CN116468151A - Carbon emission calculation method, carbon emission calculation device, computer equipment and storage medium - Google Patents
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Abstract
The scheme relates to a carbon emission calculation method, a carbon emission calculation device, computer equipment and a storage medium. The method comprises the following steps: acquiring carbon emission sources and historical electricity consumption data corresponding to each industry; determining the actual value of the carbon emission corresponding to each carbon emission source according to a carbon emission factor method and a material balance method; determining the association relation between the actual value of the carbon emission and the historical electricity consumption data, and obtaining a predicted value of the carbon emission; calculating a residual value and a residual floating rate according to actual values of industry carbon check data and report data, and correcting a carbon emission predicted value by using a Markov chain model; and adding the corrected values of the carbon emission amounts to obtain the total carbon emission amount of the target industry, forming a corrected carbon emission calculation model, and calculating the carbon emission in real time according to the change data of the power consumption amount. By collecting historical electricity consumption data and actual carbon emission values of all carbon emission sources, an association relation between the historical electricity consumption data and the actual carbon emission values is established, a calculation method for calculating carbon by electricity is established, and the accuracy of carbon emission calculation is improved.
Description
Technical Field
The present invention relates to the field of carbon emission measurement technologies, and in particular, to a carbon emission calculation method, a carbon emission calculation device, a computer device, and a storage medium.
Background
At present, on one hand, the carbon emission data of enterprises are mainly obtained through statistical accounting of data reports such as energy consumption and the like, and on the other hand, the verification of the carbon emission data also needs complicated data cross-validation verification, a great deal of manpower, material resources and financial resources are consumed, and hysteresis of up to one natural year exists on time. Although some enterprises build energy consumption monitoring platforms, the real-time monitoring function of carbon emission cannot be realized, and mainly other carbon emission data cannot be accurately measured and obtained in real time.
Therefore, the conventional carbon emission calculation method has a problem of inaccurate calculation.
Disclosure of Invention
In order to solve the above technical problems, a carbon emission calculation method, a device, a computer device, and a storage medium are provided, which can improve the accuracy of carbon emission calculation.
A carbon emission calculation method, the method comprising:
acquiring carbon emission sources corresponding to each industry, and acquiring historical electricity consumption data of each industry;
determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method;
determining an association relation between the actual value of the carbon emission and the historical electricity consumption data, and acquiring a predicted value of the carbon emission;
calculating a residual error value between the carbon emission predicted value and the industrial carbon check data and the actual value of report data, calculating a residual error floating rate according to the residual error value and the carbon emission predicted value, and correcting the carbon emission predicted value by using a Markov chain model;
and adding all the carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of the carbon emission according to the power consumption change data.
In one embodiment, the determining the association relationship between the actual value of the carbon emission amount and the historical electricity consumption amount data includes:
based on a gray prediction method, performing data fitting on the actual value of the carbon emission and the historical electricity consumption data;
and according to a data fitting result, determining the association relation between the actual value of the carbon emission and the historical electricity consumption data.
In one embodiment, the data fitting the actual carbon emission value and the historical electricity consumption data based on the gray prediction method includes:
taking the actual value of the carbon emission as a system research sequence and the historical electricity consumption data as a related factor sequence;
and fitting a relation between the actual value of the carbon emission and the historical electricity consumption data by using the gray prediction method according to the system research sequence and the related factor sequence.
In one embodiment, the correcting the predicted value of the carbon emission amount by using a markov chain model includes:
acquiring the range of the residual floating rate, and dividing a correction state interval according to the range of the residual floating rate;
acquiring a state interval in which the carbon emission predicted value is located, and calculating a Markov state transition probability matrix according to the corrected state interval;
and carrying out prediction verification and correction on the carbon emission predicted value according to the state interval in which the carbon emission predicted value is located, the Markov state transition probability matrix and the correction state interval.
In one embodiment, determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the carbon emission factor method and the material balance method includes:
determining a carbon emission factor and a carbon emission activity level corresponding to each carbon emission source;
and calculating the product between the carbon emission factor and the carbon emission activity level, and determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the product.
In one embodiment, the carbon emissions source comprises carbon emissions generated by combustion of fossil fuels, carbon emissions generated in industrial processes, outsourced electricity/thermal carbon emissions.
A carbon emission calculation device, the device comprising:
the data acquisition module is used for acquiring carbon emission sources corresponding to each industry and acquiring historical electricity consumption data of each industry;
the historical carbon emission determining module is used for determining actual carbon emission values corresponding to the carbon emission sources in each industry according to a carbon emission factor method and a material balance method;
the incidence relation determining module is used for determining the incidence relation between the actual value of the carbon emission and the historical electricity consumption data and obtaining a predicted value of the carbon emission;
the correction module is used for calculating a residual error value between the carbon emission predicted value and the carbon emission predicted value according to actual values of industry carbon check data and report data, calculating residual error floating rate according to the residual error value and the carbon emission predicted value, and correcting the carbon emission predicted value by using a Markov chain model;
and the real-time carbon emission calculation module is used for summing the corrected carbon emission values through the corrected carbon emission predicted values to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model and realizing the real-time calculation of the carbon emission according to the change data of the power consumption.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring carbon emission sources corresponding to each industry, and acquiring historical electricity consumption data of each industry;
determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method;
determining an association relation between the actual value of the carbon emission and the historical electricity consumption data, and acquiring a predicted value of the carbon emission;
calculating a residual error value between the carbon emission predicted value and the industrial carbon check data and the actual value of report data, calculating a residual error floating rate according to the residual error value and the carbon emission predicted value, and correcting the carbon emission predicted value by using a Markov chain model;
and adding all the carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of the carbon emission according to the power consumption change data.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring carbon emission sources corresponding to each industry, and acquiring historical electricity consumption data of each industry;
determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method;
determining an association relation between the actual value of the carbon emission and the historical electricity consumption data, and acquiring a predicted value of the carbon emission;
calculating a residual error value between the carbon emission predicted value and the industrial carbon check data and the actual value of report data, calculating a residual error floating rate according to the residual error value and the carbon emission predicted value, and correcting the carbon emission predicted value by using a Markov chain model;
and adding all the carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of the carbon emission according to the power consumption change data.
According to the carbon emission calculation method, the carbon emission calculation device, the computer equipment and the storage medium, the carbon emission sources corresponding to all industries are obtained, and the historical electricity consumption data of all industries are obtained; determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method; determining an association relation between the actual value of the carbon emission and the historical electricity consumption data, and acquiring a predicted value of the carbon emission; calculating a residual error value between the carbon emission predicted value and the industrial carbon check data and the actual value of report data, calculating a residual error floating rate according to the residual error value and the carbon emission predicted value, and correcting the carbon emission predicted value by using a Markov chain model; and adding all the carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of the carbon emission according to the power consumption change data. By collecting historical electricity consumption data and actual carbon emission values and establishing an association relationship between the historical electricity consumption data and the actual carbon emission values, the electricity data and the carbon emission are related, an electricity-based carbon calculation method is constructed, dynamic analysis and real-time calculation of carbon emission based on large electricity data are realized, and the accuracy of carbon emission calculation is improved.
Drawings
FIG. 1 is an application environment diagram of a carbon emission calculation method in one embodiment;
FIG. 2 is a flow chart of a method of carbon emission calculation in one embodiment;
FIG. 3 is a block diagram of a carbon emission calculation device in one embodiment;
fig. 4 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The carbon emission calculation method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. As shown in FIG. 1, the application environment includes a computer device 110. The computer device 110 may obtain carbon emission sources corresponding to each industry and obtain historical electricity consumption data of each industry; the computer device 110 may determine actual values of carbon emissions corresponding to respective carbon emissions sources of respective industries according to a carbon emission factor method and a material balance method; the computer device 110 may determine an association relationship between the actual value of the carbon emission amount and the historical electricity consumption amount data, and obtain a predicted value of the carbon emission amount; the computer equipment 110 can calculate a residual error value between the carbon emission predicted value according to the actual values of the industry carbon check data and the report data, calculate a residual error floating rate according to the residual error value and the carbon emission predicted value, and correct the carbon emission predicted value by using a Markov chain model; the computer device 110 may sum the corrected carbon emission amount correction values to obtain the total carbon emission amount of the target industry, form a corrected carbon emission calculation model, and implement real-time calculation of carbon emission according to the power consumption variation data.
The computer device 110 may be, but is not limited to, various personal computers, notebook computers, smart phones, robots, unmanned aerial vehicles, tablet computers, and the like.
In one embodiment, as shown in fig. 2, there is provided a carbon emission calculation method including the steps of:
step 202, obtaining carbon emission sources corresponding to each industry, and obtaining historical electricity consumption data of each industry.
Different industries may correspond to different carbon emission sources, and in this embodiment, the computer device may obtain the carbon emission sources corresponding to the industries, respectively. In another embodiment, the carbon emissions sources may include carbon emissions generated by the combustion of fossil fuels, carbon emissions generated in industrial processes, outsourced electricity/thermal carbon emissions, and the like.
The computer device may obtain detailed historical statistics of various industries as well as historical electricity usage data for establishing a connection between the electricity usage data and the carbon emissions.
And 204, determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the carbon emission factor method and the material balance method.
Wherein, the carbon emission factor can be determined according to actual measurement and experience value, and different carbon emission sources can be corresponding to different carbon emission factors. The computer device may determine the corresponding carbon emission factor based on the carbon emission source, thereby calculating an actual value of the carbon emission corresponding to each carbon emission source for each industry.
And 206, determining the association relation between the actual carbon emission value and the historical electricity consumption data, and acquiring a predicted carbon emission value.
There is a correspondence between the actual value of the carbon emission and the historical electricity consumption data, and different carbon emission can correspond to different electricity consumption data, wherein the more the carbon emission is, the more the electricity consumption data is. In this embodiment, the computer device may predict the carbon emission amount based on the historical electricity consumption data, and determine the carbon emission amount predicted value.
And step 208, calculating residual values between the residual values and the predicted carbon emission values according to actual values of the industrial carbon check data and the report data, calculating residual floating rate according to the residual values and the predicted carbon emission values, and correcting the predicted carbon emission values by using a Markov chain model.
And 210, adding all carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of the carbon emission according to the power consumption change data.
The computer device may construct a summation model to calculate carbon electronically, i.e., a carbon emission calculation model. And taking enterprise power consumption variable data as input, and realizing carbon emission real-time calculation through a carbon emission calculation model.
And calculating the carbon emission amount of each carbon emission source according to the electric quantity, and then summing according to the result of the Markov correction to finally obtain the total carbon emission amount of the industry.
In the embodiment, the computer equipment acquires the carbon emission sources corresponding to each industry and acquires the historical electricity consumption data of each industry; determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method; determining an association relation between an actual value of the carbon emission and historical electricity consumption data, and acquiring a predicted value of the carbon emission; calculating residual error values between the industrial carbon check data and the carbon emission predicted values according to actual values of the industrial carbon check data and the report data, calculating residual error floating rate according to the residual error values and the carbon emission predicted values, and correcting the carbon emission predicted values by using a Markov chain model; and adding all carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of carbon emission according to the power consumption change data. By collecting historical electricity consumption data and actual values of carbon emission, establishing an association relation between the historical electricity consumption data and the actual values of the carbon emission, associating the electric power data with the carbon emission, deeply excavating the value application of the electric power big data in the carbon emission accounting, realizing panoramic and dynamic display of the carbon emission in the industry from the electricity, and improving the accuracy of the carbon emission calculation by relying on the advantages of strong instantaneity, high accuracy, high resolution, wide acquisition range and the like of the electric power big data, constructing an electric calculation method, realizing the dynamic analysis and real-time calculation of the carbon emission based on the electric power big data; the current carbon emission condition can be accurately mastered, so that a carbon reduction schedule and a route map are supported and formulated, and accurate navigation is provided for optimizing the regional industrial structure and adjusting the energy structure; the method can provide accurate images for important industries, enterprise carbon emission levels and energy conservation and synergy, and can formulate a next carbon reduction path, optimize an industrial structure and accelerate low-carbon transformation of enterprises and industries.
In one embodiment, the provided carbon emission calculation method may further include a process of determining an association relationship between an actual value of the carbon emission amount and the historical electricity consumption amount data, and the specific process includes: based on a gray prediction method, performing data fitting on actual values of carbon emission and historical power consumption data; and according to the data fitting result, determining the association relation between the actual value of the carbon emission and the historical electricity consumption data.
The computer device may perform data fitting on each carbon emission data and the power consumption based on the GM (0, 2) prediction method, and determine the energy active carbon emission and the association relationship between the process carbon emission and the power consumption.
In one embodiment, the provided carbon emission calculation method may further include a process of performing data fitting, and the specific process includes: taking the actual value of the carbon emission as a system research sequence and taking the historical electricity consumption data as a related factor sequence; and fitting a relation between the actual value of the carbon emission and the historical electricity consumption data by using a gray prediction method according to the system research sequence and the related factor sequence.
Specifically, the computer device may fit the relationship between fossil fuel combustion carbon emissions H, industrial process emissions G, and outsourcing electricity emissions W and electricity usage data E using a gray GM (0, 2) model. Set H (0) ＝{h (0) (1)，h (0) (2)，...，h (0) (n)}、G (0) ＝{g (0) (1)，g (0) (2)，...，g (0) (n) } and W (0) ＝{w (0) (1)，w (0) (2)，...，w (0) (n) } is the data sequence to be studied by the system, set E (0) ＝{e (0) (1)，e (0) (2)，...，e (0) (n) is a sequence of correlation factors; based on a gray GM (0, 2) model, the sequences are accumulated once to obtain a new sequence, namely: h (1) ＝{h (1) (1)，h (1) (2)，...，h (1) (n)}、G (1) ＝{g (1) (1)，g (1) (2)，...，g (1) (n)}、W (1) ＝{w (1) (1)，w (1) (2)，...，w (1) (n) } setting a related factor sequence E (1) ＝{e (1) (1)，e (1) (2)，...，e (1) (n) }; wherein, the liquid crystal display device comprises a liquid crystal display device,L (1) is H (1) 、G (1) 、W (1) 。
In one embodiment, the provided carbon emission calculation method may further include a process of performing numerical correction, and the specific process includes: calculating a carbon emission residual value between the industrial carbon check data and the report data according to the actual values of the industrial carbon check data and the report data; calculating residual floating rate according to the carbon emission residual value and the carbon emission predicted value; and correcting the carbon emission predicted value by using a Markov chain model according to the residual floating rate to obtain the corrected carbon emission predicted value.
The computer device can utilize Markov chain to correct data, calculate residual error by actual value and predicted value, and calculate residual error epsilon (k) and predicted valueAnd calculating the residual floating rate. The correction state interval is divided by the range of the residual floating rate to calculate a Markov state transition probability matrix P, and the state i, the Markov state transition probability matrix P and the correction state interval S where the system prediction data are located are used for performing prediction verification and correction on the system prediction data.
Specifically, the computer device may set L according to the new sequence obtained (1) (k)＝a+bE (1) (k) Constructing an input matrix B and an output vector Y of the model; let A= [ a, b ]] T ＝(B T B) -1 B T Y adopts least square method to calculate estimated valueAnd->Obtaining a prediction of carbon emissionsValues. Then, the computer equipment can calculate the residual error of the predicted value by using the actual values of the industrial carbon check data and the report data, and calculate the residual error floating rate according to the residual error and the predicted value. Let the residual be ε (k), the predicted value be +.>The residual floating rate is denoted by ε (k), the residual floating rate is: />
In one embodiment, the provided carbon emission calculation method may further include a data correction process, and the specific process includes: acquiring the range of the residual floating rate, and dividing a correction state interval according to the range of the residual floating rate; acquiring a state interval in which a carbon emission predicted value is located, and calculating a Markov state transition probability matrix according to the corrected state interval; and carrying out prediction verification and correction on the carbon emission predicted value according to the state interval in which the carbon emission predicted value is located, the Markov state transition probability matrix and the corrected state interval.
The computer device may divide the corrected state interval based on the residual floating rate, and divide the three state intervals S based on the predicted value of each emission source 1 、S 2 、S 3 The method comprises the steps of carrying out a first treatment on the surface of the The computer device can calculate a state transition probability matrix P, wherein The computer device can calculate a state transition probability p ij Let M i =1, 2,3,..n is the amount of system data in state i, M ij For the number of i states transferred to j states in one step +.> In this embodiment, the computer device may set the row directionQuantity R 0 An initial vector representing system data, and a setting row vector R i I=1, 2, …, n, representing the system data vector, R i ＝R 0 ×P k K=0, 1, …, n; set state interval S 1 Is in overestimated state S 2 Is in a normal state S 3 When the estimated value is under estimated state and the estimated value is corrected based on the Markov state interval, if the result is S 1 State, correction value=predicted value-predicted value×s 1 The average value of the sum of the upper absolute value and the lower absolute value of the state interval; if the result is at S 2 The state may not be modified; if the result is at S 3 State, i.e. predictive underestimated state, then markov correction = predictive + predictive x S 3 The mean value of the sum of the upper and lower absolute values of the state interval.
In one embodiment, the provided carbon emission calculating method may further include a process of calculating an actual value of the carbon emission amount, the specific process including: determining a carbon emission factor and a carbon emission activity level corresponding to each carbon emission source; and calculating the product between the carbon emission factor and the carbon emission activity level, and determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the product.
Wherein the carbon emission factor may be determined by actual measurement and empirical values, and the carbon emission of each emission source is determined by the product of its corresponding carbon emission factor and activity level.
It should be understood that, although the steps in the above-described flowcharts are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with at least a part of the sub-steps or stages of other steps or other steps.
In one embodiment, as shown in fig. 3, there is provided a carbon emission calculation apparatus including: a data acquisition module 310, a historical carbon emission determination module 320, an association determination module 330, a correction module 340, and a real-time carbon emission calculation module 350, wherein:
the data acquisition module 310 is configured to acquire carbon emission sources corresponding to each industry, and acquire historical electricity consumption data of each industry;
a historical carbon emission determining module 320, configured to determine an actual carbon emission value corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method;
the association relation determining module 330 is configured to determine an association relation between an actual value of the carbon emission and the historical electricity consumption data, and obtain a predicted value of the carbon emission;
the correction module 340 is configured to calculate a residual value between the industrial carbon verification data and the report data and the predicted carbon emission value, calculate a residual floating rate according to the residual value and the predicted carbon emission value, and correct the predicted carbon emission value by using a markov chain model;
the real-time carbon emission calculation module 350 is configured to sum the corrected carbon emission values to obtain a total carbon emission of the target industry, form a corrected carbon emission calculation model, and implement real-time calculation of carbon emission according to the power consumption variation data.
In one embodiment, the linkage determination module 330 is further configured to perform data fitting on the actual carbon emission value and the historical electricity consumption data based on a gray prediction method; and according to the data fitting result, determining the association relation between the actual value of the carbon emission and the historical electricity consumption data.
In one embodiment, the linkage determination module 330 is further configured to use the actual value of the carbon emission as a system research sequence and the historical power consumption data as a related factor sequence; and fitting a relation between the actual value of the carbon emission and the historical electricity consumption data by using a gray prediction method according to the system research sequence and the related factor sequence.
In one embodiment, the correction module 340 is further configured to obtain a range of the residual floating rate, and divide the correction status interval according to the range of the residual floating rate; acquiring a state interval in which a carbon emission predicted value is located, and calculating a Markov state transition probability matrix according to the corrected state interval; and carrying out prediction verification and correction on the carbon emission predicted value according to the state interval in which the carbon emission predicted value is located, the Markov state transition probability matrix and the corrected state interval.
In one embodiment, the historical carbon emissions determination module 320 is further configured to determine a carbon emission factor, a carbon emission activity level, corresponding to each of the carbon emission sources; and calculating the product between the carbon emission factor and the carbon emission activity level, and determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the product.
In one embodiment, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a carbon emission calculation method. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the structures shown in FIG. 4 are block diagrams only and do not constitute a limitation of the computer device on which the present aspects apply, and that a particular computer device may include more or less components than those shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring carbon emission sources corresponding to each industry, and acquiring historical electricity consumption data of each industry;
determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method;
determining an association relation between an actual value of the carbon emission and historical electricity consumption data, and acquiring a predicted value of the carbon emission;
calculating residual error values between the industrial carbon check data and the carbon emission predicted values according to actual values of the industrial carbon check data and the report data, calculating residual error floating rate according to the residual error values and the carbon emission predicted values, and correcting the carbon emission predicted values by using a Markov chain model;
and adding all carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of carbon emission according to the power consumption change data.
In one embodiment, the processor when executing the computer program further performs the steps of: based on a gray prediction method, performing data fitting on actual values of carbon emission and historical power consumption data; and according to the data fitting result, determining the association relation between the actual value of the carbon emission and the historical electricity consumption data.
In one embodiment, the processor when executing the computer program further performs the steps of: taking the actual value of the carbon emission as a system research sequence and taking the historical electricity consumption data as a related factor sequence; and fitting a relation between the actual value of the carbon emission and the historical electricity consumption data by using a gray prediction method according to the system research sequence and the related factor sequence.
In one embodiment, the processor when executing the computer program further performs the steps of: acquiring the range of the residual floating rate, and dividing a correction state interval according to the range of the residual floating rate; acquiring a state interval in which a carbon emission predicted value is located, and calculating a Markov state transition probability matrix according to the corrected state interval; and carrying out prediction verification and correction on the carbon emission predicted value according to the state interval in which the carbon emission predicted value is located, the Markov state transition probability matrix and the corrected state interval.
In one embodiment, the processor when executing the computer program further performs the steps of: determining a carbon emission factor and a carbon emission activity level corresponding to each carbon emission source; and calculating the product between the carbon emission factor and the carbon emission activity level, and determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the product.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring carbon emission sources corresponding to each industry, and acquiring historical electricity consumption data of each industry;
determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method;
determining an association relation between an actual value of the carbon emission and historical electricity consumption data, and acquiring a predicted value of the carbon emission;
calculating residual error values between the industrial carbon check data and the carbon emission predicted values according to actual values of the industrial carbon check data and the report data, calculating residual error floating rate according to the residual error values and the carbon emission predicted values, and correcting the carbon emission predicted values by using a Markov chain model;
and adding all carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of carbon emission according to the power consumption change data.
In one embodiment, the computer program when executed by the processor further performs the steps of: based on a gray prediction method, performing data fitting on actual values of carbon emission and historical power consumption data; and according to the data fitting result, determining the association relation between the actual value of the carbon emission and the historical electricity consumption data.
In one embodiment, the computer program when executed by the processor further performs the steps of: taking the actual value of the carbon emission as a system research sequence and taking the historical electricity consumption data as a related factor sequence; and fitting a relation between the actual value of the carbon emission and the historical electricity consumption data by using a gray prediction method according to the system research sequence and the related factor sequence.
In one embodiment, the computer program when executed by the processor further performs the steps of: acquiring the range of the residual floating rate, and dividing a correction state interval according to the range of the residual floating rate; acquiring a state interval in which a carbon emission predicted value is located, and calculating a Markov state transition probability matrix according to the corrected state interval; and carrying out prediction verification and correction on the carbon emission predicted value according to the state interval in which the carbon emission predicted value is located, the Markov state transition probability matrix and the corrected state interval.
In one embodiment, the computer program when executed by the processor further performs the steps of: determining a carbon emission factor and a carbon emission activity level corresponding to each carbon emission source; and calculating the product between the carbon emission factor and the carbon emission activity level, and determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the product.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. A carbon emission calculation method, characterized in that the method comprises:
acquiring carbon emission sources corresponding to each industry, and acquiring historical electricity consumption data of each industry;
determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to a carbon emission factor method and a material balance method;
determining an association relation between the actual value of the carbon emission and the historical electricity consumption data, and acquiring a predicted value of the carbon emission;
calculating a residual error value between the carbon emission predicted value and the industrial carbon check data and the actual value of report data, calculating a residual error floating rate according to the residual error value and the carbon emission predicted value, and correcting the carbon emission predicted value by using a Markov chain model;
and adding all the carbon emission correction values through the corrected carbon emission predicted value to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model, and realizing the real-time calculation of the carbon emission according to the power consumption change data.
2. The carbon emission calculation method according to claim 1, characterized in that the determining of the association relationship between the actual value of the carbon emission amount and the historical electricity consumption amount data includes:
based on a gray prediction method, performing data fitting on the actual value of the carbon emission and the historical electricity consumption data;
and according to a data fitting result, determining the association relation between the actual value of the carbon emission and the historical electricity consumption data.
3. The carbon emission calculation method according to claim 2, wherein the data fitting of the actual carbon emission value and the historical electricity consumption data based on the gray prediction method includes:
taking the actual value of the carbon emission as a system research sequence and the historical electricity consumption data as a related factor sequence;
and fitting a relation between the actual value of the carbon emission and the historical electricity consumption data by using the gray prediction method according to the system research sequence and the related factor sequence.
4. The carbon emission calculation method according to claim 3, wherein the correcting the predicted value of the carbon emission amount using a markov chain model includes:
acquiring the range of the residual floating rate, and dividing a correction state interval according to the range of the residual floating rate;
acquiring a state interval in which the carbon emission predicted value is located, and calculating a Markov state transition probability matrix according to the corrected state interval;
and carrying out prediction verification and correction on the carbon emission predicted value according to the state interval in which the carbon emission predicted value is located, the Markov state transition probability matrix and the correction state interval.
5. The carbon emission calculation method according to claim 1, wherein determining the actual value of the carbon emission amount corresponding to each of the carbon emission sources in each industry according to a carbon emission factor method and a material balance method comprises:
determining a carbon emission factor and a carbon emission activity level corresponding to each carbon emission source;
and calculating the product between the carbon emission factor and the carbon emission activity level, and determining the actual value of the carbon emission corresponding to each carbon emission source in each industry according to the product.
6. The carbon emission calculation method of claim 1, wherein the carbon emission source comprises carbon emissions generated by combustion of fossil fuel, carbon emissions generated in an industrial process, outsourced electricity/thermal carbon emissions.
7. A carbon emission calculation device, the device comprising:
the data acquisition module is used for acquiring carbon emission sources corresponding to each industry and acquiring historical electricity consumption data of each industry;
the historical carbon emission determining module is used for determining actual carbon emission values corresponding to the carbon emission sources in each industry according to a carbon emission factor method and a material balance method;
the incidence relation determining module is used for determining the incidence relation between the actual value of the carbon emission and the historical electricity consumption data and obtaining a predicted value of the carbon emission;
the correction module is used for calculating a residual error value between the carbon emission predicted value and the carbon emission predicted value according to actual values of industry carbon check data and report data, calculating residual error floating rate according to the residual error value and the carbon emission predicted value, and correcting the carbon emission predicted value by using a Markov chain model;
and the real-time carbon emission calculation module is used for summing the corrected carbon emission values through the corrected carbon emission predicted values to obtain the total carbon emission of the target industry, forming a corrected carbon emission calculation model and realizing the real-time calculation of the carbon emission according to the change data of the power consumption.
8. The carbon emission computing device of claim 7, wherein the association determination module is further configured to: based on a gray prediction method, performing data fitting on the actual value of the carbon emission and the historical electricity consumption data; and according to a data fitting result, determining the association relation between the actual value of the carbon emission and the historical electricity consumption data.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 6 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 6.
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| CN117273276A (en) * | 2023-10-10 | 2023-12-22 | 南方电网能源发展研究院有限责任公司 | Carbon emission monitoring method and device based on electric power data |
| CN117313997A (en) * | 2023-09-21 | 2023-12-29 | 国网河北省电力有限公司物资分公司 | Accounting method and device for life cycle carbon footprint of lead wire |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117313997A (en) * | 2023-09-21 | 2023-12-29 | 国网河北省电力有限公司物资分公司 | Accounting method and device for life cycle carbon footprint of lead wire |
| CN117273276A (en) * | 2023-10-10 | 2023-12-22 | 南方电网能源发展研究院有限责任公司 | Carbon emission monitoring method and device based on electric power data |
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