Regional carbon emission prediction method, device and equipment based on energy consumption structure

Abstract

The invention relates to the technical field of energy, in particular to a regional carbon emission prediction method, device and equipment based on an energy consumption structure, and the method specifically comprises the steps: predicting the total energy consumption of a target region, and obtaining the total energy consumption of the target region at a target time node; predicting the energy consumption structure of the whole country at the key time node to obtain the energy consumption structure of the whole country at the key time node, and determining the energy consumption structure of the target area at the target time node based on the energy consumption structure of the whole country at the key time node; determining a carbon emission coefficient corresponding to each energy of the target area; and calculating a carbon emission prediction result of the target region at the target time node based on the total energy consumption, the energy consumption structure and the carbon emission coefficient corresponding to each energy of the target region at the target time node. Therefore, the carbon emission prediction effect can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a regional carbon emission prediction, device and equipment based on energy consumption structure. Background Art

[0002] As a rapidly developing economy, my country's provinces, as its basic units, are facing environmental challenges brought by energy consumption and carbon emissions while promoting economic growth. From the black soil in the northeast to the tropical rainforest in the south, from the coastal economic belt in the east to the vast inland in the west, different provinces have their own unique energy consumption patterns and carbon emission characteristics due to their geographical location, economic development level, industrial structure and resource endowment. With the increasing global attention to climate change issues, the Chinese government has put forward the goals of "carbon peak" and "carbon neutrality", which requires provinces to effectively control energy consumption and reduce carbon emissions while maintaining economic growth. For example, the eastern coastal provinces such as Guangdong, Jiangsu, Shandong, etc., as the most economically developed regions in my country, have rapid industrial production, service industry and urbanization, large energy consumption, especially high dependence on electricity and fossil fuels, and relatively prominent carbon emission problems. The central and western provinces such as Inner Mongolia and Shanxi have become important energy supply bases for the country because of their rich coal resources, but they are also facing the problems of over-exploitation of resources and increased environmental pollution.

[0003] Traditional energy consumption control and carbon emission management methods are often based on static data and empirical judgments, which make it difficult to fully capture the dynamic changes in energy consumption and carbon emissions in various provinces. In particular, they fail to fully consider the impact of external factors such as technological progress, industrial structure adjustment, and policy intervention, resulting in a lack of foresight and pertinence in the formulation of energy policies. This not only limits the improvement of energy efficiency, but also affects the green transformation and sustainable development of the regional economy. Summary of the invention

[0004] In view of this, the purpose of the present invention is to provide a method, device and equipment for predicting regional carbon emissions based on energy consumption structure, so as to overcome the current problem of poor carbon emission prediction results.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] First invention, this application provides a regional carbon emission prediction method based on energy consumption structure, including:

[0007] Predicting the total energy consumption of the target area to obtain the total energy consumption of the target area at a target time node;

[0008] Predicting the national energy consumption structure at a key time node, obtaining the national energy consumption structure at the key time node, and determining the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node;

[0009] Determine the carbon emission coefficient corresponding to each energy source in the target area;

[0010] Based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy of the target area at the target time node, the carbon emission prediction result of the target area at the target time node is calculated.

[0011] Further, in the embodiment of the present application, the total energy consumption of the target area is predicted to obtain the total energy consumption of the target area at the target time node, including:

[0012] Determine the main variables for predicting the total energy consumption based on brainstorming and expert decision-making methods;

[0013] Based on correlation analysis, the final variables are selected from the main variables;

[0014] The total energy consumption of the target area is predicted based on the final variable and the principal component analysis method to obtain the annual total energy consumption of the target area at the target time node.

[0015] Furthermore, in an embodiment of the present application, the final variables include energy consumption per unit of GDP, rural population, urban employment population, industrial added value, energy consumption per unit of industrial added value, total oil consumption, total natural gas consumption, and total coal consumption.

[0016] Further, in the embodiment of the present application, the energy consumption structure of the whole country at the key time node is predicted to obtain the energy consumption structure of the whole country at the key time node, including:

[0017] For the preset scenarios, the national energy consumption structure at key time nodes is predicted to obtain the national energy consumption structure corresponding to each preset scenario at the key time nodes, wherein the preset scenarios include a conservative scenario, a positive scenario and an aggressive scenario, and the energy consumption structure includes the proportion of coal, oil, natural gas and non-fossil energy consumption.

[0018] Further, in the embodiment of the present application, determining the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node includes:

[0019] For each of the preset scenarios, based on the national energy consumption structure at the key time point, the national energy consumption structure within the preset time range is predicted according to the average annual growth rate;

[0020] According to the weights of the three preset scenarios, determine the final energy consumption structure of the country within the preset time range;

[0021] Based on the final energy consumption structure of the whole country within a preset time range and a preset model, the energy consumption structure of the target area within the preset time range is determined, wherein the target time node is within the preset time range.

[0022] Furthermore, in an embodiment of the present application, the preset model is established based on the energy consumption structure of the target area and the changing trend of the national energy consumption structure.

[0023] Furthermore, in an embodiment of the present application, the target area is a provincial area.

[0024] Further, in the embodiment of the present application, the calculation formula for calculating the predicted result of the carbon emissions of the target area at the target time node based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy of the target area at the target time node is:

[0025]

[0026] Where X is the predicted result of carbon emissions in the target area at the target time node; E is the total energy consumption in the target area at the target time node; S i K is the proportion of the consumption of the i-th energy in the total energy consumption of the target area at the target time node; i is the carbon emission coefficient of the i-th energy source in the target area at the target time node.

[0027] Second invention, the present application also provides a regional carbon emission prediction device based on energy consumption structure, comprising:

[0028] The first prediction module is used to predict the total energy consumption of the target area to obtain the total energy consumption of the target area at a target time node;

[0029] The second prediction module is used to predict the national energy consumption structure at the key time node, obtain the national energy consumption structure at the key time node, and determine the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node;

[0030] A determination module, used to determine the carbon emission coefficient corresponding to each energy source in the target area;

[0031] The calculation module is used to calculate the predicted result of the carbon emissions of the target area at the target time node based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy in the target area at the target time node.

[0032] The third invention, the present application also provides a regional carbon emission prediction device based on energy consumption structure, including a processor and a memory, wherein the processor is connected to the memory:

[0033] Wherein, the processor is used to call and execute the program stored in the memory;

[0034] The memory is used to store the program, and the program is at least used to execute the above-mentioned regional carbon emission prediction method based on energy consumption structure.

[0035] The present invention relates to the field of energy technology, and in particular to a regional carbon emission prediction, device and equipment based on energy consumption structure, the method specifically comprising: predicting the total energy consumption of a target area to obtain the total energy consumption of the target area at a target time node; predicting the energy consumption structure of the whole country at a key time node to obtain the energy consumption structure of the whole country at a key time node, and determining the energy consumption structure of the target area at the target time node based on the energy consumption structure of the whole country at the key time node; determining the carbon emission coefficient corresponding to each energy in the target area; and calculating the carbon emission prediction result of the target area at the target time node based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy in the target area at the target time node. In this way, the effect of carbon emission prediction can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 It is a flow chart of a method for predicting regional carbon emissions based on energy consumption structure provided by an embodiment of the present invention;

[0038] Figure 2 is a schematic diagram of the structure of a regional carbon emission prediction device based on energy consumption structure provided by an embodiment of the present invention;

[0039] Figure 3 It is a structural schematic diagram of a regional carbon emission prediction device based on energy consumption structure provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0040] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0041] Application Overview:

[0042] Existing technologies mainly rely on traditional statistical methods and empirical models in predicting urban or regional energy consumption and carbon emissions. These methods have obvious shortcomings in dealing with the complex relationship between energy consumption and carbon emissions. For example, traditional methods often make linear predictions based on historical data, which makes it difficult to capture the nonlinear dynamic relationship between energy consumption and carbon emissions. In addition, existing technologies rarely consider the combined impact of multi-dimensional factors such as economy, population, policy and technological progress, resulting in a lack of accuracy and adaptability in the prediction results. In terms of long-term trend prediction, existing technologies often ignore the potential impact of energy structure transformation and emerging technology applications on energy consumption and carbon emissions. These limitations restrict the application of existing technologies in formulating effective energy policies and achieving carbon emission control targets. Although all provinces are actively responding to the national "dual carbon" policy, promoting energy structure optimization and industrial upgrading, and the installed capacity and power generation of clean and renewable energy have increased significantly, the transformation of energy consumption patterns and the optimization of industrial structure still face challenges. Technological progress and the strengthening of infrastructure construction have increased the sensitivity and response speed to policy changes. However, in order to effectively decouple energy consumption from carbon emissions, optimize energy management, and support regions such as provincial units or cities to achieve green and low-carbon development goals while maintaining economic growth, more advanced and comprehensive forecasting methods and technological innovations are still needed to make up for the shortcomings of existing technologies.

[0043] Method Example:

[0044] The regional carbon emission prediction method based on energy consumption structure provided in this application involves the intersection of energy economics and environmental science, and predicts urban carbon emissions by analyzing the energy consumption structure. It can provide decision support tools for urban energy policy makers to achieve energy efficiency improvement, carbon emission reduction and economic sustainable development, and is particularly suitable for urban planning and environmental management scenarios that require energy consumption control and carbon emission analysis, such as large industrial areas or provinces, to promote energy structure optimization and green and low-carbon transformation.

[0045] Figure 1 This is a flow chart of a method for predicting regional carbon emissions based on energy consumption structure provided by an embodiment of the present invention. Figure 1 , this embodiment may include the following steps:

[0046] S101. Predict the total energy consumption of a target area to obtain the total energy consumption of the target area at a target time node.

[0047] S102. Predict the national energy consumption structure at key time nodes to obtain the national energy consumption structure at key time nodes, and determine the energy consumption structure of the target area at the target time node based on the national energy consumption structure at key time nodes.

[0048] S103, determining the carbon emission coefficient corresponding to each energy source in the target area;

[0049] S104. Calculate the predicted result of carbon emissions of the target area at the target time node based on the total energy consumption, energy consumption structure and carbon emission coefficients corresponding to each energy source in the target area at the target time node.

[0050] The regional carbon emission prediction method based on energy consumption structure provided in this application predicts the total energy consumption of the target area to obtain the total energy consumption of the target area at the target time node; predicts the national energy consumption structure at the key time node to obtain the national energy consumption structure at the key time node, and determines the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node; determines the carbon emission coefficient corresponding to each energy in the target area; calculates the carbon emission prediction result of the target area at the target time node based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy in the target area at the target time node. It can accurately analyze and predict the future energy consumption and carbon emission trends of various provinces, and provide data-driven decision support for policymakers by applying energy consumption structure analysis to achieve effective control of energy consumption and carbon emissions, optimize energy management, and support target areas such as a province and similar cities to achieve green and low-carbon development goals while maintaining economic growth.

[0051] In some embodiments of the present application, the total energy consumption of the target area is predicted to obtain the total energy consumption of the target area at a target time node, which may specifically include: first determining the main variables for predicting the total energy consumption based on brainstorming and expert decision-making methods; then screening the final variables from the main variables based on correlation analysis; finally, predicting the total energy consumption of the target area based on the final variables and principal component analysis to obtain the annual total energy consumption of the target area at the target time node.

[0052] Specifically, for example, we can comprehensively consider factors such as economic and social development, proportion of industrial structure, urbanization process, residents' consumption level and low-carbon economy, and on the basis of simplifying the attributes of factors affecting the total energy consumption, select multiple indicators through "brainstorming method" and "expert decision-making method" as the main variables for predicting the total energy consumption of target areas such as provincial unit areas.

[0053] Then, SPSS (Statistical Products and Services Solution Software) was used to conduct a correlation analysis between energy consumption and the above main variables, and the final variables were selected. For example, 9 final variables were selected, namely: energy consumption per unit of GDP, rural population, urban employment population, industrial added value, energy consumption per unit of industrial added value, total oil consumption, total natural gas consumption, primary electricity and other energy consumption, and total coal consumption.

[0054] On this basis, for these nine indicators, the principal component analysis method is used to determine the total energy consumption of the target area at the target time node. It should be noted that in this application, the forecast data can be for the year. For example, if the target time node is a certain year, the corresponding total energy consumption is the annual total energy consumption.

[0055] In some embodiments of the present application, predicting the national energy consumption structure at key time nodes to obtain the national energy consumption structure at key time nodes may include: predicting the national energy consumption structure at key time nodes for preset scenarios to obtain the national energy consumption structure at key time nodes corresponding to each preset scenario, wherein the preset scenarios include a robust scenario, a positive scenario, and an aggressive scenario, and the energy consumption structure includes the proportion of coal, oil, natural gas, and non-fossil energy consumption.

[0056] Specifically, we can use relevant research documents, such as the article "China's Energy Demand Forecast and Transformation Development Trends under the Carbon Peak and Carbon Neutrality Vision" published by the Carbon Neutrality Development Research Institute, to comprehensively analyze existing research results, comprehensively consider economic development, energy security and carbon emission reduction goals, and set up three scenarios for achieving carbon neutrality: conservative, active and radical. The energy demand of my country at key time nodes between 2022 and 2060 is predicted.

[0057] It should be noted that according to the model prediction of the above article, my country's national primary energy consumption will peak around 2030, slowly decline between 2030 and 2035, and then remain basically stable. In the energy consumption structure, the proportion of coal continues to decline, the proportion of oil remains basically stable, and the proportion of natural gas and non-fossil energy grows rapidly. Before 2035, the energy consumption structure under the three scenarios is relatively close. From 2035 to 2060, the energy consumption structure under the three scenarios gradually diverges, mainly due to the positioning of fossil energy in the energy structure, the development of emission reduction technologies such as renewable energy technology and CCUS, and the scale of hydrogen application in the field of carbon emission reduction. Among them, CCS / CCUS technology will achieve large-scale commercial application earlier in the robust scenario, renewable energy will break through faster in the radical scenario, and CCS / CCUS and renewable energy technologies will develop simultaneously in the positive scenario, resulting in differences in the forecast of total primary energy demand, carbon emissions, and carbon emission reduction contribution of ecological carbon sinks + CCS / CCUS.

[0058] On this basis, the future energy consumption structure forecast under the three scenarios can be specifically as follows (in this embodiment, the key time nodes are 2030, 2035 and 2060):

[0059] Under the conservative scenario, in 2030, the proportions of coal, oil, natural gas and non-fossil energy consumption will be 44.0%, 18.1%, 11.9% and 26.0% respectively; in 2035, they will be 39.7%, 15.8%, 13.5% and 31.0% respectively; and in 2060, they will be 7.2%, 7.2%, 12.4% and 73.2% respectively.

[0060] Under the positive scenario, in 2030, the proportions of coal, oil, natural gas and non-fossil energy consumption will be 42.8%, 18.4%, 12.0% and 26.8% respectively; in 2035, they will be 37.8%, 15.8%, 14.0% and 32.4% respectively; in 2060, they will be 4.9%, 5.6%, 9.4% and 80.1% respectively.

[0061] Under the radical scenario, in 2030, the consumption of coal, oil, natural gas and non-fossil energy will account for 43.2%, 17.4%, 11.6% and 27.8% respectively; in 2060, they will be 3.0%, 4.1%, 8.1% and 84.8% respectively.

[0062] On this basis, determining the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node may include: for each preset scenario, based on the national energy consumption structure at the key time node, predicting the national energy consumption structure within the preset time range according to the average annual growth rate; then determining the final energy consumption structure of the country within the preset time range according to the weights of the three preset scenarios; and determining the energy consumption structure of the target area within the preset time range based on the final energy consumption structure of the country within the preset time range and the preset model, wherein the target time node is within the preset time range.

[0063] Specifically, we first analyze the energy consumption structure at each key time node under three different scenarios, combine the national historical energy consumption data, and predict the national energy consumption structure (annual data) from 2024 to 2060 under the three scenarios according to the average annual growth rate.

[0064] Then, according to the pre-set weights of the three scenarios, the final energy consumption structure of the country between 2024 and 2060 is calculated.

[0065] Then, by comparing the energy consumption structure of a target region, such as a province, with that of the whole country, and analyzing the trend changes of the two, a model is established to predict and convert the energy consumption structure of the province in different time periods. Then, through this model, combined with the relevant policies of the province, the energy consumption structure of the province within a preset time range is predicted, such as the energy consumption structure of the province between 2024 and 2060.

[0066] It should be noted that when predicting the carbon emissions at a target time node in the target area in the future, the energy consumption structure of the corresponding time node in the area is needed. Therefore, the preset time range, such as 2024 to 2060, needs to include the time node to be predicted. In other words, when predicting a certain time node in the future, it is all for the time node within the preset time range.

[0067] Furthermore, in the present application, the carbon emission coefficient corresponding to each energy source in the target area is determined, and specifically, the carbon emission coefficient of coal, oil, natural gas and non-fossil energy in a province can be found through relevant data such as the National Statistical Yearbook and the Statistical Yearbook of a province.

[0068] On this basis, the carbon emission forecast results are calculated, and in this application, specifically, the carbon dioxide emission forecast results generated by fossil fuel combustion energy activities are calculated.

[0069] Specifically, the carbon dioxide emissions generated by the above-mentioned fossil fuel combustion energy activities, that is, the carbon emissions mentioned in this application, can be calculated based on the consumption of different fuels and the corresponding carbon emission coefficient and other parameters of the carbon dioxide emissions of a single energy source, and then accumulated and comprehensively calculated. The specific calculation formula is:

[0070]

[0071] Among them, X is the predicted result of carbon emissions in the target area at the target time node; E is the total energy consumption in the target area at the target time node; S i K is the proportion of the consumption of the i-th energy in the total energy consumption of the target area at the target time node; i is the carbon emission coefficient of the i-th energy in the target area at the target time node; the above fraction is the conversion coefficient from carbon emissions to carbon dioxide emissions.

[0072] By substituting the calculated carbon emissions forecast results, energy consumption structure and energy consumption coefficient corresponding to the target area at the target time node into the above formula, the carbon emissions of a certain area at the corresponding time node can be obtained.

[0073] The regional carbon emission prediction method based on energy consumption structure proposed in the present invention is applicable to various regions such as provinces, and is used to realize energy consumption and carbon emission prediction analysis. It can scientifically predict the energy consumption and carbon emission trends of various provinces in the future period of time by comprehensively considering multi-dimensional factors such as economic growth, population changes, industrial structure, technological progress, and policy orientation according to the actual situation of different provinces. And it can provide customized policy recommendations for various departments such as governments at all levels through high-precision time series prediction, scenario simulation and optimization algorithms, and help formulate more accurate and effective energy management and emission reduction strategies.

[0074] Device Example:

[0075] The present invention also provides a regional carbon emission prediction device based on energy consumption structure, which is used to implement the above method embodiment. Figure 2 Schematic diagram of the structure of the regional carbon emission prediction device based on energy consumption structure provided by the embodiment of the present invention. Figure 2 As shown, the device comprises:

[0076] The first prediction module 11 is used to predict the total energy consumption of the target area and obtain the total energy consumption of the target area at the target time node; the second prediction module 12 is used to predict the energy consumption structure of the whole country at the key time node, obtain the energy consumption structure of the whole country at the key time node, and determine the energy consumption structure of the target area at the target time node based on the energy consumption structure of the whole country at the key time node; the determination module 13 is used to determine the carbon emission coefficient corresponding to each energy in the target area; the calculation module 14 is used to calculate the carbon emission prediction result of the target area at the target time node based on the total energy consumption of the target area at the target time node, the energy consumption structure and the carbon emission coefficient corresponding to each energy.

[0077] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0078] Device Example:

[0079] The present invention also provides a regional carbon emission prediction device based on energy consumption structure, which is used to implement the above method embodiment. Figure 3 is a schematic diagram of the structure of a regional carbon emission prediction device based on energy consumption structure provided by an embodiment of the present invention, such as Figure 3 As shown, the regional carbon emission prediction device based on energy consumption structure of this embodiment includes a processor 21 and a memory 22, and the processor 21 is connected to the memory 22. The processor 21 is used to call and execute the program stored in the memory 22; the memory 22 is used to store the program, which is at least used to execute the regional carbon emission prediction method based on energy consumption structure in the above embodiment.

[0080] The specific implementation scheme of the regional carbon emission prediction device based on energy consumption structure provided in the embodiment of the present application can refer to the implementation scheme of the regional carbon emission prediction method based on energy consumption structure in any of the above embodiments, which will not be repeated here.

[0081] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0082] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0083] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0084] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0085] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0086] In addition, each functional unit in each embodiment of the present invention may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0087] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0088] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0089] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A regional carbon emission prediction method based on energy consumption structure, characterized in that: include: Predicting the total energy consumption of the target area to obtain the total energy consumption of the target area at a target time node; Predicting the national energy consumption structure at a key time node, obtaining the national energy consumption structure at the key time node, and determining the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node; Determine the carbon emission coefficient corresponding to each energy source in the target area; Based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy of the target area at the target time node, the carbon emission prediction result of the target area at the target time node is calculated.

2. The regional carbon emission prediction method based on energy consumption structure according to claim 1 is characterized in that: The total energy consumption of the target area is predicted to obtain the total energy consumption of the target area at the target time node, including: Determine the main variables for predicting the total energy consumption based on brainstorming and expert decision-making methods; Based on correlation analysis, the final variables are selected from the main variables; The total energy consumption of the target area is predicted based on the final variable and the principal component analysis method to obtain the annual total energy consumption of the target area at the target time node.

3. The regional carbon emission prediction method based on energy consumption structure according to claim 2 is characterized in that: The final variables include energy consumption per unit of GDP, rural population, urban employment population, industrial added value, energy consumption per unit of industrial added value, total oil consumption, total natural gas consumption, and total coal consumption.

4. The regional carbon emission prediction method based on energy consumption structure according to claim 3 is characterized in that: The energy consumption structure of the whole country at the key time node is predicted to obtain the energy consumption structure of the whole country at the key time node, including: For the preset scenarios, the national energy consumption structure at key time nodes is predicted to obtain the national energy consumption structure corresponding to each preset scenario at the key time nodes, wherein the preset scenarios include a conservative scenario, a positive scenario and an aggressive scenario, and the energy consumption structure includes the proportion of coal, oil, natural gas and non-fossil energy consumption.

5. The regional carbon emission prediction method based on energy consumption structure according to claim 4 is characterized in that: The determining of the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node includes: For each of the preset scenarios, based on the national energy consumption structure at the key time point, the national energy consumption structure within the preset time range is predicted according to the average annual growth rate; According to the weights of the three preset scenarios, determine the final energy consumption structure of the country within the preset time range; Based on the final energy consumption structure of the whole country within a preset time range and a preset model, the energy consumption structure of the target area within the preset time range is determined, wherein the target time node is within the preset time range.

6. The regional carbon emission prediction method based on energy consumption structure according to claim 5 is characterized in that: The preset model is established based on the energy consumption structure of the target area and the changing trend of the national energy consumption structure.

7. The regional carbon emission prediction method based on energy consumption structure according to claim 6 is characterized in that: The target area is a provincial level area.

8. The regional carbon emission prediction method based on energy consumption structure according to claim 7 is characterized in that: The calculation formula for calculating the predicted result of carbon emissions of the target area at the target time node based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy source of the target area at the target time node is: Where X is the predicted result of carbon emissions in the target area at the target time node; E is the total energy consumption in the target area at the target time node; S i K is the proportion of the consumption of the i-th energy in the total energy consumption of the target area at the target time node; i is the carbon emission coefficient of the i-th energy source in the target area at the target time node.

9. A regional carbon emission prediction device based on energy consumption structure, characterized in that: include: The first prediction module is used to predict the total energy consumption of the target area to obtain the total energy consumption of the target area at a target time node; The second prediction module is used to predict the national energy consumption structure at the key time node, obtain the national energy consumption structure at the key time node, and determine the energy consumption structure of the target area at the target time node based on the national energy consumption structure at the key time node; A determination module, used to determine the carbon emission coefficient corresponding to each energy source in the target area; The calculation module is used to calculate the predicted result of the carbon emissions of the target area at the target time node based on the total energy consumption, energy consumption structure and carbon emission coefficient corresponding to each energy in the target area at the target time node.

10. A regional carbon emission prediction device based on energy consumption structure, characterized in that: The invention comprises a processor and a memory, wherein the processor is connected to the memory: Wherein, the processor is used to call and execute the program stored in the memory; The memory is used to store the program, and the program is at least used to execute the regional carbon emission prediction method based on energy consumption structure as described in any one of claims 1-8.

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