WO2024001045A1

WO2024001045A1 - Carbon quota surplus and deficit prediction method, apparatus, and electronic device, and storage medium

Info

Publication number: WO2024001045A1
Application number: PCT/CN2022/136032
Authority: WO
Inventors: 褚健; 崔山; 林想; 田利军; 吴玉成
Original assignee: 浙江中控技术股份有限公司
Priority date: 2022-06-30
Filing date: 2022-12-01
Publication date: 2024-01-04
Also published as: CN115169678A

Abstract

The present application relates to the technical field of data processing, and provided thereby are a carbon quota surplus and deficit prediction method, apparatus, and electronic device, and a storage medium. The carbon quota surplus and deficit prediction method comprises: firstly, acquiring a carbon emission intensity reference value of each carbon emission source in at least one preset working condition interval in a carbon emission main body to be predicted, each preset working condition interval corresponding to a parameter range of a preset working condition variable; then acquiring a production plan of each carbon emission source in a preset future time period and a prediction parameter of the preset working condition variable; according to the prediction parameter of the preset working condition variable, determining a target working condition interval from the at least one preset working condition interval; and finally, according to the carbon emission reference value and the production plan of the target working condition interval, performing carbon quota surplus and deficit prediction on the carbon emission main body, and obtaining a predicted carbon quota surplus and deficit amount of the carbon emission main body in the future time period. The present application can predict the carbon quota surplus and deficit circumstances of the carbon emission main body by means of historical data, the production plan, etc., of the carbon emission main body, assisting an enterprise in accurate compliance.

Description

Carbon quota surplus and deficit prediction methods, device electronic equipment and storage media

Technical field

The present invention relates to the field of data processing technology, and specifically to a carbon quota surplus and deficit prediction method, device electronic equipment and storage media.

Background technique

In recent years, the state has increasingly tightened its control over emissions, and has also introduced tools such as carbon trading to support companies in fulfilling their obligations and to reward companies for their emission reduction actions. If companies want to achieve precise compliance or increase transaction value, they must improve their own carbon emissions management. ability.

Most of the existing similar technologies for optimizing corporate carbon emission management are fragmented or too macroscopic. Fragmented technologies only focus on helping continuous production enterprises establish carbon emission databases and improve carbon emission accounting, although they have improved carbon emission indicator accounting. efficiency, but it still cannot empower enterprises to deepen the application of carbon emission data, and naturally cannot help enterprises gain benefits. Relatively macro-level technologies use big data processing methodologies from other fields. Most of these methodologies are applicable to regions or governments. In enterprises, It is extremely difficult to implement at the levels of factories and specific emission sources.

Contents of the invention

The purpose of the present invention is to provide a carbon quota surplus and shortage prediction method, device electronic equipment and storage medium in view of the above-mentioned shortcomings in the prior art, so as to assist continuous production enterprises to deepen the application of carbon emission data and realize carbon quota surplus and shortage prediction. accurate prediction.

In order to achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

In the first aspect, embodiments of the present application provide a method for predicting carbon quota surplus and deficit, which method includes:

Obtain the carbon emission intensity baseline value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval; each preset working condition interval corresponds to a parameter range of the preset working condition variable;

Obtain the production plan of each carbon emission source within a preset future period and the prediction parameters of the preset working condition variables;

Determine the target operating condition interval from the at least one preset operating condition interval according to the predicted parameters of the preset operating condition variable;

According to the carbon emission benchmark value of the target working condition interval and the production plan, the carbon quota surplus and deficit prediction is performed on the carbon emission entity to obtain the carbon quota surplus and deficit prediction amount of the carbon emission entity in the future period. .

Optionally, obtaining the carbon emission intensity baseline value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval includes:

According to the historical carbon emission data of each carbon emission source in each preset working condition interval, the carbon emission intensity benchmark value of each preset working condition interval is calculated.

Optionally, before obtaining the carbon emission intensity baseline value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval, the method further includes:

According to the historical carbon emission data of each carbon emission source within the preset historical period, the actual parameters of the preset working condition variables of each carbon emission source within the preset historical period are classified, and we obtain The at least one preset working condition interval.

Optionally, based on the historical carbon emission data of each carbon emission source within the preset historical period, the preset working condition variable of each carbon emission source within the preset historical period is calculated. Before classifying the actual parameters to obtain the at least one preset working condition interval, the method further includes:

Perform correlation analysis on the historical carbon emission data and the actual parameters of at least one working condition variable within the preset historical period to obtain the carbon emission correlation degree of the at least one working condition variable;

According to the carbon emission correlation degree of the at least one working condition variable, the working condition variable with the highest carbon emission correlation degree is determined from the at least one working condition variable as the preset working condition variable.

Optionally, the prediction parameters include: production load rate; the prediction parameters for obtaining the preset working condition variables of each carbon emission source within a preset future period include:

According to the production plan, the production load rate of the preset future period is determined.

Optionally, the prediction parameters also include: production process status parameters; and the prediction parameters for obtaining the preset working condition variables of each carbon emission source within a preset future period include:

The production process status parameters in the preset future time period are obtained from the historical contemporaneous data of each carbon emission source or the prediction database of the preset future time period.

Optionally, according to the carbon emission benchmark value of the target working condition interval and the production plan, the carbon quota surplus and deficit prediction is performed on the carbon emission entity to obtain the carbon emission entity's carbon quota surplus and deficit in the future period. Carbon allowance surplus and deficit forecast estimates include:

Predict the predicted amount of carbon emissions from each carbon emission source in the future period according to the carbon emission baseline value of the target working condition interval and the production plan;

Determine the predicted carbon emission amount of the carbon emission entity in the future period based on the predicted carbon emission amount of each carbon emission source in the carbon emission entity;

According to the predicted amount of carbon emissions of the carbon emitting entity and the amount of carbon emission quota of the carbon emitting entity, the predicted amount of carbon quota surplus or deficit of the carbon emitting entity in the future period is determined.

In the second aspect, embodiments of the present application also provide a carbon quota surplus and deficit prediction device, including: a carbon emission intensity benchmark value acquisition module, a prediction parameter acquisition module, a determination module, and a prediction module;

The carbon emission intensity benchmark value acquisition module is used to obtain the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval; each preset working condition interval corresponds to a preset A parameter range of the working condition variable;

The prediction parameter acquisition module is used to obtain the production plan of each carbon emission source within a preset future period and the prediction parameters of the preset working condition variables;

The determination module is configured to determine a target working condition interval from the at least one preset working condition interval according to the prediction parameter of the preset working condition variable;

The prediction module is used to predict the carbon quota surplus and deficit of the carbon emission subject according to the carbon emission benchmark value of the target working condition interval and the production plan, and obtain the carbon quota surplus and shortage of the carbon emission subject in the future period. Predictive measurement of carbon quota surplus and deficit.

In a third aspect, embodiments of the present application also provide an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores program instructions executable by the processor. When the electronic device is running, the The processor communicates with the storage medium through a bus, and the processor executes the program instructions to perform the steps of the carbon quota surplus and deficit prediction method as described in any one of the first aspects.

In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium, with a computer program stored on the storage medium, and the computer program executes the carbon quota as described in any one of the first aspects when run by the processor. Steps in the Profit and Loss Forecasting Method.

The beneficial effects of this application are: the embodiments of this application provide a carbon quota surplus and deficit prediction method, which first obtains the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval; Each preset working condition interval corresponds to a parameter range of the preset working condition variable; then the production plan of each carbon emission source in the preset future period and the prediction parameters of the preset working condition variable are obtained; according to the preset working condition variable The prediction parameters are used to determine the target working condition interval from at least one preset working condition interval; finally, according to the carbon emission benchmark value and the production plan of the target working condition interval, the carbon quota surplus and deficit forecast is carried out for the carbon emitting subject, and the carbon emitting subject is obtained Predicted measurement of carbon allowance surplus and deficit in future periods. Since in the continuous production process, there is a great correlation between production working conditions and emissions, and the working conditions are related to the production plan of the carbon emitting entity and other objective factors, they are traceable and predictable. Therefore, this paper provides a carbon quota surplus and deficit prediction method based on the production characteristics of continuous production enterprises and the shortcomings of existing technologies. It can predict carbon emissions through the historical data of carbon emission entities, actual production conditions, production plans, etc. The carbon quota surplus and shortage status of the entity can be analyzed to optimize the carbon trading strategy of the carbon emission entity and help the carbon emission entity to accurately fulfill its obligations.

Description of drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the drawings required to be used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and therefore do not It should be regarded as a limitation of the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a flow chart of a carbon quota surplus and deficit prediction method provided by an embodiment of the present application;

Figure 2 is a schematic diagram of a carbon emission source accounting boundary provided by an embodiment of the present application;

Figure 3 is a schematic diagram of a carbon emission model architecture provided by an embodiment of the present application;

Figure 4 is a schematic diagram of a carbon emission model architecture provided by another embodiment of the present application;

Figure 5 is a flow chart of a carbon quota surplus and deficit prediction method provided by another embodiment of the present application;

Figure 6 is a diagram showing the relationship between the carbon emission intensity and working condition variables of a carbon emission source provided by an embodiment of the present application;

Figure 7 is a flow chart of a carbon quota surplus and deficit prediction method provided by another embodiment of the present application;

Figure 8 is a schematic diagram of a carbon quota surplus and deficit prediction device provided by an embodiment of the present application;

FIG. 9 is a schematic diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, rather than all embodiments. In this application, unless otherwise expressly stated and limited, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. quantity. Therefore, the features defined as “first” and “second” may explicitly or implicitly include at least one feature. In the description of the present invention, "plurality" means at least two, such as two or three, unless otherwise clearly and specifically limited. The terms "comprises," "comprises," or any other variation thereof are intended to cover a non-exclusive inclusion such that a process, method, article or apparatus including a list of elements includes not only those elements but also others not expressly listed elements, or elements inherent to such process, method, article or equipment. Without further limitation, an element defined by the statement "comprises a..." does not exclude the presence of additional identical elements in a process, method, article, or apparatus that includes the stated element.

After China made its global commitment to achieve carbon peaking in 2030 and achieve carbon neutrality in 2060, units at all levels began to explore, carry out, and optimize their own carbon emission management work, especially those companies that need to fulfill their carbon quota obligations. . The present invention provides a quantitative prediction method and system for carbon quota surplus and deficit for continuous production enterprises, helping continuous production enterprises to deepen the application of carbon emission data, optimize carbon trading strategies, and achieve accurate compliance and asset appreciation.

The current carbon emission management of continuous production enterprises is relatively extensive, with manual reporting being the main form, and data timeliness is poor. In addition, there are no suitable data analysis methods and tools, resulting in a large amount of forward-looking and guiding value information being hidden and buried. In previous years, companies were only required to use carbon emissions data for annual compliance, and even if compliance was not completed, the penalties were not severe. However, in recent years, the state has increasingly tightened its control over emissions, and has also introduced tools such as carbon trading to support companies in fulfilling their obligations and to reward companies for their emission reduction actions. If companies want to achieve precise compliance or add value through transactions, they must increase their own carbon emissions. management capabilities.

Existing similar technologies for enterprise carbon emission management are fragmented or too macroscopic. The fragmented technology only focuses on helping continuous production enterprises to establish carbon emission databases and improve carbon emission accounting, but cannot empower enterprises to deepen the application of carbon emission data. To help enterprises gain benefits, relatively macro-level technologies use big data processing methodologies from other fields. These methodologies are extremely difficult to implement at the levels of enterprises, factories, and specific emission sources.

In view of the problems in the prior art, embodiments of the present application provide a variety of possible implementation methods to achieve accurate prediction of the carbon quota surplus and shortage of continuous production enterprises. The following is explained through multiple examples in conjunction with the accompanying drawings. Figure 1 is a flow chart of a carbon quota surplus and deficit prediction method provided by an embodiment of the present application. This method can be implemented by an electronic device running the above carbon quota surplus and deficit prediction method. The electronic device can be, for example, a terminal device, or it can for the server. As shown in Figure 1, the method includes:

Step 101: Obtain the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval; each preset working condition interval corresponds to a parameter range of the preset working condition variable.

It should be noted that the carbon emission entity is the prediction object of the carbon quota surplus and deficit prediction in this application. For example, it can be a continuous production enterprise, factory, etc., and this application does not limit this.

Each carbon emission subject may include one or more carbon emission sources. What is obtained in step 101 of this application can be the carbon emission intensity benchmark value of each carbon emission source in at least one preset working condition interval; the carbon emission sources in the carbon emission subject can also be screened, and the screened at least For a carbon emission source, what is obtained can be the carbon emission intensity baseline value of each carbon emission source in at least one preset working condition interval.

In a specific implementation manner, the carbon emission sources included in the accounting scope of the carbon emission subject can be screened based on the specific industry in which the carbon emission subject is located and the corresponding accounting standards. Figure 2 shows a carbon emission source provided by an embodiment of the present application. Schematic diagram of the source accounting boundary, as shown in Figure 2. There are three carbon emission sources in this carbon emission subject: emission source A, emission source B, and emission source C. Because the carbon emission source C is specified in the industry accounting standard where the carbon emission subject is located, are not included in the accounting scope (for ease of understanding, the accounting boundary is framed with a dotted line in Figure 2, and the carbon emission sources within the dotted line frame are included in the accounting scope), then when step 101 is executed, the emission source A and emission source B respectively obtained The carbon emission intensity baseline value in at least one preset working condition interval is sufficient.

In another specific implementation method, modeling software can be used to define the carbon emission model boundaries of each carbon emission subject, the emission indicators of each carbon emission source, configuration calculation formulas, etc., to establish the carbon emission model of the carbon emission subject. . Figure 3 is a schematic diagram of a carbon emission model architecture provided by an embodiment of the present application; Figure 4 is a schematic diagram of a carbon emission model architecture provided by yet another embodiment of the present application; as shown in Figures 3 and 4, in each carbon Among the emission subjects, the carbon emission model boundary of each carbon emission subject (the boundary framed by a dotted line in Figure 3), the emission indicators, configuration calculation formula, etc. of each carbon emission source can be determined (Figure 4). On the basis of this model, the carbon emission intensity baseline value of each carbon emission source in at least one preset working condition interval can be obtained.

It should also be noted that the preset working condition interval is an interval formed by the parameter range of the preset working condition variable. The preset working condition variable can be a single variable or a multi-variable. This application does not define the preset working condition variable. The specific quantity is not limited. If the preset working condition variable is a single variable, such as temperature, the working condition interval can be the interval corresponding to the temperature range (for example, 0 to 5 degrees Celsius can be one preset working condition interval, and 10 to 20 degrees Celsius can be another Preset working condition interval, etc.); if the preset working condition variable is multiple variables, such as temperature and load rate, then the working condition interval can be an interval jointly demarcated by the temperature range and the load rate range (for example, the temperature range is 0 to 5 degrees Celsius and a load rate of 50% to 60% can be a preset working condition interval, a temperature of 10 to 20 degrees Celsius and a load rate of 50% to 60% can be another preset working condition interval, etc.); and so on. , when there are more than two preset working condition variables, the parameter range of each preset working condition variable can also be used to form an interval.

In addition, the selection of the preset operating condition variables may be selected by engineering personnel or may be obtained by screening based on a preset algorithm, which is not limited in this application.

The above is only an example. In actual implementation, the preset working condition interval can also be set in other ways, which is not limited in this application.

Step 102: Obtain the production plan of each carbon emission source in the preset future period and the prediction parameters of the preset working condition variables.

In order to accurately predict the surplus and shortage of carbon quotas in the preset future period, it is necessary to obtain the production plan of each carbon emission source in the preset future period and the prediction parameters of the preset working condition variables. Among them, the production plan may include, for example: planned total output, production arrangements for each statistical period, etc.; according to the specific form of the preset working condition variables, the prediction parameters of the preset working condition variables can be obtained through historical data, relevant prediction data, etc. For example, if the preset operating condition variable is temperature, the prediction parameters of the temperature can be obtained by obtaining weather forecast data; for another example, if the preset operating condition variable is pressure, the pressure value can be obtained by obtaining the pressure value of the same historical period. Forecast parameters (such as obtaining parameters for the same period in previous years, etc.).

The above is only an example. In actual implementation, there may be other methods of obtaining prediction parameters, which are not limited in this application.

Step 103: Determine the target operating condition interval from at least one preset operating condition interval according to the prediction parameters of the preset operating condition variables.

It should be noted that one or more target operating condition intervals are determined from at least one preset operating condition interval based on the prediction parameters of the preset operating condition variables. This application does not limit the number of determined target operating condition intervals.

In a possible implementation method, if the preset future period is the next two months, each day corresponds to a preset working condition interval. Therefore, based on the prediction parameters of the preset working condition variables for each day, the prediction parameters for each day can be calculated. Determine a target operating range. The above is only an example. In actual implementation, the time period corresponding to each preset working condition interval can also be one hour, or several hours, etc. This application does not limit this. When obtaining the target working condition space, you can According to the accuracy of the specific working condition interval, determine the target working condition interval corresponding to each time period under the current accuracy.

Step 104: Based on the carbon emission benchmark value of the target working condition interval and the production plan, predict the carbon quota surplus and deficit of the carbon-emitting entity, and obtain the predicted amount of carbon quota surplus and deficit of the carbon-emitting entity in the future period.

In one possible implementation method, based on the carbon emission baseline value of the target working condition interval and the production plan, the predicted carbon emission amount of the carbon emission source in the future period can be predicted. By accumulating the predicted carbon emissions of all carbon emission sources (or carbon emission sources within the accounting scope) of the carbon-emitting entity, the total predicted amount of carbon emissions of the carbon-emitting entity in the future period can be obtained. Based on the carbon emission entity's carbon quota in the future period and the carbon emission entity's predicted total carbon emissions in the future period, the carbon emission entity's carbon quota surplus and deficit are forecast to obtain the carbon emission entity's carbon quota surplus in the future period. Missing measurement.

In summary, the embodiments of this application provide a method for predicting carbon quota surplus and deficit. First, the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted is obtained in at least one preset working condition interval; each preset The working condition interval corresponds to a parameter range of the preset working condition variable; then the production plan of each carbon emission source in the preset future period and the prediction parameters of the preset working condition variable are obtained; according to the prediction parameters of the preset working condition variable, Determine the target working condition interval from at least one preset working condition interval; finally, according to the carbon emission benchmark value and production plan of the target working condition interval, forecast the carbon quota surplus and deficit of the carbon emitting entity, and obtain the carbon emission subject's carbon quota surplus and deficit in the future period. Predictive measurement of carbon quota surplus and deficit. Since in the continuous production process, there is a great correlation between production working conditions and emissions, and the working conditions are related to the production plan of the carbon emission subject and other objective factors, which are traceable and predictable, therefore this application combines continuous Based on the production characteristics of manufacturing enterprises and the shortcomings of existing technologies, the carbon quota surplus and deficit prediction method provided can predict the carbon quota surplus and deficit of carbon emission entities through the historical data of carbon emission entities, actual production conditions, production plans, etc. situation, optimize the carbon trading strategy of carbon emitters, and help carbon emitters accurately fulfill their obligations.

Optionally, on the basis of the above Figure 1, this article also provides a possible implementation method of the carbon quota surplus and deficit prediction method to obtain at least one preset operating condition of each carbon emission source in the carbon emission subject to be predicted. The carbon emission intensity baseline value of the interval includes:

Based on the historical carbon emission data of each carbon emission source in each preset working condition interval, the carbon emission intensity baseline value of each preset working condition interval is calculated.

In one possible implementation, during actual use of each carbon emission source, at least one historical carbon emission data of each preset working condition interval can be collected. Based on the historical carbon emission data of each carbon emission source in each preset working condition interval, the carbon emission intensity benchmark value of each preset working condition interval can be calculated through arithmetic mean, median, clustering algorithm, etc.

The above is only an example. In actual implementation, the baseline value of carbon emission intensity can also be calculated in other ways, and this application does not limit this.

In a specific implementation manner, before calculating the carbon emission intensity baseline value of each preset working condition interval based on the historical carbon emission data of each carbon emission source in each preset working condition interval, the method also includes:

Classify the historical carbon emission data of each carbon emission source, and automatically classify each historical carbon emission data into the defined working condition interval.

If there is at least one target working condition interval in which the amount of historical carbon emission data is less than the preset data amount threshold, the target working condition interval and the adjacent preset working condition interval are preset with the smallest amount of historical carbon emission data. The working condition intervals are merged to obtain a new preset working condition interval.

The above is only an example. In actual implementation, there may be other implementation methods, which are not limited in this application.

Optionally, based on the above-mentioned Figure 1, this application also provides a possible implementation method of the carbon quota surplus and deficit prediction method to obtain at least one preset working condition interval for each carbon emission source in the carbon emission subject to be predicted. Before the carbon emission intensity baseline value, the method also includes:

According to the historical carbon emission data of each carbon emission source within the preset historical period, the actual parameters of the preset working condition variables of each carbon emission source within the preset historical period are classified to obtain at least one preset working condition interval.

In a possible implementation, the historical carbon emission data of each carbon emission source within the preset historical period can be obtained, and the historical carbon emission data of each carbon emission source within the preset historical period can be obtained. quantity, or historical carbon emission data, classify the actual parameters of the preset working condition variables of each carbon emission source within the preset historical period, so that each preset working condition interval obtained after classification includes a similar number of Historical carbon emission data makes the classification of preset working condition intervals more even, thereby further making the predicted carbon quota surplus and deficit more accurate.

In another possible implementation, when classifying the actual parameters of the preset working condition variables of each carbon emission source, the actual parameters of the preset working condition variables in the preset historical period (or the preset working condition variables of the carbon emission source) can be classified. Set the upper and lower limits of the theoretical parameters of the working condition variable), divide between the upper and lower limits according to the preset number of divisions, and divide the preset number of uniform preset working condition intervals.

In a specific implementation manner, the preset working condition variables may be, for example, temperature and load rate, and the divided preset working condition intervals may be, for example, as shown in Table 1. Table 1 is a preset working condition provided by an embodiment. Assume the working condition interval division table:

Table 1 A preset working condition interval division table provided by an embodiment of the present application

Optionally, based on the above embodiments, this application also provides a possible implementation method of a carbon quota surplus and deficit prediction method. Figure 5 is a flowchart of a carbon quota surplus and deficit prediction method provided by another embodiment of the present application. Figure; As shown in Figure 5, according to the historical carbon emission data of each carbon emission source in the preset historical period, the actual parameters of the preset working condition variables of each carbon emission source in the preset historical period are classified, and we get Before at least one preset working condition interval, the method also includes:

Step 501: Perform correlation analysis on the historical carbon emission data and the actual parameters of at least one working condition variable within the preset historical period to obtain the carbon emission correlation degree of at least one working condition variable.

In one possible implementation, correlation analysis is performed on historical carbon emission data and actual parameters of at least one working condition variable within a preset historical period to obtain the carbon emission correlation degree of at least one working condition variable. Among them, the working condition variables may include, for example, temperature, pressure, load rate, operating time, etc. This application does not limit this, and engineers can expand it according to specific carbon emission sources.

In addition, this application does not limit the specific calculation method of carbon emission correlation. Users can determine the calculation method based on actual use, such as: correlation algorithm, graphics method, etc.

Step 502: Based on the carbon emission correlation of at least one working condition variable, determine the working condition variable with the highest carbon emission correlation from at least one working condition variable as the preset working condition variable.

In a possible implementation, in step 501, the carbon emission correlation degrees of multiple working condition variables are calculated, and the working condition variable with the highest carbon emission correlation degree is selected as the preset working condition variable, or you can select The preset number of working condition variables with the highest carbon emission correlation (a preset number greater than or equal to one) are used as the preset working condition variables, or it can be determined from at least one working condition variable that is related (or strongly related) to the carbon emission intensity. ) as the preset working condition variable.

In a specific implementation manner, Figure 6 is a relationship diagram between the carbon emission intensity of a carbon emission source and working condition variables provided by an embodiment of the present application; as shown in Figure 6, according to the carbon emission intensity of emission source A From the relationship diagram with working condition variables such as temperature, load rate, pressure, and operating time, it can be seen that the carbon emission intensity of emission source A has a significant correlation with temperature and load rate. Therefore, temperature and load rate can be selected as the preset working condition variables. . It should be noted that the above-mentioned correlation is concretely displayed in the form of a relationship diagram (engineers can use this diagram to judge the accuracy of the selection of preset working condition variables), but when the computer program selects the preset working condition variables, it does not need to be specific. Generate this image.

Optionally, on the basis of the above Figure 1, this application also provides a possible implementation method of the carbon quota surplus and deficit prediction method. The prediction parameters include: production load rate; obtaining the output of each carbon emission source within a preset future period. Prediction parameters of preset working condition variables, including:

According to the production plan, determine the production load rate for the preset future period.

In a possible implementation, the production load rate Fh for the preset future period can be determined according to the production plan:

Fh _i = Q _i /full-load production; where Q _i is the planned production volume of the carbon emission source in the preset future period (i.e., the planned product production volume).

In a specific implementation method, if there is no specific product output arrangement in the production plan, the product output Q _i within the forecast period can be determined through the production plan M _i of the carbon emission source, and then the product output Q _i within the forecast period can be determined. The production load rate Fh _i . The above is only an example. In actual implementation, there may be other implementation methods, which are not limited in this application.

Optionally, based on the above Figure 1, this application also provides a possible implementation method of the carbon quota surplus and deficit prediction method. The prediction parameters also include: production process status parameters; obtaining each carbon emission source in a preset future period The prediction parameters of the preset working condition variables within include:

Obtain the production process status parameters in the preset future period from the historical contemporaneous data of each carbon emission source or the prediction database of the preset future period.

In a possible implementation, the preset future can be obtained through historical contemporaneous data (such as historical contemporaneous running time data, pressure data, etc.) or a prediction database for a preset future period (such as a temperature prediction database provided by weather forecasts). The production process status parameters within the period (such as obtaining the operating time data, pressure data, etc. in the preset future period from the historical operating time data, pressure data, etc. for the same period; for another example, obtaining the preset future period from the temperature prediction database provided by the weather forecast temperature inside).

The above is only an example. In actual implementation, there may be other prediction methods, which are not limited in this application.

Optionally, on the basis of the above-mentioned Figure 1, this application also provides a possible implementation method of a carbon quota surplus and deficit prediction method. Figure 7 is a flow chart of a carbon quota surplus and deficit prediction method provided by another embodiment of the present application. Figure; As shown in Figure 7, based on the carbon emission benchmark value and production plan of the target working condition interval, the carbon quota surplus and deficit prediction of the carbon emission entity is carried out, and the carbon quota surplus and deficit prediction amount of the carbon emission entity in the future period is obtained, including :

Step 301: Predict the predicted amount of carbon emissions from each carbon emission source in the future period based on the carbon emission baseline value of the target working condition interval and the production plan;

Step 302: Determine the predicted carbon emission amount of the carbon emission entity in the future period based on the predicted carbon emission amount of each carbon emission source in the carbon emission entity;

Step 303: Based on the carbon emission prediction amount of the carbon emission entity and the carbon emission quota amount of the carbon emission entity, determine the carbon quota surplus and deficit prediction amount of the carbon emission entity in the future period.

In one possible implementation method, the predicted amount of carbon emissions for each carbon emission source of the carbon-emitting entity in the future period (or each carbon emission source within the accounting scope) is the E _{emission source prediction} . The carbon-emitting entity in the future period The predicted amount of carbon emissions (i.e. the total predicted amount of carbon emissions of carbon emission entities in the future period) E _{carbon emission entity prediction} can be calculated in the following way:

E _{carbon emission subject prediction} = ∑ E _{emission source prediction} ;

Based on the carbon emission quota amount (carbon quota) D _{of the carbon emission entity in the future period and the predicted} total amount of carbon emissions of the carbon emission entity in the future period E _{the carbon emission entity prediction} , calculate the carbon emission entity's future period Predictive measurement of carbon allowance surplus and deficit within:

Carbon quota surplus and deficit forecast amount = D _{carbon emission subject forecast} - E _{carbon emission subject forecast} ;

It should be noted that the _{carbon emission subject prediction of} carbon quota D can be calculated in any of the following ways, and this application does not limit this:

First, the historical emission method can directly calculate the annual carbon quota amount D of the carbon emission entity through the historical average quota amount. D _i is the carbon quota amount of any carbon emission source in the carbon emission entity:

D _i =D _{i historical average} □ decline coefficient _{published by the country/local government} ; where i is greater than or equal to 1, the decline coefficient can be set according to the decline coefficient released by the local or national government.

Through the above method, the carbon quota amount of any carbon emission source (or any carbon emission source within the accounting scope) of each carbon emission entity can be obtained. Then the carbon quota amount of all carbon emission sources of each carbon emission entity is calculated. Accumulating, we can get the annual carbon quota amount D of the carbon emission entity:

Where N is the number of carbon emission sources (or any carbon emission source within the accounting scope).

Second, the historical intensity reduction method calculates the carbon quota D of the carbon emission entity during the prediction period through the product output Q during the prediction period. D _i is the carbon quota amount of any carbon emission source in the carbon emission entity:

D _i = Q _i □S _{i historical average} □ decline coefficient; where i is greater than or equal to 1, the decline coefficient can be set according to the decline coefficient released by local or national authorities;

Q _i is the product output corresponding to the carbon emission source, which can be obtained through the production plan of the carbon emission source;

S _{i historical average} is the historical average of the carbon emission intensity corresponding to the carbon emission source;

S _{i historical average} = E _{i history} /Q _{i history} ; where E _{i history} is the historical carbon emissions in the preset historical time period corresponding to the carbon emission source; Q _{i history} is the preset historical time corresponding to the carbon emission source segment’s product output;

E _i can be calculated as follows:

E _i =F _i +G _i +R _i -C _i ;

Among them, F _i is the fuel combustion emission of the carbon emission source;

G _i is the industrial process emissions of carbon emission sources;

R _i is the net electricity and heat emissions of carbon emission sources;

C _i is the amount of CO2 recycled from the carbon emission source.

Third, the baseline method calculates the enterprise carbon quota D during the prediction period through the product output Q during the prediction period. D _i is the carbon quota amount of any carbon emission source among the carbon emission entities:

D _i = Q _i □ benchmark value; where the benchmark value can be set according to the benchmark value issued by the local or national government;

The above are only examples. In actual implementation, there may be other carbon quota calculation methods, which are not limited in this application.

Therefore, this application provides a carbon quota surplus and deficit prediction method from carbon emission modeling and accounting to data deepening application.

The following is a description of the device, electronic equipment, storage media, etc. used to implement the carbon quota surplus and deficit prediction provided in this application. The specific implementation process and technical effects are as mentioned above, and will not be described again below.

The embodiments of this application provide a possible implementation example of a carbon quota surplus and deficit prediction device, which can execute the carbon quota surplus and deficit prediction method provided in the above embodiments. Figure 8 is a schematic diagram of a carbon quota surplus and deficit prediction device provided by an embodiment of the present application. As shown in Figure 8, the above-mentioned carbon quota surplus and deficit prediction device 100 includes: a carbon emission intensity benchmark value acquisition module 81, a prediction parameter acquisition module 83, a determination module 85, and a prediction module 87;

The carbon emission intensity benchmark value acquisition module 81 is used to obtain the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval; each preset working condition interval corresponds to the preset working condition. A parameter range of the condition variable;

The prediction parameter acquisition module 83 is used to obtain the production plan of each carbon emission source in the preset future period and the prediction parameters of the preset working condition variables;

The determination module 85 is used to determine the target working condition interval from at least one preset working condition interval according to the prediction parameters of the preset working condition variable;

The prediction module 87 is used to predict the carbon quota surplus and deficit of the carbon-emitting entity based on the carbon emission benchmark value of the target working condition interval and the production plan, and obtain the predicted amount of carbon quota surplus and deficit of the carbon-emitting entity in the future period.

Optionally, the carbon emission intensity benchmark value acquisition module 81 is used to calculate the carbon emission intensity benchmark value for each preset working condition interval based on the historical carbon emission data of each carbon emission source in each preset working condition interval.

Optionally, the carbon quota surplus and deficit forecasting device 100 also includes: a calculation module, configured to calculate the historical carbon emission data of each carbon emission source within the preset historical period based on the historical carbon emission data of each carbon emission source within the preset historical period. The actual parameters of the preset working condition variables are classified to obtain at least one preset working condition interval.

Optionally, the calculation module is used to perform correlation analysis on historical carbon emission data and the actual parameters of at least one working condition variable within the preset historical period to obtain the carbon emission correlation degree of at least one working condition variable; based on at least one working condition variable The carbon emission correlation degree of the working condition variable is determined, and the working condition variable with the highest carbon emission correlation degree is determined from at least one working condition variable as the preset working condition variable.

Optionally, the prediction parameters include: production load rate; the prediction module 87 is used to determine the production load rate of the preset future period according to the production plan.

Optionally, the prediction parameters also include: production process status parameters; the prediction module 87 is used to obtain the production process status in the preset future period from the historical contemporaneous data of each carbon emission source or the prediction database of the preset future period. parameter.

Optionally, the prediction module 87 is used to predict the predicted carbon emission amount of each carbon emission source in the future period based on the carbon emission benchmark value of the target working condition interval and the production plan; according to the carbon emission amount of each carbon emission source in the carbon emission subject The predicted amount of emissions determines the predicted amount of carbon emissions of the carbon-emitting entity in the future period; based on the predicted amount of carbon emissions of the carbon-emitting entity and the amount of carbon emission quotas of the carbon-emitting entity, determines the prediction of carbon quota surplus and shortage of the carbon-emitting entity in the future period. quantity.

The above device is used to execute the method provided in the foregoing embodiments. Its implementation principles and technical effects are similar and will not be described again here.

The above modules may be one or more integrated circuits configured to implement the above methods, such as: one or more application specific integrated circuits (ASIC for short), or one or more microprocessors (digital singnal processor, referred to as DSP), or one or more Field Programmable Gate Arrays (Field Programmable GateArray, referred to as FPGA), etc. For another example, when one of the above modules is implemented in the form of a processing element scheduler code, the processing element can be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU for short) or other processors that can call program code. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

The embodiments of this application provide a possible implementation example of an electronic device that can execute the carbon quota surplus and deficit prediction method provided in the above embodiments. Figure 9 is a schematic diagram of an electronic device provided by an embodiment of the present application. The device can be integrated into a terminal device or a chip of the terminal device. The terminal can be a computing device with data processing functions.

The electronic device includes: a processor 901, a storage medium 902 and a bus. The storage medium stores program instructions executable by the processor. When the electronic device is running, the processor and the storage medium communicate through the bus, and the processor executes the program instructions. The steps of the above carbon quota surplus and deficit prediction method are performed when executing. The specific implementation methods and technical effects are similar and will not be described again here.

The embodiment of the present application provides a possible implementation example of a computer-readable storage medium that can execute the carbon quota surplus and deficit prediction method provided in the above embodiment. The storage medium stores a computer program, and the computer program executes the above carbon quota when run by the processor. Steps in the Profit and Loss Forecasting Method.

A computer program stored in a storage medium may include a number of instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor (English: processor) to execute the various embodiments of the present invention. Some steps of the method. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviation: ROM), random access memory (English: Random Access Memory, abbreviation: RAM), magnetic disk or optical disk, etc. Various media that can store program code.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the coupling or direct coupling or communication connection between each other shown or discussed may be through some interfaces, and the indirect coupling or communication connection of the devices or units may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of hardware plus software functional units.

The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium and includes a number of instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to execute the various embodiments of the present invention. Some steps of the method. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviation: ROM), random access memory (English: Random Access Memory, abbreviation: RAM), magnetic disk or optical disk, etc. Various media that can store program code.

The above are only specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, and they should be covered by within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

A carbon quota surplus and deficit prediction method, characterized in that the method includes:

Obtain the carbon emission intensity baseline value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval; each preset working condition interval corresponds to a parameter range of the preset working condition variable;

Obtain the production plan of each carbon emission source within a preset future period and the prediction parameters of the preset working condition variables;

Determine a target operating condition interval from the at least one preset operating condition interval according to the prediction parameter of the preset operating condition variable;

According to the carbon emission benchmark value of the target working condition interval and the production plan, the carbon quota surplus and deficit prediction is performed on the carbon emission entity to obtain the carbon quota surplus and deficit prediction amount of the carbon emission entity in the future period. .
The method of claim 1, wherein obtaining the carbon emission intensity baseline value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval includes:

According to the historical carbon emission data of each carbon emission source in each preset working condition interval, the carbon emission intensity benchmark value of each preset working condition interval is calculated.
The method according to claim 1, characterized in that before obtaining the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval, the method further includes:

According to the historical carbon emission data of each carbon emission source within the preset historical period, the actual parameters of the preset working condition variables of each carbon emission source within the preset historical period are classified, and we obtain The at least one preset working condition interval.
The method of claim 3, wherein, based on the historical carbon emission data of each carbon emission source within the preset historical period, the method for each carbon emission source within the preset historical period is Before classifying the actual parameters of the preset working condition variables and obtaining the at least one preset working condition interval, the method further includes:

Perform correlation analysis on the historical carbon emission data and the actual parameters of at least one working condition variable within the preset historical period to obtain the carbon emission correlation degree of the at least one working condition variable;

According to the carbon emission correlation degree of the at least one working condition variable, the working condition variable with the highest carbon emission correlation degree is determined from the at least one working condition variable as the preset working condition variable.
The method of claim 1, wherein the prediction parameters include: production load rate; and the prediction parameters for obtaining the preset working condition variables of each carbon emission source within a preset future period include :

According to the production plan, the production load rate of the preset future period is determined.
The method of claim 1, wherein the prediction parameters further include: production process status parameters; and the prediction parameters for obtaining the preset working condition variables of each carbon emission source within a preset future period. ,include:

The production process status parameters in the preset future time period are obtained from the historical contemporaneous data of each carbon emission source or the prediction database of the preset future time period.
The method according to claim 1, characterized in that, based on the carbon emission benchmark value of the target working condition interval and the production plan, a carbon quota surplus and deficit prediction is performed on the carbon emission subject to obtain the carbon The carbon allowance surplus and deficit forecast of the emitting entity in the future period includes:

Predict the predicted amount of carbon emissions from each carbon emission source in the future period according to the carbon emission baseline value of the target working condition interval and the production plan;

Determine the predicted carbon emission amount of the carbon emission entity in the future period based on the predicted carbon emission amount of each carbon emission source in the carbon emission entity;

According to the predicted amount of carbon emissions of the carbon emitting entity and the amount of carbon emission quota of the carbon emitting entity, the predicted amount of carbon quota surplus or deficit of the carbon emitting entity in the future period is determined.
A carbon quota surplus and deficit prediction device, which is characterized in that it includes: a carbon emission intensity benchmark value acquisition module, a prediction parameter acquisition module, a determination module, and a prediction module;

The carbon emission intensity benchmark value acquisition module is used to obtain the carbon emission intensity benchmark value of each carbon emission source in the carbon emission subject to be predicted in at least one preset working condition interval; each preset working condition interval corresponds to a preset A parameter range of the working condition variable;

The prediction parameter acquisition module is used to obtain the production plan of each carbon emission source within a preset future period and the prediction parameters of the preset working condition variables;

The determination module is configured to determine a target working condition interval from the at least one preset working condition interval according to the prediction parameter of the preset working condition variable;

The prediction module is used to predict the carbon quota surplus and deficit of the carbon emission subject according to the carbon emission benchmark value of the target working condition interval and the production plan, and obtain the carbon quota surplus and shortage of the carbon emission subject in the future period. Predictive measurement of carbon quota surplus and deficit.
An electronic device, characterized in that it includes: a processor, a storage medium and a bus. The storage medium stores program instructions executable by the processor. When the electronic device is running, the processor and the storage medium Through bus communication, the processor executes the program instructions, so as to execute the steps of the carbon quota surplus and deficit prediction method as described in any one of claims 1 to 7.
A computer-readable storage medium, characterized in that a computer program is stored on the storage medium, and when the computer program is run by a processor, it executes the carbon quota surplus and deficit prediction method as described in any one of claims 1 to 7. step.