Park carbon emission accounting method based on multi-dimensional data and historical data correction

Abstract

The invention provides a park carbon emission accounting method based on multi-dimensional data and historical data correction, and belongs to the technical field of carbon emission, and the method comprises the steps: collecting historical carbon emission data, basic energy consumption data, production process data and external environment data of a park; calculating the initial carbon emission of the park according to the basic energy consumption data; according to the historical carbon emission data, performing non-seasonal difference and seasonal difference processing to obtain a carbon emission accounting error; calculating a production process correction factor, an air temperature influence correction factor, a policy influence factor and a carbon price influence factor according to the production process data and the external environment data; and performing comprehensive operation on the initial carbon emission of the park, the carbon emission accounting error and the plurality of correction factors to obtain a corrected park carbon emission accounting result. The method solves the problems that the implementation cost is high and the accounting error is large due to the fact that an existing park carbon emission accounting technology depends on an artificial intelligence algorithm and ignores multi-source key carbon emission influence factors.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of carbon emission, and particularly relates to a park carbon emission accounting method based on multi-dimensional data and historical data correction. BACKGROUND

[0002] Under the background of the "double carbon" target, industrial parks, as the core carriers of industrial carbon emissions, need to accurately grasp the carbon emission peak timing to support the formulation and implementation of emission reduction strategies. Existing carbon emission accounting methods mainly rely on artificial intelligence algorithms or traditional single time series models, but these methods have significant technical problems.

[0003] Firstly, the scheme based on artificial intelligence algorithm usually requires a large amount of computing resources and data support, resulting in high computing power requirements. For small and medium-sized parks, the deployment cost is high, and it is difficult to achieve large-scale implementation. Secondly, the traditional single time series model often ignores key influencing factors such as raw material consumption, temperature changes, policy adjustments, and market carbon price fluctuations, resulting in large deviations in accounting results and insufficient prediction accuracy. These problems limit the reliability and practicality of carbon emission accounting methods in actual application. SUMMARY

[0004] In view of the above problems in the prior art, the park carbon emission accounting method based on multi-dimensional data and historical data correction provided by the application solves the problems of high implementation cost and large accounting error of existing park carbon emission accounting technology due to reliance on artificial intelligence algorithms and neglect of multi-source key carbon emission influencing factors.

[0005] In order to achieve the above purposes, the technical scheme adopted by the application is: The park carbon emission accounting method based on multi-dimensional data and historical data correction comprises the following steps: S1: collecting historical carbon emission data and multi-source data of the park, wherein the multi-source data at least includes basic energy consumption data, production process data and external environment data of the park; S2: calculating the initial carbon emission of the park according to the basic energy consumption data; S3: calculating the carbon emission accounting error by performing non-seasonal difference and seasonal difference processing according to the historical carbon emission data; S4: calculating correction factors according to the production process data and the external environment data, wherein the correction factors at least include a production process correction factor, a temperature influence correction factor, a policy influence factor and a carbon price influence factor; S5: performing comprehensive operation on the initial carbon emission of the park, the carbon emission accounting error and the correction factors to obtain the corrected park carbon emission accounting result.

[0006] To address the problems of existing carbon emission accounting methods, such as high computational deployment costs due to reliance on high-performance AI algorithms, making them unsuitable for various small and medium-sized industrial parks, and low accuracy in carbon emission accounting predictions due to neglecting multi-dimensional key factors such as production processes, environmental climate, policy regulation, and market carbon prices, this invention proposes a carbon emission accounting method for industrial parks based on multi-dimensional data and historical data correction. This method calculates initial carbon emissions by collecting multi-source data on basic energy consumption, production processes, and the external environment of the industrial park. It then uses historical carbon emission data to accurately estimate accounting errors through non-seasonal and seasonal differencing. Simultaneously, it introduces correction factors for production processes, temperature influence, policy influence, and carbon price influence to dynamically optimize and compensate for the initial accounting results. This allows for timely responses to carbon price trend fluctuations and external factors affecting the industrial park, improving the accuracy and reliability of carbon emission accounting, reducing reliance on high-performance computing resources, and enabling deployment and implementation on low-cost servers. It is suitable for carbon emission accounting and monitoring in various industrial parks.

[0007] Furthermore: the expression for the initial carbon emissions of the park in S2 is as follows:

[0008]

[0009]

[0010]

[0011]

[0012]

[0013] in, For the first The initial carbon emissions of the park in one month. Carbon emissions from electricity generation. Carbon emissions from coal. Carbon emissions from natural gas. Carbon emissions generated by heat Carbon emissions from raw material conversion For electricity consumption, Coal consumption, For gas consumption, To consume heat, This refers to the amount of raw materials consumed. For power grid emission factors, Coal emission factors As a natural gas emission factor, As a thermal emission factor, This is the raw material conversion efficiency factor.

[0014] The further beneficial effects mentioned above are: converting the energy consumption of multiple energy sources such as electricity, coal, natural gas, and raw materials into a uniform carbon emission per unit through corresponding factors, ensuring the comprehensiveness and standardization of carbon emission accounting, and providing accurate and reliable benchmark data for subsequent multi-dimensional data correction.

[0015] Furthermore: the expression for the carbon emission accounting error in S3 is as follows:

[0016]

[0017]

[0018] in, For the park's first The error in carbon emission accounting over a month This is a non-seasonal difference of order d, where d=2 represents a comparison of carbon emission data between adjacent months. This is a D-order seasonal difference, where D is 12 to represent a comparison between the carbon emission data of the current month and the carbon emission data of the same month of the previous year. For the park's first Total carbon emissions for the month For the park's first Total carbon emissions for the month For the park's first Total carbon emissions for the month For the park's first Total carbon emissions for the month For the park's first Total carbon emissions for the month For the park's first Total carbon emissions for the month.

[0019] The further beneficial effects mentioned above are as follows: by using second-order non-seasonal differencing, the trend changes in carbon emission data caused by long-term factors such as capacity expansion and technological upgrades are removed, allowing the data to focus on short-term fluctuations and eliminating trend interference for error estimation; by using first-order seasonal differencing, the carbon emission fluctuations caused by seasonal factors such as winter heating and summer production peaks are eliminated by comparing data from the same period, thus avoiding seasonal differences being misjudged as accounting errors.

[0020] Furthermore, the expression for the production process correction factor is as follows:

[0021] in, For the park's first Monthly production process correction factor For the park's first Monthly operating load, is an annual average of the park operation load, is a product output of the park in the month, is an annual average of the product output of the park.

[0022] The further beneficial effect is that by weighting and fusing the park operation load and the product output, the influence of the production intensity change on the carbon emission is dynamically reflected, the problem of ignoring the actual production fluctuation in the traditional accounting method is solved, the carbon emission under different production states is accurately corrected, and the adaptability of the accounting model to the actual operation condition of the park is improved.

[0023] Further, the expression of the air temperature influence correction factor is as follows:

[0024]

[0025]

[0026]

[0027] wherein, is an air temperature influence correction factor of the park in the month, is a difference between the monthly average air temperature and the reference air temperature of the park in the month, is a monthly average air temperature of the park in the month.

[0028] The further beneficial effect is that the temperature difference between the monthly average air temperature and the reference air temperature is quantified as a carbon emission correction coefficient, the influence of the temperature regulation behavior such as winter heating and summer cooling on the energy consumption can be reflected, the problem of insufficient consideration of environmental factors in the traditional accounting method is solved, and the accuracy and adaptability of the carbon emission accounting result under different climate conditions are improved.

[0029] Further, the expression of the policy influence factor is as follows:

[0030] wherein, is a policy influence factor of the park in the month, is a number of effective policies influencing the carbon emission of the park in the month, is a serial number, is an emission reduction coefficient of the policy, is an emission reduction coefficient of the The weight of each policy item.

[0031] The further beneficial effects mentioned above are: quantifying different emission reduction policies into corresponding emission reduction coefficients and weighting and integrating them can establish a quantitative correlation between policies and carbon emissions, solve the problem that traditional accounting methods do not adequately consider policy factors, and improve the model's responsiveness and adaptability to national and local emission reduction policies.

[0032] Furthermore, the expression for the carbon price influencing factor is as follows:

[0033]

[0034] in, For the park's first The factors influencing carbon prices over a month For the park's first The change in carbon prices over a month. For the park's first Carbon price over a month, For the park's first The carbon price over a month.

[0035] The further beneficial effects mentioned above are as follows: by quantifying the negative correlation between carbon price changes and carbon emissions, the market regulation mechanism is introduced into carbon emission accounting, which can dynamically reflect the economic incentive effect of carbon trading policies on corporate emission behavior, solve the problem that traditional accounting methods ignore market factors, and improve the adaptability and prediction accuracy of the model in the carbon market environment.

[0036] Furthermore, the expression for the revised carbon emission accounting result of the industrial park is as follows:

[0037] in, For the park's first The revised carbon emission accounting results for the park over the past month. For the park's first Initial carbon emissions for the month, For the park's first Monthly production process correction factor For the park's first Monthly temperature impact correction factor For the park's first The factors influencing carbon prices over a month For the park's first Policy influencing factors over the months For the park's first The carbon emission accounting error over a month.

[0038] The further beneficial effects mentioned above are: dynamically coupling the initial carbon emissions with multiple maintenance positive factors such as process, temperature, carbon price, and policy, and introducing an error compensation term based on historical data, thus constructing a carbon emission accounting system that can comprehensively reflect the actual operating status and environmental impact of the park, and solving the problem of insufficient consideration of the coupling effect of multi-dimensional factors in traditional methods.

[0039] The beneficial effects of this invention are as follows: This invention solves the problem of insufficient consideration of multi-dimensional influencing factors such as production, environment, policy changes and market factors in traditional carbon emission accounting by integrating multi-source data and historical data for dynamic correction. It separates trend error and seasonal fluctuation by using seasonal and non-seasonal differences, and combines multiple correction factors to correct the data. It can improve the accuracy, adaptability and feasibility of carbon emission accounting without the need for high computing power resources, and provides an efficient and reliable carbon emission accounting method for various industrial parks. Attached Figure Description

[0040] Figure 1 This is a flowchart of the carbon emission accounting method for the industrial park, which is based on multidimensional data and historical data correction. Detailed Implementation

[0041] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0042] Example 1 like Figure 1 The diagram shows a flowchart of a carbon emission accounting method for industrial parks based on multidimensional data and historical data correction. Industrial parks, as the core carriers of industrial carbon emissions, need to accurately grasp the peak carbon emission time series, i.e., the peak year and peak emission amount. However, existing prediction schemes relying on AI algorithms require significant computing power and data, making them costly and difficult to implement for small and medium-sized parks. Traditional carbon emission accounting methods mostly use single time series accounting models, which ignore key factors such as raw material consumption and temperature, resulting in prediction errors exceeding 20%. Therefore, this invention provides a carbon emission accounting method for industrial parks based on multidimensional data and historical data correction, including the following steps: S1: Collect historical carbon emission data and multi-source data of the park, including at least the park's basic energy consumption data, production process data and external environmental data; S2: Calculate the initial carbon emissions of the park based on basic energy consumption data; S3: According to historical carbon emission data, by performing non-seasonal difference and seasonal difference processing, the carbon emission accounting error is calculated; S4: According to production process data and external environment data, a correction factor is calculated, which at least includes a production process correction factor, a gas temperature influence correction factor, a policy influence factor and a carbon price influence factor; S5: The initial carbon emission of the park, the carbon emission accounting error and the correction factor are comprehensively operated to obtain the corrected park carbon emission accounting result.

[0043] In an embodiment of the present application, in S1, the basic energy consumption data is obtained monthly, including: electricity, unit kWh, coal, unit ton, natural gas, unit m 3 , heat consumption, unit GJ, the acquisition method is enterprise electric meter / coal yard account book, gas company interface and heat supply report etc.; The production process data is obtained monthly, including: park operating parameters, unit load % and steam production, product output, unit ton, raw material consumption, unit ton, the acquisition method is park SCADA system, production management platform and raw material procurement account book; The external environment data is obtained monthly, including: regional electricity price, unit yuan / kWh, carbon price, unit yuan / ton CO2, policy emission reduction target, monthly average temperature, unit ℃, the acquisition method is power grid company, carbon trading platform, government document and weather station.

[0044] In an embodiment of the present application, in S2, the expression of the initial carbon emission of the park is as follows:

[0045]

[0046]

[0047]

[0048]

[0049]

[0050] Wherein, is the initial carbon emission of the park in the m th month, is the carbon emission of electricity generation, is the carbon emission of coal generation, is the carbon emission of natural gas generation, is the carbon emission of heat generation, is the carbon emission of raw material conversion, is the electricity consumption, is the coal consumption, is the gas consumption, is the heat consumption, is the raw material consumption, is the grid emission factor, is the coal emission factor, is the natural gas emission factor, is the thermal emission factor, is the raw material conversion efficiency factor; in specific embodiments of the present application, is the grid emission factor, preferably 0.61 tons of CO2 / MWh, is the coal emission factor, preferably 2.62 tons of CO2 / ton, is the natural gas emission factor, preferably 0.00208 tons of CO2 / m 3 , is the thermal emission factor, preferably 0.11 tons of CO2 / GJ, and the specific values can be adjusted according to historical carbon emission data and actual production of the park.

[0051] In an embodiment of the present application, there is a long-term growth or decline trend in the carbon emission data of the park, such as the growth of carbon emissions caused by capacity expansion, and seasonal fluctuations, such as the difference in carbon emissions caused by heating in winter and cooling in summer in a manufacturing park. For the above seasonal fluctuations, the present application eliminates trends and fluctuations through difference, accurately estimates accounting errors, and the expression of carbon emission accounting error in S3 is as follows:

[0052]

[0053]

[0054] wherein, is the carbon emission accounting error of the park in the th month, is the d-order non-seasonal difference, d=2 represents the comparison of carbon emission data between adjacent months, is the D-order seasonal difference, D=12 represents the comparison of carbon emission data between the current month and the same month of the previous year, is the total carbon emission of the park in the th month, is the total carbon emission of the park in the th month, is the total carbon emission of the park in the th month, is the total carbon emission of the park in the th month, is the total carbon emission of the park in the th month, is the total carbon emission of the park in the Total carbon emissions for the month; The second-order difference method is used to remove the trend changes in carbon emission data caused by long-term factors such as capacity expansion and technological upgrades, so that the data focuses on short-term fluctuations and eliminates trend interference for error estimation. This is a first-order seasonal difference method, using data from the same month in different years to eliminate carbon emission fluctuations caused by seasonal factors such as winter heating and summer production peaks, thus avoiding misjudging seasonal differences as accounting errors. The main consideration is the park's first The total carbon emissions for the month fluctuated compared to the carbon emissions of the previous two months. Mainly considering the relationship with the park's first The month corresponds to the same month last year, i.e., the fluctuation of the park's total carbon emissions between two adjacent months in December (t-12). The consideration is the first The seasonal error in carbon emission accounting for one month.

[0055] In one embodiment of the present invention, in step S4, the expression for the production process correction factor is as follows:

[0056] in, For the park's first Monthly production process correction factor For the park's first Monthly operating load, This represents the annual average of the park's operating load. For the park's first Monthly product output This represents the annual average output of products in the industrial park. 0.6 and 0.4 are weighting coefficients and can be adjusted based on the actual situation of the park. When both the park's load and output are higher than the annual average, The revised figure shows an increase in carbon emissions, reflecting the increase in carbon emissions under high load and high output. Conversely, when both the park's load and output are less than the annual average, The revised model reduces carbon emissions and addresses the issue of traditional models neglecting production fluctuations.

[0057] In one embodiment of the present invention, in S4, the temperature influence correction factor... It can quantify the nonlinear effects of temperature on carbon emissions, such as in winter. The lower, The larger, The larger the value, the greater the increase in carbon emissions; summer The higher, The larger, The larger the value, the greater the increase in carbon emissions. This solves the problem of traditional carbon emission accounting models ignoring environmental factors. The expression for the temperature impact correction factor is as follows:

[0058]

[0059]

[0060]

[0061] in, For the park's first Monthly temperature impact correction factor For the park's first The difference between the monthly average temperature and the baseline temperature over a period of time. For the park's first The average monthly temperature over the past few months; based on historical carbon emission data regression, it can be concluded that for every 10°C deviation of temperature from the baseline, carbon emissions deviate by approximately 20%. hour, Heating energy consumption increases by 20%, and carbon emissions increase by 20%. Therefore, we take 0.2 as the value. The coefficients can be obtained, and more repeated experiments can be conducted to obtain more accurate values. In a specific embodiment of the present invention, when the park's first Average monthly temperature for the month This indicates winter heating, which requires additional energy consumption for heating. The base temperature is taken as... When the park's first Average monthly temperature for the month At this time, representing the transitional season, there is no additional heating or cooling energy consumption, and the impact on temperature is 0; when the park's first Average monthly temperature for the month When this time, it represents summer cooling, which requires additional energy consumption for cooling. The base temperature is taken as... ; In one embodiment of the present invention, carbon emissions from industrial parks are also affected by policies such as emission reduction targets. The present invention transforms policy emission reduction targets into policy impact factors, and the impact of each policy is transformed into an emission reduction coefficient. In S4, the expression for the policy impact factor is as follows:

[0062] in, For the park's first Policy influencing factors over the months For the park's first The number of effective policies that impacted the park's carbon emissions within a month. For serial number, For the first The emission reduction coefficient of this policy For the first The weight of each policy item; In a specific embodiment of the present invention The value can be determined according to the policy classification standards: Industrial parks without explicit emission reduction requirements have no mandatory policies. Parks that require a reduction in carbon emission growth of no more than 5% generally have mandatory policies. The park is required to implement strict mandatory policies to reduce its carbon emission growth rate by no more than 10%. Industrial parks that require a reduction in carbon emission growth of more than 20% generally have mandatory policies. Meanwhile, policy emission reduction targets take into account the national, local, enterprise, and other levels. If only the national and local levels are considered, then... Taking a weight of 2, the national-level policy weight is 0.6, and the local-level policy weight is 0.4, resulting in... Policy Influence Factors Qualitative policies are transformed into quantitative adjustment coefficients; the stricter the policy, the better. The smaller the value, the lower the corrected carbon emissions, achieving a precise correlation between policy and carbon emissions and solving the problem of traditional models ignoring dynamic policy changes.

[0063] In one embodiment of the present invention, the expression for the carbon price influencing factor is as follows:

[0064]

[0065] in, For the park's first The factors influencing carbon prices over a month For the park's first The change in carbon prices over a month. For the park's first Carbon price over a month, For the park's first Carbon price over a month; In a specific embodiment of the present invention, based on historical carbon emission data patterns, for every 10 yuan / ton increase in carbon price, carbon emissions decrease by 3.5%. Therefore, the carbon price coefficient of the regression model can be dynamically adjusted to a value of - Adjustments need to be made based on the carbon emissions of different industrial parks; carbon price influencing factors The economic leverage effect that can quantify carbon prices means that when carbon prices rise, , A negative value indicates a reduction in carbon emissions after correction, reflecting companies' motivation to reduce emissions; when carbon prices fall, , If the result is positive, the carbon emissions will increase after the correction, thus addressing the problem that traditional models ignore market factors.

[0066] In one embodiment of the present invention, based on the calculated carbon emission accounting error, production process correction factor, temperature impact correction factor, carbon price impact factor, policy impact factor, and carbon emission accounting error, the initial carbon emission amount is corrected. In S5, the expression for the corrected carbon emission accounting result of the industrial park is as follows:

[0067] in, For the park's first The revised carbon emission accounting results for the park over the past month. For the park's first Initial carbon emissions for the month, For the park's first Monthly production process correction factor For the park's first Monthly temperature impact correction factor For the park's first The factors influencing carbon prices over a month For the park's first Policy influencing factors over the months For the park's first The carbon emission accounting error over a month.

[0068] The beneficial effects of this invention are as follows: This invention solves the problem of insufficient consideration of multi-dimensional influencing factors such as production, environment, policy changes and market factors in traditional carbon emission accounting by integrating multi-source data and historical data for dynamic correction. It separates trend error and seasonal fluctuation by using seasonal and non-seasonal differences, and combines multiple correction factors to correct the data. It can improve the accuracy, adaptability and feasibility of carbon emission accounting without the need for high computing power resources, and provides an efficient and reliable carbon emission accounting method for various industrial parks.

Claims

1. A park carbon emission accounting method based on multi-dimensional data and historical data correction, characterized in that, The method comprises the following steps: S1: collecting historical carbon emission data and multi-source data of the park, wherein the multi-source data at least comprises basic energy consumption data, production process data and external environment data of the park; S2: calculating initial carbon emission of the park according to the basic energy consumption data; S3: calculating carbon emission accounting error by performing non-seasonal difference and seasonal difference processing according to the historical carbon emission data; S4: calculating correction factors according to the production process data and the external environment data, wherein the correction factors at least comprise production process correction factor, air temperature influence correction factor, policy influence factor and carbon price influence factor; S5: comprehensively operating the initial carbon emission of the park, the carbon emission accounting error and the correction factors to obtain the corrected park carbon emission accounting result.

2. The park carbon emission accounting method based on multi-dimensional data and historical data correction according to claim 1, characterized in that, The expression of the initial carbon emission of the park in the S2 is as follows: wherein, is the initial carbon emissions of the park for the month, is the carbon emissions from electricity generation, is the carbon emissions from coal generation, is the carbon emissions from natural gas generation, is the carbon emissions from heat generation, is the carbon emissions from feedstock conversion, is the electricity consumption, is the coal consumption, is the gas consumption, is the heat consumption, is the feedstock consumption, is the grid emission factor, is the coal emission factor, is the natural gas emission factor, is the heat emission factor, is the feedstock conversion efficiency factor.

3. The park carbon emission accounting method based on multi-dimensional data and historical data correction according to claim 1, characterized in that, The expression of the carbon emission accounting error in the S3 is as follows: wherein, is the total carbon emission of the park in the month, is the d-th order non-seasonal difference, takes 2 to represent the comparison of carbon emission data between adjacent months, is the D-th order seasonal difference, takes 12 to represent the comparison of carbon emission data between this month and the same month of last year, is the total carbon emission of the park in the month, is the total carbon emission of the park in the month, is the total carbon emission of the park in the month, is the total carbon emission of the park in the month, is the total carbon emission of the park in the month, is the total carbon emission of the park in the month.

4. The park carbon emission accounting method based on multi-dimensional data and historical data correction according to claim 1, characterized in that, The expression of the production process correction factor is as follows: in, For the park's first Monthly production process correction factor For the park's first Monthly operating load, This represents the annual average of the park's operating load. For the park's first Monthly product output This represents the annual average output of products from the industrial park.

5. The park carbon emission accounting method based on multi-dimensional data and historical data correction according to claim 1, characterized in that, The expression of the air temperature influence correction factor is as follows: wherein, is a temperature influence correction factor for the month of the park, is a difference between the average temperature of the month of the park and the reference temperature, is the average temperature of the month of the park.

6. The park carbon emission accounting method based on multi-dimensional data and historical data correction according to claim 1, characterized in that, The expression of the policy influence factor is as follows: wherein, is the policy impact factor for the th month of the park, is the number of effective policies that have an impact on carbon emissions in the park in the th month, is the serial number, is the emission reduction coefficient of the th policy, is the weight of the th policy.

7. The park carbon emission accounting method based on multi-dimensional data and historical data correction according to claim 1, characterized in that, The expression of the carbon price influence factor is as follows: wherein, is a carbon price impact factor for the month of the park, is a carbon price change amount for the month of the park, is a carbon price for the month of the park, is a carbon price for the month of the park.

8. The park carbon emission accounting method based on multi-dimensional data and historical data correction according to claim 1, characterized in that, The expression of the corrected park carbon emission accounting result is as follows: The expression of the corrected park carbon emission accounting result is as follows: wherein, is the initial carbon emission of the park in the first month, is the revised carbon emission accounting result of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the production process correction factor of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month, is the initial carbon emission of the park in the first month.

Images