A method and device for calculating regional carbon emissions based on power data

Abstract

The present application relates to carbon emission calculation technical field, specifically provide a kind of regional carbon emission calculation method and device based on electric power data, comprising: using calculation model calculates the annual energy consumption data of the region to be analyzed and the annual product output data of each industry;Monthly energy consumption data and the monthly product output data of each industry of the region to be analyzed are determined based on the monthly electricity consumption data, annual energy consumption data and the annual product output data of each industry of the region to be analyzed;Monthly carbon emission of the region to be analyzed is determined based on monthly energy consumption data and the monthly product output data of each industry of the region to be analyzed.The technical scheme provided by the present application establishes the correlation between electric power data and energy activity, industrial production process, and calculates the monthly carbon emission of each region and each industry through the model framework of "calculating energy (output) with electricity and calculating carbon with energy (output)", which is theoretically and practically feasible.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of carbon emission calculation, in particular to a regional carbon emission calculation method and device based on power data. BACKGROUND

[0002] At present, carbon emission accounting mainly relies on energy consumption and carbon emission factors of main fossil energy. Due to the problems of insufficient granularity, lack of real-time, limited accuracy, single dimension and lack of close combination with information technology of source-end data, the accounting results are difficult to meet the calculation demand.

[0003] The carbon emission calculation method can be divided into two categories: one is calculation method, and the other is measurement method. The calculation method does not directly monitor or measure carbon dioxide, but indirectly calculates the carbon dioxide emission through emission activity data or material balance relationship, which is divided into two methods of emission factor method and material balance method. Among them, the material balance method is based on the law of conservation of mass and transformation, which calculates the material balance of chemical reaction process. The actual emission is obtained by subtracting the carbon contained in the product and waste from the input carbon content, which is used for enterprise carbon emission accounting. The advantage is that it is more accurate for single equipment, and the disadvantage is that the application range is limited, which is limited by the accuracy of mass, density and other measuring equipment. The emission factor method constructs the activity data and emission factor for each emission source according to the carbon emission inventory, and takes the product of activity data and emission factor as carbon emission. It is applied to carbon emission accounting of different dimension objects such as country, region and industry. Its advantage is wide application range and low cost, and its disadvantage is that it depends on a large amount of statistical data, and its time efficiency and resolution are not high. The measurement method is to directly measure the concentration and flow of carbon dioxide by measuring instrument for real-time monitoring, including satellite monitoring at macro level and continuous monitoring of flue gas emission at micro level. Among them, the satellite monitoring method is suitable for monitoring the carbon dioxide concentration of macro regional atmosphere, and the advantage is wide monitoring range, and the disadvantage is that it can only monitor the carbon concentration state and cannot directly monitor the carbon emission. The continuous monitoring method of flue gas emission is mainly applied to the emission source facilities (discharge port) of enterprises, and the advantage is high measurement accuracy and real-time measurement, and the disadvantage is high investment and operation cost.

[0004] At present, carbon emission accounting mainly relies on energy consumption and carbon emission factors of main fossil energy. Due to the problems of insufficient granularity, lack of real-time, limited accuracy, single dimension and lack of close combination with information technology of source-end data, the accounting results are difficult to meet the calculation demand. SUMMARY

[0005] In order to overcome the above defects, the present application provides a regional carbon emission calculation method and device based on power data.

[0006] In a first aspect, a method for calculating regional carbon emissions based on power data is provided, and the method comprises:

[0007] The annual electricity consumption data and the annual first supplementary variable of the region to be analyzed are used as inputs of the pre-constructed first electricity-to-energy model, and the annual energy consumption data of the region to be analyzed output by the pre-constructed first electricity-to-energy model is obtained.

[0008] The annual electricity consumption data and the annual second supplementary variable of each industry are used as inputs of the pre-constructed second electricity-to-energy model corresponding to each industry, and the annual product output data of each industry in the region to be analyzed output by the pre-constructed second electricity-to-energy model corresponding to each industry is obtained.

[0009] The monthly energy consumption data and the monthly product output data of each industry in the region to be analyzed are determined based on the monthly electricity consumption data, the annual energy consumption data, and the annual product output data of each industry in the region to be analyzed.

[0010] The monthly carbon emissions in the region to be analyzed are determined based on the monthly energy consumption data and the monthly product output data of each industry in the region to be analyzed.

[0011] Preferably, the first supplementary variable comprises at least one of the following: regional GDP, clean energy proportion;

[0012] The second supplementary variable comprises at least one of the following: industry production index, industry supply index.

[0013] Preferably, the obtaining process of the pre-constructed first electricity-to-energy model comprises:

[0014] The historical annual electricity consumption data, the historical annual first supplementary variable, and the historical annual energy consumption data of the region to be analyzed are used to construct training sample data;

[0015] The initial autoregressive distributed lag model is trained using the training sample data, and the pre-constructed first electricity-to-energy model is obtained.

[0016] Preferably, the obtaining process of the pre-constructed second electricity-to-energy model corresponding to each industry comprises:

[0017] The historical annual electricity consumption data, the historical annual second supplementary variable, and the historical annual product output data of each industry in the region to be analyzed are used to construct training sample data;

[0018] The initial autoregressive distributed lag model is trained using the training sample data, and the pre-constructed second electricity-to-energy model corresponding to each industry is obtained.

[0019] Preferably, the mathematical model of the pre-built first electric computing energy model or the pre-built second electric computing energy model corresponding to each industry is as follows:

[0020]

[0021] In the above formula, Y is the annual energy consumption of the region to be analyzed or the annual product output of each industry, a0 is the basic influencing factor, and Y t-j is the annual energy consumption of the region to be analyzed or the annual product output of each industry in year tj, t-j is the annual energy consumption impact coefficient of the region to be analyzed in year tj or the annual product output impact coefficient of each industry, J is the lag order of the annual energy consumption impact factor data of the region to be analyzed or the annual product output impact factor data of each industry, u t is the random deviation in year t, β t-p is the influence coefficient of the annual electricity consumption data of the region to be analyzed in year tp or the influence coefficient of the annual electricity consumption data of each industry, t-p is the annual electricity consumption data of the region to be analyzed or the annual electricity consumption data of each industry in the year tp, P is the lag order of the annual electricity consumption data of the region to be analyzed or the lag order of the annual electricity consumption data of each industry, γ t-q is the annual first supplementary variable influence coefficient or the annual second supplementary variable influence coefficient of the region to be analyzed in year tq, g t-q is the annual first supplementary variable or the annual second supplementary variable of the region to be analyzed in year tq, Q is the lag order of the annual first supplementary variable or the annual second supplementary variable of the region to be analyzed in year tq, and t is the current year.

[0022] Preferably, the determining of the monthly energy consumption data of the region to be analyzed and the monthly product output data of each industry based on the monthly electricity consumption data of the region to be analyzed, the annual energy consumption data and the annual product output data of each industry includes:

[0023] Substitute the monthly electricity consumption data and annual energy consumption data of the area to be analyzed into the pre-built monthly split model and solve it to obtain the monthly energy consumption data of the area to be analyzed;

[0024] The monthly electricity consumption data of each industry in the area to be analyzed and the annual product output data of each industry are substituted into the pre-built monthly split model and solved to obtain the monthly product output data of each industry in the area to be analyzed.

[0025] Furthermore, the mathematical model of the monthly split model is as follows:

[0026] y=arg min{(xy) T A(xy)}

[0027]

[0028] In the above formula, y is a 12-month energy consumption vector or a monthly product output vector of each industry of the region to be analyzed, x is a 12-month monthly electricity consumption data vector or a monthly electricity consumption data vector of each industry of the region to be analyzed, y m is the m-month energy consumption or the monthly product output of each industry of the region to be analyzed, A is a preset quadratic matrix, T is a transpose symbol, s.t. represents a constraint condition, and Y is the annual energy consumption or the annual product output of each industry of the region to be analyzed.

[0029] Preferably, the calculation formula of the monthly carbon emission of the region to be analyzed is as follows:

[0030]

[0031] In the above formula, C m is the m-month carbon emission of the region to be analyzed, a m is the m-month energy consumption of the region to be analyzed, b m,i is the m-month product output of the i-th industry of the region to be analyzed, I is the number of industry categories of the region to be analyzed, c m is the m-month carbon emission of the region to be analyzed, a′ is the carbon emission factor corresponding to the energy consumption, b

[0032] In a second aspect, a region carbon emission calculation device based on power data is provided, and the region carbon emission calculation device based on power data comprises:

[0033] A first analysis module is configured to input annual electricity consumption data and annual first supplementary variables of a region to be analyzed into a pre-constructed first electricity-to-energy model to obtain annual energy consumption data of the region to be analyzed output by the pre-constructed first electricity-to-energy model;

[0034] A second analysis module is configured to input annual electricity consumption data and annual second supplementary variables of each industry into a pre-constructed second electricity-to-energy model corresponding to each industry to obtain annual product output data of each industry of the region to be analyzed output by the pre-constructed second electricity-to-energy model corresponding to each industry;

[0035] A first determination module is configured to determine monthly energy consumption data and monthly product output data of each industry of the region to be analyzed based on monthly electricity consumption data, annual energy consumption data, and annual product output data of each industry of the region to be analyzed;

[0036] The second determining module is configured to determine monthly carbon emissions of the region to be analyzed based on monthly energy consumption data of the region to be analyzed and monthly product output data of each industry.

[0037] Preferably, the first supplementary variable comprises at least one of the following: regional GDP, clean energy proportion;

[0038] The second supplementary variable comprises at least one of the following: industry production index, industry supply index.

[0039] Preferably, the obtaining process of the first pre-constructed electricity-energy model comprises:

[0040] The training sample data is constructed by using historical annual electricity consumption data, historical annual first supplementary variable and historical annual energy consumption data of the region to be analyzed;

[0041] The initial autoregressive distributed lag model is trained by using the training sample data, and the first pre-constructed electricity-energy model is obtained.

[0042] Preferably, the obtaining process of the second pre-constructed electricity-energy model corresponding to each industry comprises:

[0043] The training sample data is constructed by using historical annual electricity consumption data, historical annual second supplementary variable and historical annual product output data of each industry of the region to be analyzed;

[0044] The initial autoregressive distributed lag model is trained by using the training sample data, and the second pre-constructed electricity-energy model corresponding to each industry is obtained.

[0045] Preferably, the mathematical model of the first pre-constructed electricity-energy model or the second pre-constructed electricity-energy model corresponding to each industry is as follows:

[0046]

[0047] In the above formula, Y represents annual energy consumption of the region to be analyzed or annual product output of each industry, a0 represents a basic influencing factor, Y t-j represents annual energy consumption of the region to be analyzed or annual product output of each industry in the t-jth year, φ t-j represents an annual energy consumption influencing degree coefficient of the region to be analyzed or an annual product output influencing degree coefficient of each industry, J represents an annual energy consumption influencing factor data lag order of the region to be analyzed or an annual product output influencing factor data lag order of each industry, u t represents a random deviation in the tth year, β t-p represents an annual electricity consumption influencing degree coefficient of the region to be analyzed or an annual electricity consumption influencing degree coefficient of each industry in the t-pth year, ft-p is the annual electricity consumption data of the region to be analyzed or the annual electricity consumption data of each industry in the t-p year, P is the lag order of the annual electricity consumption data of the region to be analyzed or the lag order of the annual electricity consumption data of each industry, γ t-q is the annual first supplementary variable influence degree coefficient or the annual second supplementary variable influence degree coefficient of the region to be analyzed in the t-q year, g t-q is the annual first supplementary variable or the annual second supplementary variable of the region to be analyzed in the t-q year, Q is the lag order of the annual first supplementary variable or the lag order of the annual second supplementary variable of the region to be analyzed in the t-q year, and t is the current year.

[0048] Preferably, the monthly energy consumption data and the monthly product output data of each industry of the region to be analyzed are determined based on the monthly electricity consumption data, the annual energy consumption data and the annual product output data of each industry of the region to be analyzed, comprising:

[0049] The monthly electricity consumption data and the annual energy consumption data of the region to be analyzed are substituted into the monthly splitting model constructed in advance and solved to obtain the monthly energy consumption data of the region to be analyzed;

[0050] The monthly electricity consumption data of each industry and the annual product output data of each industry of the region to be analyzed are substituted into the monthly splitting model constructed in advance and solved to obtain the monthly product output data of each industry of the region to be analyzed.

[0051] Further, the mathematical model of the monthly splitting model is as follows:

[0052] y = arg min {(x-y) T A(x-y)}

[0053]

[0054] In the above formula, y is the energy consumption vector of 12 months of the region to be analyzed or the monthly product output vector of each industry, x is the monthly electricity consumption data vector of 12 months of the region to be analyzed or the monthly electricity consumption data vector of each industry, y m is the energy consumption of the m month of the region to be analyzed or the monthly product output of each industry, A is a preset quadratic matrix, T is a transpose symbol, s.t. represents a constraint condition, and Y is the annual energy consumption of the region to be analyzed or the annual product output of each industry.

[0055] Preferably, the calculation formula of the monthly carbon emission of the region to be analyzed is as follows:

[0056]

[0057] In the formula, Cm is the carbon emission of the analyzed region in the mth month, am is the energy consumption of the analyzed region in the mth month, bm,i is the product output of the i th industry of the analyzed region in the mth month, I is the number of industry categories of the analyzed region, cm is the carbon emission amount of the analyzed region in the mth month, a' is the carbon emission factor corresponding to the energy consumption, and b i' is the carbon emission factor corresponding to the product output of the i th industry.

[0058] In a third aspect, a computer device is provided, comprising: one or more processors;

[0059] The processor is configured to store one or more programs.

[0060] When the one or more programs are executed by the one or more processors, the method for calculating regional carbon emission based on power data is implemented.

[0061] In a fourth aspect, a computer readable storage medium is provided, having a computer program stored thereon, and the computer program is executed to implement the method for calculating regional carbon emission based on power data.

[0062] The one or more technical solutions of the present application have at least one or more of the following beneficial effects:

[0063] The present application provides a method and device for calculating regional carbon emission based on power data, comprising: calculating annual energy consumption data and annual product output data of each industry of the analyzed region by using a calculation model; determining monthly energy consumption data and monthly product output data of each industry of the analyzed region based on monthly electricity consumption data, annual energy consumption data and annual product output data of each industry of the analyzed region; and determining monthly carbon emission of the analyzed region based on monthly energy consumption data and monthly product output data of each industry of the analyzed region. The technical solution provided by the present application establishes the correlation between power data and energy activities and industrial production processes, and calculates monthly carbon emission in different regions and industries, which is theoretically and practically feasible. Furthermore, the technical solution provided by the present application inherits and expands the IPCC system, forms an electricity-carbon analysis methodology, and proposes a calculation method of "calculating energy (output) based on electricity and calculating carbon based on energy (output)", and annual low-frequency data monthly splitting, which has good scientificity and innovation, and is an effective supplement to the existing carbon emission accounting method. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 FIG. 1 is a main step flow diagram of a method for calculating regional carbon emission based on power data according to an embodiment of the present application;

[0065] Figure 2 FIG. 2 is a main structure block diagram of a device for calculating regional carbon emission based on power data according to an embodiment of the present application. DETAILED DESCRIPTION

[0066] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0067] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0068] As disclosed in the background technology, current carbon emission accounting mainly relies on energy consumption and carbon emission factors of major fossil energy sources. Due to problems such as insufficient granularity, lack of real-time performance, limited accuracy, and single dimension of source data, and the lack of close integration with information technology, the accounting results are difficult to meet the measurement needs.

[0069] Carbon emission measurement methods can be divided into two categories: calculation and measurement. Calculation methods do not directly monitor or measure carbon dioxide (CO2). Instead, they indirectly calculate CO2 emissions through emission activity data or material balance relationships. These methods are categorized into emission factor and material balance methods. The material balance method, based on the laws of conservation and transformation of matter, calculates the material balance of chemical reaction processes. Actual emissions are calculated by subtracting the carbon content in products and waste from the input carbon content. This method is used for corporate carbon emission accounting. Its advantage lies in its more accurate calculations for individual equipment. Its disadvantage lies in its limited application, limited by the accuracy of measurement equipment such as mass and density. The emission factor method, based on the carbon emission inventory, constructs activity data and emission factors for each emission source. The product of the activity data and emission factors is used as the carbon emission figure. This method is applied to carbon emission accounting at the national, regional, and industry levels. Its advantages lie in its wide application and low cost. Its disadvantages lie in its reliance on large amounts of statistical data, which can result in limited timeliness and resolution. The field measurement method uses measuring instruments to directly monitor carbon dioxide concentrations and flow rates in real time. This includes macro-level satellite monitoring and micro-level continuous monitoring of flue gas emissions. Satellite monitoring is suitable for monitoring carbon dioxide concentrations in the atmosphere of macro-regions. Its advantage lies in its wide monitoring range, but its disadvantage is that it can only monitor carbon concentrations, not carbon emissions. Continuous flue gas emission monitoring is primarily used at enterprise emission source facilities (outlets). Its advantages lie in its high measurement accuracy and real-time performance, but its disadvantage lies in its high investment and operation and maintenance costs.

[0070] At present, carbon emission accounting mainly relies on energy consumption and carbon emission factors of main fossil energy. Due to the problems of insufficient granularity, lack of real-time, limited precision, single dimension and lack of close combination with information technology of source end data, the accounting results are difficult to meet the calculation requirements.

[0071] In order to improve the above problems, the application provides a regional carbon emission calculation method and device based on power data, comprising: calculating annual energy consumption data and annual product output data of each industry of the region to be analyzed by using a calculation model; determining monthly energy consumption data and monthly product output data of each industry of the region to be analyzed based on monthly electricity consumption data, annual energy consumption data and annual product output data of each industry of the region to be analyzed; and determining monthly carbon emission of the region to be analyzed based on monthly energy consumption data and monthly product output data of each industry of the region to be analyzed. The technical scheme provided by the application establishes the correlation between power data and energy activities and industrial production processes, and carries out monthly carbon emission calculation in different regions and different industries, which has theoretical and practical feasibility. Further, the technical scheme provided by the application inherits and expands on the basis of the IPCC system, forms an electricity-carbon analysis methodology, and proposes a calculation method of "calculating energy (output) by electricity and calculating carbon by energy (output)", annual low-frequency data monthly splitting, which has good scientificity and innovation, and is an effective supplement to the existing carbon emission accounting method. The above scheme will be described in detail below.

[0072] Embodiment 1

[0073] Refer to the accompanying Figure 1 , Figure 1 is the main step flowchart of the regional carbon emission calculation method based on power data of an embodiment of the application. As shown in Figure 1 , the regional carbon emission calculation method based on power data in the embodiment of the application mainly comprises the following steps:

[0074] Step S101: taking annual electricity consumption data and annual first supplementary variables of the region to be analyzed as inputs of a pre-constructed first electricity-to-energy model, to obtain annual energy consumption data of the region to be analyzed output by the pre-constructed first electricity-to-energy model;

[0075] Step S102: taking annual electricity consumption data of each industry and annual second supplementary variables as inputs of a pre-constructed second electricity-to-energy model corresponding to each industry, to obtain annual product output data of each industry of the region to be analyzed output by the pre-constructed second electricity-to-energy model corresponding to each industry;

[0076] Step S103: determining monthly energy consumption data and monthly product output data of the region to be analyzed based on monthly electricity consumption data, annual energy consumption data and annual product output data of each industry of the region to be analyzed;

[0077] Step S104: determining monthly carbon emissions of the region to be analyzed based on monthly energy consumption data and monthly product output data of each industry of the region to be analyzed.

[0078] The first supplementary variable includes at least one of the following: regional GDP, clean energy proportion; and the second supplementary variable includes: industry production index, industry supply index.

[0079] The embodiment proposes an "electricity-carbon analysis model", which reveals the internal correlation between electricity and carbon emissions through big data analysis, and constructs an electricity-carbon correlation function through a machine learning algorithm, fully utilizes the advantages of comprehensive, real-time and accurate power big data to solve the problems of poor timeliness and low accuracy of the existing carbon accounting system. Based on data aggregation, through the combination of electricity data and energy, economic, population and other statistical data, monthly carbon emission calculation is supported. The power grid electricity data is the key to improving timeliness, which fully utilizes the real-time data collection, intelligent calculation and big data management capabilities of the power grid, and can quickly and comprehensively reflect the energy use of each region and each industry. The "electricity-carbon analysis model" takes algorithm as the core, applies the autoregressive distributed lag model (ARDL) and the time series model (ARIMA) according to the principle of first overall and then partial and identity, and constructs an automatic parameter adjustment and self-adaptive "electricity-carbon analysis model".

[0080] Specifically, the obtaining process of the pre-constructed first electricity-energy model includes:

[0081] The training sample data is constructed by using the historical annual electricity consumption data, the historical annual first supplementary variable and the historical annual energy consumption data of the region to be analyzed;

[0082] The initial autoregressive distributed lag model is trained by using the training sample data, and the pre-constructed first electricity-energy model is obtained.

[0083] The obtaining process of the pre-constructed second electricity-energy model corresponding to each industry includes:

[0084] The training sample data is constructed by using the historical annual electricity consumption data, the historical annual second supplementary variable and the historical annual product output data of each industry of the region to be analyzed;

[0085] The initial autoregressive distributed lag model is trained by using the training sample data, and the pre-constructed second electricity-energy model corresponding to each industry is obtained.

[0086] In one embodiment, the pre-constructed first mathematical model of the energy consumption model or the pre-constructed second mathematical model of the energy consumption model corresponding to each industry is as follows:

[0087]

[0088] In the above formula, Y is the annual energy consumption of the region to be analyzed or the annual product output of each industry, a0 is a basic influencing factor, Y t-j is the annual energy consumption of the region to be analyzed or the annual product output of each industry in t-j years, φ t-j is the annual energy consumption of the region to be analyzed or the annual product output of each industry in t-j years, J is the data lag order of the annual energy consumption of the region to be analyzed or the data lag order of the annual product output of each industry, u t is the random deviation in t years, β t-p is the annual electricity consumption data of the region to be analyzed or the annual electricity consumption data of each industry in t-p years, f t-p is the annual electricity consumption data of the region to be analyzed or the annual electricity consumption data of each industry in t-p years, P is the data lag order of the annual electricity consumption data of the region to be analyzed or the data lag order of the annual electricity consumption data of each industry, γ t-q is the annual first supplementary variable of the region to be analyzed or the annual second supplementary variable in t-q years, g t-q is the annual first supplementary variable of the region to be analyzed or the annual second supplementary variable in t-q years, Q is the data lag order of the annual first supplementary variable of the region to be analyzed or the data lag order of the annual second supplementary variable in t-q years, and t is the current year.

[0089] In this embodiment, the monthly split method is used to calculate the monthly energy consumption data of all industries at the provincial level based on the monthly electricity consumption data of all industries at the provincial level. The monthly split is mainly used to convert low-frequency (annual) data into high-frequency (monthly) data. Since the electricity data has a significant strong correlation with energy activities, industrial process emissions and total carbon emissions, the monthly split based on the quadratic optimization algorithm takes the abstract distance between energy activities, industrial processes and electricity consumption data as the objective function, and calculates the monthly energy activities and industrial process data to minimize the objective function. Therefore, the monthly energy consumption data and the monthly product output data of each industry of the region to be analyzed are determined based on the monthly electricity consumption data, the annual energy consumption data and the annual product output data of each industry of the region to be analyzed, which comprises:

[0090] The monthly electricity consumption data and the annual energy consumption data of the region to be analyzed are substituted into the pre-constructed monthly split model and solved to obtain the monthly energy consumption data of the region to be analyzed;

[0091] The monthly electricity consumption data of each industry in the region to be analyzed and the annual product output data of each industry are substituted into the pre-constructed monthly splitting model and solved to obtain the monthly product output data of each industry in the region to be analyzed.

[0092] In one embodiment, the mathematical model of the monthly splitting model is as follows:

[0093] y = argmin {(x-y)TA(x-y)}

[0094]

[0095] In the above formula, y is a vector of energy consumption of 12 months in the region to be analyzed or a vector of monthly product output of each industry, x is a vector of monthly electricity consumption data of 12 months in the region to be analyzed or a vector of monthly electricity consumption data of each industry, ym is the energy consumption or the monthly product output of each industry in the mth month of the region to be analyzed, A is a preset quadratic matrix, T is a transpose symbol, s.t. represents a constraint condition, and Y is the annual energy consumption or the annual product output of each industry in the region to be analyzed.

[0096] In this embodiment, the energy consumption and industrial output data output by the "electricity-to-energy (output)" module are multiplied by the corresponding carbon emission factors to obtain the carbon emissions. The carbon emission factors include the general factors defined in the "Provincial Greenhouse Gas Inventory Compilation Guide", the "Enterprise Greenhouse Gas Emission Accounting Method and Reporting Guide (Trial)", and the dynamic power carbon emission factor calculated by the "electricity-to-energy (output)" module. Therefore, the calculation formula of the monthly carbon emissions of the region to be analyzed is as follows:

[0097]

[0098] In the above formula, Cm is the carbon emissions in the mth month of the region to be analyzed, am is the energy consumption in the mth month of the region to be analyzed, bm,i is the product output of the ith industry in the mth month of the region to be analyzed, I is the number of industry categories in the region to be analyzed, cm is the import and export carbon emissions in the mth month of the region to be analyzed, a' is the carbon emission factor corresponding to energy consumption, and b'i is the carbon emission factor corresponding to the product output of the ith industry.

[0099] In a specific embodiment, in the calculation process of the model, both the carbon emissions generated in the energy activities and industrial processes need to be calculated, and the import and export of electricity in the same region also needs to be considered. Therefore, in the model algorithm, the monthly import and export carbon emission data need to be calculated separately and then summed with the carbon emission data of the energy activities and industrial processes in the region.

[0100] Taking a certain region as an example, carbon emissions cover two parts of energy activities and industrial processes. The energy activity part is trained by the current and historical electricity data and historical energy consumption data to obtain an "electricity to energy" model, and the monthly electricity data is input to calculate the monthly energy consumption data in 2022, and the monthly energy carbon emissions in 2022 are obtained based on the emission factor method, and the monthly energy activity carbon emissions are obtained by adding the carbon emissions of the power carbon emissions of the region. The industrial process part is obtained by the current and historical electricity data and historical industry output data to obtain an "electricity to energy" model, and the monthly electricity data is input to calculate the monthly output data in 2022, and the corresponding monthly industrial production process carbon emissions are obtained based on the emission factor method. The sum of the energy activity part and the industrial process part is the monthly carbon emissions of the certain region in 2022.

[0101] The calculation process of the energy activity part is as follows Table 1:

[0102] Table 1

[0103]

[0104] The calculation process of the industrial process part of the certain region is as follows Table 2:

[0105] Table 2

[0106]

[0107]

[0108] The calculation process of the carbon emissions data of the certain region is as follows Table 3:

[0109] Table 3

[0110]

[0111] Embodiment 2

[0112] Based on the same inventive concept, the application also provides a regional carbon emission calculation device based on power data, as shown in Figure 2 The regional carbon emission calculation device based on power data comprises:

[0113] A first analysis module is configured to input annual electricity consumption data and annual first supplementary variables of a region to be analyzed into a pre-constructed first electricity-to-energy model to obtain annual energy consumption data of the region to be analyzed output by the pre-constructed first electricity-to-energy model.

[0114] A second analysis module is configured to input annual electricity consumption data and annual second supplementary variables of each industry into a pre-constructed second electricity-to-energy model corresponding to each industry to obtain annual product output data of each industry in the region to be analyzed output by the pre-constructed second electricity-to-energy model corresponding to each industry.

[0115] The first determining module is configured to determine monthly energy consumption data and monthly product output data of the region to be analyzed based on monthly electricity consumption data, annual energy consumption data and annual product output data of each industry of the region to be analyzed.

[0116] The second determining module is configured to determine monthly carbon emissions of the region to be analyzed based on the monthly energy consumption data and the monthly product output data of each industry of the region to be analyzed.

[0117] Preferably, the first supplementary variable comprises at least one of the following: regional GDP, clean energy proportion.

[0118] The second supplementary variable comprises: industry production index, industry supply index.

[0119] Preferably, the obtaining process of the pre-constructed first electricity-to-energy model comprises:

[0120] The training sample data is constructed by using historical annual electricity consumption data, historical annual first supplementary variable and historical annual energy consumption data of the region to be analyzed;

[0121] The initial autoregressive distributed lag model is trained by using the training sample data, and the pre-constructed first electricity-to-energy model is obtained.

[0122] Preferably, the obtaining process of the pre-constructed second electricity-to-energy model corresponding to each industry comprises:

[0123] The training sample data is constructed by using historical annual electricity consumption data, historical annual second supplementary variable and historical annual product output data of each industry of the region to be analyzed;

[0124] The initial autoregressive distributed lag model is trained by using the training sample data, and the pre-constructed second electricity-to-energy model corresponding to each industry is obtained.

[0125] Preferably, the mathematical model of the pre-constructed first electricity-to-energy model or the pre-constructed second electricity-to-energy model corresponding to each industry is as follows:

[0126]

[0127] In the above formula, Y is annual energy consumption of the region to be analyzed or annual product output of each industry, a0 is a basic influencing factor, Y t-j is annual energy consumption of the region to be analyzed or annual product output of each industry in t-j years, φ t-jis the influence degree coefficient of the annual energy consumption of the region to be analyzed or the influence degree coefficient of the annual product output of each industry in the year t, J is the lag order of the annual energy consumption influence factor data of the region to be analyzed or the lag order of the annual product output influence factor data of each industry, u t is the random deviation in the year t, β t-p is the influence degree coefficient of the annual electricity consumption data of the region to be analyzed or the influence degree coefficient of the annual electricity consumption data of each industry in the year t-p, f t-p is the annual electricity consumption data of the region to be analyzed or the annual electricity consumption data of each industry in the year t-p, P is the lag order of the annual electricity consumption data of the region to be analyzed or the lag order of the annual electricity consumption data of each industry, γ t-q is the influence degree coefficient of the annual first supplementary variable of the region to be analyzed or the influence degree coefficient of the annual second supplementary variable in the year t-q, g t-q is the annual first supplementary variable of the region to be analyzed or the annual second supplementary variable in the year t-q, Q is the lag order of the annual first supplementary variable of the region to be analyzed or the lag order of the annual second supplementary variable in the year t-q, t is the current year.

[0128] Preferably, the monthly energy consumption data and the monthly product output data of each industry of the region to be analyzed are determined based on the monthly electricity consumption data, the annual energy consumption data and the annual product output data of each industry of the region to be analyzed, and the method comprises the following steps:

[0129] The monthly electricity consumption data and the annual energy consumption data of the region to be analyzed are substituted into a pre-constructed monthly splitting model and solved to obtain the monthly energy consumption data of the region to be analyzed;

[0130] The monthly electricity consumption data of each industry of the region to be analyzed and the annual product output data of each industry are substituted into a pre-constructed monthly splitting model and solved to obtain the monthly product output data of each industry of the region to be analyzed.

[0131] Further, the mathematical model of the monthly splitting model is as follows:

[0132] y = arg min {(x-y) T A(x-y)}

[0133]

[0134] In the above formula, y is the energy consumption vector of 12 months of the region to be analyzed or the monthly product output vector of each industry, x is the monthly electricity consumption data vector of 12 months of the region to be analyzed or the monthly electricity consumption data vector of each industry, y mwherein, Cm is the carbon emission of the analyzed region in the mth month, a is a preset quadratic matrix, T is a transpose symbol, s.t. represents a constraint condition, Y is the annual energy consumption or annual product output of each industry of the analyzed region.

[0135] Preferably, the calculation formula of the monthly carbon emission of the analyzed region is as follows:

[0136]

[0137] In the above formula, Cm is the carbon emission of the analyzed region in the mth month, a is a preset quadratic matrix, T is a transpose symbol, s.t. represents a constraint condition, Y is the annual energy consumption or annual product output of each industry of the analyzed region. m m m,i m i In the above formula, Cm is the carbon emission of the analyzed region in the mth month, a is a preset quadratic matrix, T is a transpose symbol, s.t. represents a constraint condition, Y is the annual energy consumption or annual product output of each industry of the analyzed region. m m m,i m i In the above formula, Cm is the carbon emission of the analyzed region in the mth month, a is a preset quadratic matrix, T is a transpose symbol, s.t. represents a constraint condition, Y is the annual energy consumption or annual product output of each industry of the analyzed region.

[0138] Embodiment 3

[0139] Based on the same inventive concept, the present application also provides a computer device, which comprises a processor and a memory, the memory is used to store a computer program, the computer program comprises program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in the computer storage medium to implement a corresponding method flow or a corresponding function, so as to implement the steps of the above-mentioned embodiment of the regional carbon emission calculation method based on power data.

[0140] Embodiment 4

[0141] Based on the same inventive concept, the present application also provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in a computer device, used for storing programs and data. It can be understood that the computer readable storage medium here can include the built-in storage medium in the computer device, and of course can also include the extended storage medium supported by the computer device. The computer readable storage medium provides a storage space, which stores the operating system of the terminal. Moreover, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium here can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to realize the steps of the above-mentioned embodiment of the area carbon emission calculation method based on power data.

[0142] Those skilled in the art will appreciate that embodiments of the present application can be supplied as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0143] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus for performing the functions specified in one or more flows and / or blocks.

[0144] These computer program instructions can also be stored in a computer readable storage medium, which can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable storage medium produce a product including instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocksFigure 1 the function specified in the one or more blocks.

[0145] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processing, so that the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flow Figure 1 the flow or flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0146] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the above embodiments of the present application have been described in detail, those skilled in the art should understand: the specific embodiments of the present application can be modified or replaced by the same, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered within the scope of protection of the claims of the present application.

Claims

1. A method for calculating regional carbon emissions based on power data, characterized in that: The method comprises: Using the annual electricity consumption data of the region to be analyzed and the annual first supplementary variable as inputs of a pre-constructed first electric energy calculation model, and obtaining the annual energy consumption data of the region to be analyzed outputted by the pre-constructed first electric energy calculation model; Using the annual electricity consumption data of each industry and the annual second supplementary variable as inputs of the pre-constructed second electric energy model corresponding to each industry, obtaining the annual product output data of each industry in the region to be analyzed as output by the pre-constructed second electric energy model corresponding to each industry; Determine the monthly energy consumption data of the region to be analyzed and the monthly product output data of each industry based on the monthly electricity consumption data, annual energy consumption data and annual product output data of each industry in the region to be analyzed; Determine the monthly carbon emissions of the region to be analyzed based on the monthly energy consumption data of the region to be analyzed and the monthly product output data of each industry; The first supplementary variable includes at least one of the following: regional GDP, clean energy proportion; The second supplementary variables include: industry production index and industry supply index; The mathematical model of the pre-built first electric computing energy model or the pre-built second electric computing energy model corresponding to each industry is as follows: In the above formula, Y is the annual energy consumption of the region to be analyzed or the annual product output of each industry, a0 is the basic influencing factor, and Yt -j is the annual energy consumption of the region to be analyzed or the annual product output of each industry in year tj, -j is the annual energy consumption impact coefficient of the region to be analyzed in year tj or the annual product output impact coefficient of each industry, J is the lag order of the annual energy consumption impact factor data of the region to be analyzed or the annual product output impact factor data of each industry, ut is the random deviation of year t, βt -p is the influence coefficient of the annual electricity consumption data of the region to be analyzed in year tp or the influence coefficient of the annual electricity consumption data of each industry, ft -p is the annual electricity consumption data of the region to be analyzed or the annual electricity consumption data of each industry in year tp, P is the lag order of the annual electricity consumption data of the region to be analyzed or the lag order of the annual electricity consumption data of each industry, γt-q is the annual first supplementary variable influence coefficient or the annual second supplementary variable influence coefficient of the region to be analyzed in year tq, gt -q is the annual first supplementary variable or annual second supplementary variable of the region to be analyzed in year tq, Q is the lag order of the annual first supplementary variable or annual second supplementary variable of the region to be analyzed in year tq, and t is the current year; The calculation formula for the monthly carbon emissions of the region to be analyzed is as follows: In the above formula, Cm is the carbon emission of the region to be analyzed in month m, am is the energy consumption of the region to be analyzed in month m, bm ,i is the product output of the i-th industry in the region to be analyzed in month m, I is the number of industry categories in the region to be analyzed, c m is the carbon emissions of the region to be analyzed in month m, a′ is the carbon emission factor corresponding to energy consumption, b i ′ is the carbon emission factor corresponding to the product output of the i-th industry.

2. The method according to claim 1, wherein The process of obtaining the pre-built first computing energy model includes: The training sample data is constructed using the historical annual electricity consumption data, the historical annual first supplementary variable and the historical annual energy consumption data of the area to be analyzed; The initial autoregressive distributed lag model is trained using the training sample data to obtain the pre-constructed first computing energy model.

3. The method according to claim 1, wherein The process of obtaining the pre-built second computing energy model corresponding to each industry includes: The training sample data are constructed using the historical annual electricity consumption data of each industry in the region to be analyzed, the historical annual second supplementary variable and the historical annual product output data of each industry; The initial autoregressive distributed lag model is trained using the training sample data to obtain a pre-built second computing energy model corresponding to each industry.

4. The method according to claim 1, wherein The monthly energy consumption data of the region to be analyzed and the monthly product output data of each industry are determined based on the monthly electricity consumption data, annual energy consumption data and annual product output data of each industry in the region to be analyzed, including: Substitute the monthly electricity consumption data and annual energy consumption data of the area to be analyzed into the pre-built monthly split model and solve it to obtain the monthly energy consumption data of the area to be analyzed; The monthly electricity consumption data of each industry in the area to be analyzed and the annual product output data of each industry are substituted into the pre-built monthly split model and solved to obtain the monthly product output data of each industry in the area to be analyzed.

5. The method according to claim 4, wherein The mathematical model of the monthly split model is as follows: y = argmin{(xy)TA(xy)} In the above formula, y is the energy consumption vector of the region to be analyzed for 12 months or the monthly product output vector of each industry, x is the monthly electricity consumption data vector of the region to be analyzed for 12 months or the monthly electricity consumption data vector of each industry, and y m is the energy consumption of the region to be analyzed in month m or the monthly product output of each industry, A is the preset quadratic matrix, T is the transpose symbol, st represents the constraint condition, and Y is the annual energy consumption of the region to be analyzed or the annual product output of each industry.

6. A regional carbon emissions calculation device based on power data, characterized in that: The device comprises: a first analysis module, configured to use the annual electricity consumption data of the region to be analyzed and the annual first supplementary variable as inputs of a pre-constructed first electric energy calculation model, and obtain the annual energy consumption data of the region to be analyzed outputted by the pre-constructed first electric energy calculation model; The second analysis module is configured to use the annual electricity consumption data of each industry and the annual second supplementary variable as inputs of a pre-constructed second electric energy model corresponding to each industry, and obtain the annual product output data of each industry in the region to be analyzed as output by the pre-constructed second electric energy model corresponding to each industry; A first determination module is used to determine the monthly energy consumption data of the area to be analyzed and the monthly product output data of each industry based on the monthly electricity consumption data, annual energy consumption data and annual product output data of each industry in the area to be analyzed; The second determination module is used to determine the monthly carbon emissions of the region to be analyzed based on the monthly energy consumption data of the region to be analyzed and the monthly product output data of each industry; The first supplementary variable includes at least one of the following: regional GDP, clean energy proportion; The second supplementary variable includes at least one of the following: an industry production index and an industry supply index; The mathematical model of the pre-built first electric computing energy model or the pre-built second electric computing energy model corresponding to each industry is as follows: In the above formula, Y is the annual energy consumption of the region to be analyzed or the annual product output of each industry, a0 is the basic influencing factor, and Yt -j is the annual energy consumption of the region to be analyzed or the annual product output of each industry in year tj, -j is the annual energy consumption impact coefficient of the region to be analyzed in year tj or the annual product output impact coefficient of each industry, J is the lag order of the annual energy consumption impact factor data of the region to be analyzed or the annual product output impact factor data of each industry, ut is the random deviation of year t, βt -p is the influence coefficient of the annual electricity consumption data of the region to be analyzed in year tp or the influence coefficient of the annual electricity consumption data of each industry, ft -p is the annual electricity consumption data of the region to be analyzed or the annual electricity consumption data of each industry in year tp, P is the lag order of the annual electricity consumption data of the region to be analyzed or the lag order of the annual electricity consumption data of each industry, γt-q is the annual first supplementary variable influence coefficient or the annual second supplementary variable influence coefficient of the region to be analyzed in year tq, gt -q is the annual first supplementary variable or annual second supplementary variable of the region to be analyzed in year tq, Q is the lag order of the annual first supplementary variable or annual second supplementary variable of the region to be analyzed in year tq, and t is the current year; The calculation formula for the monthly carbon emissions of the region to be analyzed is as follows: In the above formula, Cm is the carbon emission of the region to be analyzed in month m, am is the energy consumption of the region to be analyzed in month m, bm , i is the product output of the i-th industry in the region to be analyzed in month m, I is the number of industry categories in the region to be analyzed, c m is the carbon emissions of the region to be analyzed in month m, a′ is the carbon emission factor corresponding to energy consumption, b i ′ is the carbon emission factor corresponding to the product output of the i-th industry.

7. The device according to claim 6, characterized in that The process of obtaining the pre-built first computing energy model includes: The training sample data is constructed using the historical annual electricity consumption data, the historical annual first supplementary variable and the historical annual energy consumption data of the area to be analyzed; The initial autoregressive distributed lag model is trained using the training sample data to obtain the pre-constructed first computing energy model.

8. The device according to claim 6, wherein The process of obtaining the pre-built second computing energy model corresponding to each industry includes: The training sample data are constructed using the historical annual electricity consumption data of each industry in the region to be analyzed, the historical annual second supplementary variable and the historical annual product output data of each industry; The initial autoregressive distributed lag model is trained using the training sample data to obtain a pre-built second computing energy model corresponding to each industry.

9. The device according to claim 6, wherein The monthly energy consumption data of the region to be analyzed and the monthly product output data of each industry are determined based on the monthly electricity consumption data, annual energy consumption data and annual product output data of each industry in the region to be analyzed, including: Substitute the monthly electricity consumption data and annual energy consumption data of the area to be analyzed into the pre-built monthly split model and solve it to obtain the monthly energy consumption data of the area to be analyzed; The monthly electricity consumption data of each industry in the area to be analyzed and the annual product output data of each industry are substituted into the pre-built monthly split model and solved to obtain the monthly product output data of each industry in the area to be analyzed.

10. The device according to claim 9, wherein The mathematical model of the monthly split model is as follows: y = argmin{(xy)TA(xy)} In the above formula, y is the energy consumption vector of the region to be analyzed for 12 months or the monthly product output vector of each industry, x is the monthly electricity consumption data vector of the region to be analyzed for 12 months or the monthly electricity consumption data vector of each industry, and y m is the energy consumption of the region to be analyzed in month m or the monthly product output of each industry, A is the preset quadratic matrix, T is the transpose symbol, st represents the constraint condition, and Y is the annual energy consumption of the region to be analyzed or the annual product output of each industry.

11. A computer device, characterized in that: include: one or more processors; The processor is configured to store one or more programs; When the one or more programs are executed by the one or more processors, the method for calculating regional carbon emissions based on power data as described in any one of claims 1 to 5 is implemented.

12. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed, it implements the regional carbon emission calculation method based on power data as described in any one of claims 1 to 5.

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