CN115907222A - Carbon emission prediction method and system - Google Patents
Carbon emission prediction method and system Download PDFInfo
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- CN115907222A CN115907222A CN202211679786.5A CN202211679786A CN115907222A CN 115907222 A CN115907222 A CN 115907222A CN 202211679786 A CN202211679786 A CN 202211679786A CN 115907222 A CN115907222 A CN 115907222A
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Abstract
The invention provides a carbon emission prediction method and a carbon emission prediction system. The method comprises the following steps: acquiring power consumption data of a target area; obtaining non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model; and calculating the carbon emission prediction amount of the target area according to the power consumption data and the non-power energy consumption prediction data of the target area. The regional energy carbon emission prediction method can be used for predicting regional energy carbon emission through power consumption data from the trend of energy structure and energy electrification.
Description
Technical Field
The invention belongs to the technical field of carbon emission, and particularly relates to a carbon emission prediction method and a carbon emission prediction system.
Background
With the increasing importance of coping with climate change in global management systems, various tasks such as carbon emission, carbon asset management, carbon trading and the like are also paid more attention by each party, and carbon emission systems at all levels are gradually established.
Under the double-carbon target, the electric power and the fossil energy can be mutually replaced, and the increase of the electrification level is a deterministic trend in the middle and long term. Due to the development inertia of an economic system and an energy system, a regional energy consumption structure has better short-term stability and long-term trend continuity.
The existing carbon emission measuring and calculating method measures and calculates the carbon emission in the production process of a product according to a target product carbon emission factor; calculating the carbon emission amount generated by fossil energy consumption according to the carbon emission factor of the fossil energy; and obtaining the total carbon emission amount based on the power consumption data according to the calculated carbon emission amount in the production process of the indicated product and the calculated carbon emission amount generated by the fossil energy consumption. However, this method for measuring and calculating carbon emission is a single-dimensional method for measuring and calculating carbon emission from a narrow range of subdivided products, considering energy and power consumption, and cannot support the carbon emission of energy activities in a wide range of areas.
Disclosure of Invention
In order to solve the technical problems, the invention provides a technical scheme of a carbon emission prediction method, which can realize regional energy carbon emission prediction through electric power consumption data from the trend of energy structure and energy electrification.
The invention discloses a carbon emission prediction method in a first aspect; the method comprises the following steps:
s1, acquiring power consumption data of a target area;
s2, obtaining non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model;
and S3, calculating the predicted carbon emission amount of the target area according to the power consumption data of the target area and the non-power energy consumption prediction data.
According to the method of the first aspect of the present invention, the prediction model is trained by:
acquiring historical power consumption data of a target area and historical consumption data of non-power energy;
and aiming at each non-electric energy, constructing a prediction model corresponding to the non-electric energy according to the historical consumption data of the electric energy and the historical consumption data of the non-electric energy.
According to the method of the first aspect of the invention, the non-electrical energy source comprises: coal, oil, natural gas and new energy;
accordingly, the non-electric energy source corresponding prediction model comprises: the system comprises a coal prediction model, an oil product prediction model, a natural gas prediction model and a new energy prediction model.
According to the method of the first aspect of the present invention, the building a prediction model corresponding to the non-electric energy source according to the historical consumption data of the electric power and the historical consumption data of the non-electric energy source includes:
calculating a correlation coefficient between the electric power and the non-electric power energy according to the historical consumption data of the electric power and the historical consumption data of the non-electric power energy;
judging the linear relation between the electric power and the non-electric power energy according to the correlation coefficient;
and constructing a prediction model corresponding to the non-electric energy according to the linear relation judgment result.
According to the method of the first aspect of the present invention, the building of the prediction model corresponding to the non-electric energy according to the linear relationship determination result includes:
if the electric power is linearly related to the non-electric power energy, a unitary linear regression method is adopted to construct a prediction model corresponding to the non-electric power energy;
and if the electric power is not linearly related to the non-electric power energy, constructing a prediction model corresponding to the non-electric power energy by adopting a unary exponential regression method or a one-time moving average method.
According to the method of the first aspect of the present invention, the calculating a predicted amount of carbon emission for a target area based on the power consumption data and the predicted non-power energy consumption data for the target area includes:
acquiring carbon emission coefficients of electric power and non-electric power energy;
and calculating the predicted carbon emission amount of the target area according to the carbon emission coefficient, the power consumption data of the target area and the non-power energy consumption prediction data.
The method according to the first aspect of the invention, the method further comprising:
and training and updating the prediction model according to the actual power consumption data and the actual non-power energy consumption data of the target area.
The second aspect of the invention discloses a carbon emission prediction system; the system comprises:
a first processing module configured to acquire power consumption data of a target area;
a second processing module configured to obtain non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model;
a third processing module configured to calculate a predicted amount of carbon emissions for the target area based on the predicted data of power consumption and the predicted data of non-power energy consumption for the target area.
According to the system of the second aspect of the invention, the second processing module is specifically configured to be trained by: acquiring historical power consumption data of a target area and historical consumption data of non-power energy; and aiming at each non-electric energy, constructing a prediction model corresponding to the non-electric energy according to the historical consumption data of the electric power and the historical consumption data of the non-electric energy.
According to the system of the second aspect of the invention, the non-electrical energy source comprises: coal, oil, natural gas and new energy;
accordingly, the non-electric energy source corresponding prediction model comprises: the system comprises a coal prediction model, an oil product prediction model, a natural gas prediction model and a new energy prediction model.
According to the system of the second aspect of the present invention, the second processing module is specifically configured to calculate a correlation coefficient between electric power and non-electric power source based on the historical consumption data of electric power and the historical consumption data of non-electric power source; judging the linear relation between the electric power and the non-electric power energy according to the correlation coefficient; and constructing a prediction model corresponding to the non-electric energy according to the linear relation judgment result.
According to the system of the second aspect of the present invention, the second processing module is specifically configured to construct a prediction model corresponding to the non-electric energy source by using a unary linear regression method if the electric power is linearly related to the non-electric energy source; and if the electric power is not linearly related to the non-electric power energy, constructing a prediction model corresponding to the non-electric power energy by adopting a unary exponential regression method or a one-time moving average method.
According to the system of the second aspect of the invention, the third processing module is specifically configured to obtain carbon emission coefficients of the electrical and non-electrical energy sources;
and calculating the carbon emission prediction amount of the target area according to the carbon emission coefficient, the power consumption data of the target area and the non-power energy consumption prediction data.
According to the system of the second aspect of the present invention, the second processing module is further configured to train and update the prediction model according to actual power consumption data and actual non-power energy consumption data of the target area.
A third aspect of the invention discloses an electronic device. The electronic device comprises a memory storing a computer program and a processor implementing the steps of a method of carbon emission prediction according to any one of the first aspect of the present disclosure when the processor executes the computer program.
A fourth aspect of the invention discloses a computer-readable storage medium. The computer readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps in a method of carbon emission prediction according to any one of the first aspect of the present disclosure.
According to the scheme provided by the invention, the non-electric energy consumption prediction data is obtained based on the electric power consumption data through a pre-trained prediction model, and then the carbon emission prediction amount of the target area is calculated according to the electric power consumption data and the non-electric energy consumption prediction data, so that the regional energy carbon emission prediction is realized through the electric power consumption data from the energy structure and the energy electrification trend under the forward trend of energy consumption and electric power consumption under the double-carbon background.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method for carbon emissions prediction according to an embodiment of the present invention;
FIG. 2 is a block diagram of a carbon emissions prediction system according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this disclosure and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present invention. The word "if," as used herein, may be interpreted as "at … …" or "at … …" or "in response to a determination," depending on the context.
Example 1:
the invention discloses a carbon emission prediction method. Fig. 1 is a flowchart of a carbon emission prediction method according to an embodiment of the present invention, as shown in fig. 1, the method including:
s1, acquiring power consumption data of a target area;
s2, obtaining non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model;
and S3, calculating the predicted carbon emission amount of the target area according to the power consumption data of the target area and the non-power energy consumption prediction data.
In step S1, power consumption data of a target area is acquired.
In some embodiments, in step S1, the power consumption data of the target area may be predicted power consumption data of the target area at the target time based on the power consumption data of the historical time of the target area, or may be actual power consumption data of the target area at the target time. The historical time can be historical months or historical years; accordingly, the target time can be a target month or a target year, and can be selected according to actual requirements.
In some embodiments, the acquired power consumption data of the target area is stored according to a preset data structure. The data structure is: area Id (Identity), area name, energy variety attribute Id, energy variety name, data time, data value, unit.
Specifically, region Id: the method comprises the steps that a unique regional attribute Id of a city or a garden is referred, and the global uniqueness of the Id is marked by adopting a 32-bit UUID; area name: refers to a city or campus name; energy variety attribute Id: the energy variety attribute unique code is defined and can be set by self-definition based on national and industrial standards; energy variety name: refers to the name of the variety of the electric power energy; data time: time sequence data time of electric power energy varieties is indicated; data value: the data value refers to the variety data value of the electric energy; unit: refers to the unit of electric energy variety.
And S2, obtaining non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model.
In some embodiments, in step S2, the prediction model is trained by: acquiring historical power consumption data of a target area and historical consumption data of non-power energy; and aiming at each non-electric energy, constructing a prediction model corresponding to the non-electric energy according to the historical consumption data of the electric energy and the historical consumption data of the non-electric energy.
In some embodiments, the non-electrical energy source comprises: coal, oil, natural gas and new energy. Accordingly, the non-electric energy source corresponding prediction model comprises: the system comprises a coal prediction model, an oil product prediction model, a natural gas prediction model and a new energy prediction model.
Specifically, historical power consumption data of a target area and historical consumption data of non-power energy are obtained, and a historical consumption time sequence data structure of coal, oil products, natural gas, new energy and power in the target area is constructed. The data structure is: the area Id, the area name, the energy variety attribute Id, the energy variety name, the data time, the data value, the unit, and the like are shown in table 1, where table 1 shows a partial structure.
TABLE 1
Wherein, the area name: refers to a city or campus name; energy variety attribute Id: the energy variety attribute unique code is defined and can be set by self-definition based on national and industrial standards; energy variety name: refers to the names of energy varieties such as coal, oil products, natural gas, new energy, electric power and the like; data time: refers to the time sequence data time of energy varieties such as coal, oil products, natural gas, new energy, electric power and the like; data value: the data values refer to energy varieties such as coal, oil products, natural gas, new energy, electric power and the like; unit: unit: refers to units of energy varieties such as coal, oil products, natural gas, new energy, electric power and the like.
In some embodiments, for each non-electric energy source, a corresponding prediction model of the non-electric energy source is constructed according to the historical consumption data of the electric power and the historical consumption data of the non-electric energy source.
In some embodiments, for each non-electric energy source, calculating a correlation coefficient between electric power and non-electric energy sources from the historical consumption data of electric power and the historical consumption data of the non-electric energy source; judging the linear relation between the electric power and the non-electric power energy according to the correlation coefficient; and constructing a prediction model corresponding to the non-electric energy according to the linear relation judgment result.
In some embodiments, if the electric power is linearly related to the non-electric power energy, a unitary linear regression method is adopted to construct a prediction model corresponding to the non-electric power energy; and if the electric power is not linearly related to the non-electric power energy, constructing a prediction model corresponding to the non-electric power energy by adopting a unitary exponential regression method or a one-time moving average method.
In some embodiments, for each non-electric energy source, a proportional relation between the non-electric energy source and the consumption amount of the electric power in the target area may be obtained according to the historical consumption data of the electric power in the target area and the historical consumption data of the non-electric energy source; the consumption proportional relation can be used as a data judgment standard of the prediction model. In practical application, a corresponding data structure may be constructed based on the obtained consumption proportional relationship, for example, the data structure is: the electric power variety Id, the non-electric power variety Id, the data time, the monthly power consumption, the monthly non-electric power consumption, and the ratio are shown in table 2, where table 2 shows a partial structure.
TABLE 2
Wherein, electric power energy variety Id: referring to an electric power variety Id, marking the global uniqueness of the Id by adopting a 32-bit UUID; non-electric power energy variety Id: referring to a non-electric energy variety Id, marking the global uniqueness of the Id by adopting a 32-bit UUID; bottom of month power consumption: the electricity consumption of the area; consumption of non-electric energy varieties at the bottom of the moon: the consumption of coal, oil products, natural gas and new energy varieties in the area is indicated; the proportion value is as follows: the ratio of the variety of non-electric power energy to the consumption of electric power. In this way, the correlation coefficient between the electric power and the non-electric power can be calculated from the historical data of the consumption of the electric power and the consumption of the non-electric power energy variety, and the corresponding algorithm is selected based on the linear judgment result to construct the prediction model.
In some embodiments, the constructed prediction model is a non-electric energy and electric power ratio prediction model, the constructed prediction model is a non-electric energy prediction model, and subsequently through the electric power consumption data and the prediction model of the target area, the non-electric energy consumption prediction data of the target area can be output.
In some embodiments, the prediction model constructed is a non-electric power source to electric power ratio prediction model, as shown in table 3.
TABLE 3
And subsequently, through the power consumption data and the prediction model of the target area, the prediction ratio value of the non-electric energy source and the electric power of the target area can be output. Then, non-electric energy consumption prediction data of the target area can be obtained according to the electric power consumption data of the target area and the obtained prediction proportion value.
It can be understood that the algorithm for constructing the model selected based on the linear judgment result in the present invention is an algorithm commonly used in the art, and in practical application, the selection can be performed according to actual requirements.
In some embodiments, the prediction model is trained and updated according to the actual power consumption data and the actual non-power energy consumption data of the target area, so as to guarantee prediction accuracy.
In step S3, a predicted amount of carbon emission in the target area is calculated based on the power consumption data and the predicted non-power energy consumption data in the target area.
In some embodiments, in step S3, the carbon emission coefficients of the electric and non-electric energy sources may be obtained; and calculating the predicted carbon emission amount of the target area according to the carbon emission coefficient, the power consumption data of the target area and the non-power energy consumption prediction data.
Considering that the carbon emission of the energy activity comprises the sum of carbon emission generated by consumption of five kinds of energy, namely coal, oil, natural gas, new energy and electric power, the carbon emission conditions of different energy sources are different, so that the carbon emission coefficients of the coal, the oil, the natural gas, the new energy and the electric power can be obtained firstly. Then, for each energy source, the carbon emission prediction amount is calculated from the carbon emission coefficient and consumption data of the energy source, as shown in tables 4 and 5.
TABLE 4
TABLE 5
In some embodiments, a carbon emission prediction amount of the power of the target area is calculated from the power consumption data of the target area and the carbon emission coefficient of the power; obtaining coal consumption prediction data of the target area according to the power consumption data of the target area and the prediction proportion value of coal and power, and then calculating the carbon emission prediction amount of the coal of the target area according to the coal consumption prediction data of the target area and the carbon emission coefficient of the coal; obtaining oil consumption prediction data of the target area according to the power consumption data of the target area and the prediction proportion value of the oil and the power, and then calculating the carbon emission prediction amount of the oil in the target area according to the oil consumption prediction data of the target area and the carbon emission coefficient of the oil; obtaining natural gas consumption prediction data of the target area according to the power consumption data of the target area and the prediction proportion value of the natural gas and the power, and then calculating the carbon emission prediction amount of the natural gas of the target area according to the natural gas consumption prediction data of the target area and the carbon emission coefficient of the natural gas; and according to the power consumption data of the target area and the prediction ratio value of the new energy and the power, obtaining new energy consumption prediction data of the target area, and then according to the new energy consumption prediction data of the target area and the carbon emission coefficient of the new energy, calculating the carbon emission prediction amount of the new energy of the target area.
In this way, the carbon emission prediction amount of the target area can be obtained according to the carbon emission prediction amount of coal, the carbon emission prediction amount of oil, the carbon emission prediction amount of natural gas, the carbon emission prediction amount of new energy and the carbon emission prediction amount of electric power in a data set mode.
According to the scheme provided by the invention, the non-electric energy consumption prediction data is obtained based on the electric power consumption data through a pre-trained prediction model, and then the carbon emission prediction quantity of the target area is calculated according to the electric power consumption data and the non-electric energy consumption prediction data, so that the regional energy carbon emission prediction is realized through the electric power consumption data from the energy structure and the energy electrification trend under the forward trend of energy consumption and electric power consumption under the double-carbon background.
Example 2:
the invention discloses a carbon emission prediction system. FIG. 2 is a block diagram of a carbon emissions prediction system according to an embodiment of the present invention; as shown in fig. 2, the system 100 includes:
a first processing module 101 configured to acquire power consumption data of a target area;
a second processing module 102, configured to obtain non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model;
a third processing module 103 configured to calculate a predicted amount of carbon emission for the target area based on the predicted data of power consumption and the predicted data of non-power energy consumption for the target area.
According to the system of the second aspect of the present invention, the first processing module 101 is specifically configured to obtain power consumption data of a target area.
In some embodiments, the power consumption data of the target area may be predicted consumption data of the target area at the target time based on the power consumption data of the target area at the historical time, or may be actual consumption data of the target area at the target time. The historical time can be historical months or historical years; accordingly, the target time can be a target month or a target year, and can be selected according to actual requirements.
According to the system of the second aspect of the present invention, the second processing module 102 is specifically configured to be trained by: acquiring historical power consumption data of a target area and historical consumption data of non-power energy; and aiming at each non-electric energy, constructing a prediction model corresponding to the non-electric energy according to the historical consumption data of the electric power and the historical consumption data of the non-electric energy.
According to the system of the second aspect of the invention, the non-electrical energy source comprises: coal, oil, natural gas and new energy;
accordingly, the non-electric energy source corresponding prediction model comprises: the system comprises a coal prediction model, an oil product prediction model, a natural gas prediction model and a new energy prediction model.
According to the system of the second aspect of the invention, the second processing module is specifically configured to calculate a correlation coefficient between the electric power and the non-electric energy source based on the historical consumption data of the electric power and the historical consumption data of the non-electric energy source; judging the linear relation between the electric power and the non-electric power energy according to the correlation coefficient; and constructing a prediction model corresponding to the non-electric energy according to the linear relation judgment result.
According to the system of the second aspect of the invention, the second processing module is specifically configured to construct a prediction model corresponding to the non-electric energy source by using a unary linear regression method if the electric power is linearly related to the non-electric energy source; and if the electric power is not linearly related to the non-electric power energy, constructing a prediction model corresponding to the non-electric power energy by adopting a unary exponential regression method or a one-time moving average method.
According to the system of the second aspect of the invention, the third processing module is specifically configured to obtain carbon emission coefficients of the electrical and non-electrical energy sources;
and calculating the predicted carbon emission amount of the target area according to the carbon emission coefficient, the power consumption data of the target area and the non-power energy consumption prediction data.
According to the system of the second aspect of the present invention, the second processing module is further configured to train and update the prediction model according to actual power consumption data and actual non-power energy consumption data of the target area.
It is to be understood that, for the method for implementing the specific functions of the modules in the system according to the second aspect of the present invention, reference may be made to the steps in the method according to the first aspect of the present invention, and details are not described here.
According to the scheme provided by the invention, the non-electric energy consumption prediction data is obtained based on the electric power consumption data through a pre-trained prediction model, and then the carbon emission prediction amount of the target area is calculated according to the electric power consumption data and the non-electric energy consumption prediction data, so that the regional energy carbon emission prediction is realized through the electric power consumption data from the energy structure and the energy electrification trend under the forward trend of energy consumption and electric power consumption under the double-carbon background.
Example 3:
the invention discloses an electronic device. The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the carbon emission prediction method according to any one of the embodiments 1 of the present disclosure when executing the computer program.
Fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 3, the electronic device includes a processor, a memory, a communication interface, a display screen, and an input device, which are connected by a system bus. Wherein the processor of the electronic device is configured to provide computing and control capabilities. The memory of the electronic equipment comprises a nonvolatile 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 an operating system and computer programs in the non-volatile storage medium. The communication interface of the electronic device is used for communicating with an external terminal in a wired or wireless mode, and the wireless mode can be realized through WIFI, an operator network, near Field Communication (NFC) or other technologies. The display screen of the electronic equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the electronic equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the electronic equipment, an external keyboard, a touch pad or a mouse and the like.
It will be understood by those skilled in the art that the structure shown in fig. 3 is only a partial block diagram related to the technical solution of the present disclosure, and does not constitute a limitation of the electronic device to which the solution of the present application is applied, and a specific electronic device may include more or less components than those shown in the drawings, or combine some components, or have a different arrangement of components.
Example 4:
the invention discloses a computer readable storage medium. The computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps in a carbon emission prediction method according to any one of embodiment 1 of the present invention.
It should be noted that the technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, however, as long as there is no contradiction between the combinations of the technical features, the scope of the present description should be considered. The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, and these are all within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Embodiments of the subject matter and the functional operations described in this specification can be implemented in: digital electronic circuitry, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on a tangible, non-transitory program carrier for execution by, or to control the operation of, data processing apparatus. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, that is generated to encode and transmit information to suitable receiver apparatus for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.
The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).
Computers suitable for the execution of a computer program include, for example, general and/or special purpose microprocessors, or any other type of central processing unit. Generally, a central processing unit will receive instructions and data from a read-only memory and/or a random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto-optical disks, or optical disks. However, a computer does not necessarily have such a device. Moreover, a computer may be embedded in another device, e.g., a mobile telephone, a Personal Digital Assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device such as a Universal Serial Bus (USB) flash drive, to name a few.
Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., an internal hard disk or a removable disk), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.
While this specification contains many specific implementation details, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features specific to particular embodiments of particular inventions. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. In other instances, features described in connection with one embodiment may be implemented as discrete components or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.
Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In some cases, multitasking and parallel processing may be advantageous. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.
Thus, particular embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. Further, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some implementations, multitasking and parallel processing may be advantageous.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A method of predicting carbon emissions, the method comprising:
s1, acquiring power consumption data of a target area;
s2, obtaining non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model;
and S3, calculating the predicted carbon emission amount of the target area according to the power consumption data of the target area and the non-power energy consumption prediction data.
2. The method of claim 1, wherein the prediction model is trained by:
acquiring historical power consumption data of a target area and historical consumption data of non-power energy;
and aiming at each non-electric energy, constructing a prediction model corresponding to the non-electric energy according to the historical consumption data of the electric power and the historical consumption data of the non-electric energy.
3. A method of predicting carbon emissions according to claim 2, wherein said non-electrical energy source comprises: coal, oil, natural gas, and new energy;
accordingly, the non-electric energy source corresponding prediction model comprises: the system comprises a coal prediction model, an oil product prediction model, a natural gas prediction model and a new energy prediction model.
4. A method as claimed in claim 3, wherein the step of constructing a prediction model corresponding to the non-electric energy source according to the historical consumption data of the electric power and the historical consumption data of the non-electric energy source comprises:
calculating a correlation coefficient between the electric power and the non-electric power energy according to the historical consumption data of the electric power and the historical consumption data of the non-electric power energy;
judging the linear relation between the electric power and the non-electric power energy according to the correlation coefficient;
and constructing a prediction model corresponding to the non-electric energy according to the linear relation judgment result.
5. The method for predicting carbon emission according to claim 4, wherein the building of the prediction model corresponding to the non-electric energy according to the linear relation judgment result comprises:
if the electric power is linearly related to the non-electric power energy, constructing a prediction model corresponding to the non-electric power energy by adopting a unitary linear regression method;
and if the electric power is not linearly related to the non-electric power energy, constructing a prediction model corresponding to the non-electric power energy by adopting a unary exponential regression method or a one-time moving average method.
6. The method according to claim 5, wherein the step of calculating the predicted amount of carbon emission in the target area based on the predicted data on power consumption in the target area and the predicted data on non-power consumption comprises:
acquiring carbon emission coefficients of electric power and non-electric power energy;
and calculating the carbon emission prediction amount of the target area according to the carbon emission coefficient, the power consumption data of the target area and the non-power energy consumption prediction data.
7. A method of predicting carbon emissions according to any of claims 1-6, further comprising:
and training and updating the prediction model according to the actual power consumption data and the actual non-power energy consumption data of the target area.
8. A system for carbon emissions prediction, the system comprising:
a first processing module configured to acquire power consumption data of a target area;
a second processing module configured to obtain non-electric energy consumption prediction data of the target area according to the electric power consumption data and a pre-trained prediction model;
a third processing module configured to calculate a predicted amount of carbon emissions for the target area based on the predicted data of power consumption and the predicted data of non-power energy consumption for the target area.
9. An electronic device, comprising a memory storing a computer program and a processor that, when executed, performs the steps of a method of carbon emission prediction as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, carries out the steps of a method of carbon emission prediction as claimed in any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116167669A (en) * | 2023-04-26 | 2023-05-26 | 国网浙江省电力有限公司金华供电公司 | Carbon emission assessment method based on power consumption regression |
| CN117634933A (en) * | 2024-01-26 | 2024-03-01 | 中国电力科学研究院有限公司 | Carbon emission data prediction method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116167669A (en) * | 2023-04-26 | 2023-05-26 | 国网浙江省电力有限公司金华供电公司 | Carbon emission assessment method based on power consumption regression |
| CN116167669B (en) * | 2023-04-26 | 2023-07-21 | 国网浙江省电力有限公司金华供电公司 | Carbon emission assessment method based on power consumption regression |
| CN117634933A (en) * | 2024-01-26 | 2024-03-01 | 中国电力科学研究院有限公司 | Carbon emission data prediction method and device |
| CN117634933B (en) * | 2024-01-26 | 2024-05-07 | 中国电力科学研究院有限公司 | Carbon emission data prediction method and device |
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