year calculated using the carbon emission factor method and the target company’s second total carbon emissions in the previous year calculated using the target company’s electricity data. The carbon emission correction coefficient is used to correct the first carbon emission of different processes of the target enterprise during the target period, so as to obtain the carbon emission of different processes of the target enterprise during the target period.

12. The method according to claim 11, characterized in that, The total first carbon emissions of the target enterprise in the previous year, calculated using the carbon emission factor method, include: The total first carbon emissions of the target company in the previous year are calculated using the carbon emission factor method based on the target company's total energy and material consumption in the previous year.

13. The method according to claim 11, characterized in that, The second total carbon emissions of the target enterprise in the previous year, calculated using the target enterprise's electricity data, include: Using the target company's hourly electricity consumption data from the previous year as input, the target company's hourly output of its main products is predicted using a predictive model of the target company's main product output from the previous year. Based on the target company's hourly output of its main products, the energy intensity and material intensity of different processes of the target company, calculate the second carbon emissions of different processes of the target company per hour in the previous year; Based on the second carbon emissions per hour of different processes of the target enterprise in the previous year, calculate the total second carbon emissions of the target enterprise in the previous year.

14. The method according to claim 13, characterized in that, The target time period is 1 hour in length.

15. A process-level carbon emission accounting device based on electricity data, characterized in that, include: The prediction module is used to take the electricity consumption data of the target enterprise during the target period as input and use the prediction model of the output of the target enterprise's main products during the target period to predict the output of the target enterprise's main products. The prediction model is constructed based on the target enterprise's historical electricity consumption data and main product capacity data. The calculation module is used to calculate the first carbon emissions of different processes of the target enterprise during the target period based on the main product output of the target enterprise during the target period, the energy consumption intensity and material consumption intensity of different processes of the target enterprise; The correction module is used to correct the first carbon emission amount of different processes of the target enterprise in the target period, so as to obtain the carbon emission amount of different processes of the target enterprise in the target period.

16. The apparatus according to claim 15, characterized in that, The device further includes: The first acquisition module is used to acquire the electricity consumption data and main product production capacity data of the target enterprise recorded at preset time intervals during a historical period. The clustering module is used to cluster the electricity consumption data into a corresponding number of electricity consumption intervals based on the number of production statuses of the target enterprise. A module is established to create a fitting function between electricity consumption and production load for each of the aforementioned electricity consumption intervals, and the fitting function is used as a prediction model for the output of the main products of the target enterprise.

17. The apparatus according to claim 16, characterized in that, The production status includes at least one of the following: maintenance status, stable production status, high-load production status, and low-load production status.

18. The apparatus according to claim 17, characterized in that, The establishment module is specifically used for: From the power consumption range under normal production conditions, the maximum historical power consumption value is selected as the benchmark power consumption value, and the production load corresponding to the benchmark power consumption value is defined as the benchmark load. Based on the benchmark electricity consumption and benchmark load, a fitting function between electricity consumption and production load is established for each of the electricity consumption intervals.

19. The apparatus according to claim 15, characterized in that, The device further includes: The second acquisition module is used to acquire the energy consumption, raw material consumption and auxiliary material consumption required for the production of the main products of the target enterprise in the industry unit. The energy includes at least one of the following: coal, diesel, electricity and heat. The determination module is used to determine the energy intensity and material intensity of different processes of the target enterprise based on the energy consumption, raw material consumption and auxiliary material consumption required for the production of the main products of the target enterprise in the industry unit.

20. The apparatus according to claim 15, characterized in that, The calculation module calculates the carbon emissions from the fossil fuel combustion process of the target enterprise using the following formula: In the formula: E com,i The CO2 emissions from fuel combustion of company i; T i,m The energy intensity of fuel m represents the enterprise's fuel consumption; C i Q represents the product output of company i; m U represents the average lower heating value of fuel m; m The carbon content per unit calorific value represents the fuel m.

21. The apparatus according to claim 15, characterized in that, The calculation module calculates the carbon emissions from the thermal decomposition of raw materials and auxiliary materials of the target enterprise using the following formula: In the formula: E pro,i CO2 emissions from enterprise i's production process; O i,v EF represents the consumption of raw materials and auxiliary materials v by enterprise i; v CO2 emission factor representing raw materials and auxiliary materials v.

22. The apparatus according to claim 15, characterized in that, The calculation module calculates the carbon emissions from the target company's electricity and heat processes using the following formula: In the formula: E ele+hot,i The CO2 emissions from the external purchase of electricity and heat by company i; A i,el Net purchased electricity volume representing company i; EF el CO2 emission factor representing purchased electricity; A i,hot Net purchased heat for company i; EF hot CO2 emission factor representing purchased heat.

23. The apparatus according to claim 15, characterized in that, The calculation module calculates the process carbon emissions of the target company's carbon sequestration products using the following formula: In the formula: R i Representative companies i CO2 emissions offset by carbon sequestration products; A i,cc The carbon sequestration output of company i; EF cc CO2 emission factor representing carbon sequestration products.

24. The apparatus according to claim 15, characterized in that, The correction module is specifically used for: A carbon emission correction factor is determined based on the target company’s first total carbon emissions in the previous year calculated using the carbon emission factor method and the target company’s second total carbon emissions in the previous year calculated using the target company’s electricity data. The carbon emission correction coefficient is used to correct the first carbon emission of different processes of the target enterprise during the target period, so as to obtain the carbon emission of different processes of the target enterprise during the target period.

25. The apparatus according to claim 24, characterized in that, The correction module is also used for: The total first carbon emissions of the target company in the previous year are calculated using the carbon emission factor method based on the target company's total energy and material consumption in the previous year.

26. The apparatus according to claim 24, characterized in that, The correction module is also used for: Using the target company's hourly electricity consumption data from the previous year as input, the target company's hourly output of its main products is predicted using a predictive model of its main product output from the previous year. Based on the target company's hourly output of its main products, the energy intensity and material intensity of different processes of the target company, the second carbon emissions of different processes of the target company are calculated from the previous year's hourly output. Based on the second carbon emissions of different processes of the target company from the previous year's hourly output, the target company's total second carbon emissions for the previous year are calculated.

27. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method as described in any one of claims 1 to 14.

28. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 14.

29. A computer program product, the computer program product comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 14.

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