Atmospheric carbon dioxide emission prediction and management method

Abstract

The invention discloses an atmospheric carbon dioxide emission prediction and management method, and relates to the field of carbon emission prediction. Establishment of a prediction model: constructing a prediction model of an energy consumption structure based on a Markov chain; determining a transfer matrix: firstly calculating the retention probability, then calculating the transfer probability of the element with the retention probability of 1 in the row of the transfer matrix, determining the transfer probability of the element with the retention probability of less than 1 in the row of the transfer matrix, and determining the non-zero transfer probability of the element with the retention probability of less than 1 in the row of the transfer matrix; predicting an energy consumption structure: predicting the energy consumption structure by using a prediction model based on the transfer matrix and the retention probability; and carbon emission prediction and management: based on the prediction of the energy consumption structure, predicting the carbon emission, and adjusting the energy consumption structure according to the prediction result of the carbon emission. According to the invention, carbon dioxide emission can be accurately and effectively predicted and managed based on the predicted energy consumption structure.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of carbon emission prediction, and specifically to a method for predicting and managing atmospheric carbon dioxide emissions. Background Art

[0002] Energy is an important material basis for human survival and development. It is of great strategic significance to promote changes in energy production and utilization, adjust and optimize the energy structure, and build a safe, stable, economical and clean modern energy industry system. Energy consumption is one of the main causes of carbon dioxide emissions. Fossil fuels dominate global energy consumption. The combustion process of these fossil fuels will release a large amount of carbon dioxide gas, which will lead to an increase in global carbon dioxide emissions. With the continuous development of the global economy, energy consumption has also shown a growing trend, which has further aggravated the problem of carbon dioxide emissions.

[0003] Carbon dioxide is one of the main greenhouse gases, and changes in its concentration in the atmosphere directly affect the global climate system. By predicting carbon dioxide emissions, scientists can more accurately understand the mechanism of climate change, predict future climate trends, and provide a scientific basis for formulating response strategies. This will help us better cope with the challenges brought about by climate change, such as global warming and extreme weather.

[0004] Based on existing technologies, there is still a lack of a method that can accurately and effectively predict carbon dioxide emissions. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide a method for predicting and managing atmospheric carbon dioxide emissions to solve the technical problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for predicting and managing atmospheric carbon dioxide emissions comprises the following steps:

[0008] Establishment of prediction model: Construct a prediction model of energy consumption structure based on Markov chain; the energy sources include coal, oil, natural gas, water, wind and nuclear power;

[0009] Determine the transfer matrix: first calculate the retention probability. When the energy consumption structure shifts from time n to time n+1, if the consumption share of any energy source remains unchanged or increases, the retention probability of this energy source in the transfer matrix is ​​1; if the share is decreasing, the retention probability of this energy source in the transfer matrix is ​​the share at time n+1 / the share at time n.

[0010] Then calculate the transfer probability of the row in the transfer matrix where the element with a retention probability of 1 is located. If the retention probability of a column in the transfer matrix is ​​less than 1, it means that the energy consumption share represented is decreasing, so it is impossible to absorb the consumption share of other energy sources, and the absorption probability of this column is 0;

[0011] Determine the transfer probability of the row in the transfer matrix where the element with retention probability less than 1 is located. If the retention probability of a column is less than 1, it means that the energy consumption share represented by the column is decreasing, so it is impossible to absorb the consumption share of other energy sources, and the absorption probability of the column is 0;

[0012] Determine the non-zero transition probabilities of the rows in the transition matrix where the elements with retention probability less than 1 are located;

[0013] Prediction of energy consumption structure: Predict the energy consumption structure using a prediction model based on the transfer matrix and retention probability;

[0014] Carbon emissions forecasting and management: Based on the forecast of energy consumption structure, carbon emissions forecast is made, and the energy consumption structure is adjusted according to the results of carbon emissions forecast.

[0015] Preferably, the state vector of the energy consumption structure is A(n),

[0016] A(n)={A c (n), A o (n), A g (n), A e (n)};

[0017] Among them, A c (n), A o (n), A g (n), A e (n) represent the proportion of coal, oil, natural gas, hydropower, wind power and nuclear power in the total primary energy consumption.

[0018] Preferably, the transfer matrix B(n) of the energy consumption structure from time n to time n+1 is expressed as:

[0019]

[0020] Among them, the elements on the main diagonal of B(n) are called retention probabilities, which indicate the probability that the proportions of the four energy consumption remain unchanged; the row elements outside the main diagonal are called transfer probabilities, which indicate the probability that the share of this type of energy consumption will transfer to other energy consumption; the column elements outside the main diagonal are called absorption probabilities, which indicate the probability that this type of energy consumption will absorb the share of other energy consumption; and the sum of the elements in each row of the transfer matrix is ​​1.

[0021] Preferably, the average transition probability of the energy consumption structure is:

[0022] B=[B1*B2*B3*…*B n ] 1 / n ;

[0023] Among them, B1*B2*B3*…*B n Represents the transfer matrix of energy consumption structure at each step from the initial moment to moment n.

[0024] Preferably, based on the average transition probability and state vector of the energy consumption structure, the energy consumption structure at time n+k can be predicted, and the formula is as follows:

[0025] A(n+k)=A(n)*B k .

[0026] In summary, the present invention mainly has the following beneficial effects:

[0027] This method uses the Markov model to predict carbon dioxide emissions by predicting the energy consumption structure. The energy consumption structure has the characteristic of Markov no aftereffect, that is, the change process of things is only related to the recent state, but not to the past state of things. Therefore, introducing Markov theory into the prediction of energy consumption structure can improve its accuracy; therefore, the final prediction of carbon dioxide emissions is more accurate.

[0028] At the same time, the results of carbon dioxide emissions provide a basis for predicting future energy consumption trends;

[0029] This basis can be used to optimize suggestions for adjusting consumption structure, thereby reducing carbon emissions and improving social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is the overall step flow framework diagram of the present invention;

[0031] Figure 2 The primary energy consumption structure chart of Province A from 2004 to 2011 of the present invention;

[0032] Figure 3 The transfer probability matrix chart of the primary energy consumption structure of Province A in each step from 2004 to 2011 of the present invention;

[0033] Figure 4 The primary energy consumption structure chart of Province A under two scenarios from 2012 to 2020 of the present invention;

[0034] Figure 5 This is a chart of the primary energy consumption structure of Province A under two scenarios in 2015 of the present invention. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0036] Example

[0037] like Figure 1 As Figure 5 As shown, a method for predicting and managing atmospheric carbon dioxide emissions comprises the following steps:

[0038] Establishment of prediction model: Construct a prediction model of energy consumption structure based on Markov chain; energy includes coal, oil, natural gas, water, wind and nuclear power;

[0039] Determine the transfer matrix: first calculate the retention probability. When the energy consumption structure shifts from time n to time n+1, if the consumption share of any energy source remains unchanged or increases, the retention probability of this energy source in the transfer matrix is ​​1; if the share is decreasing, the retention probability of this energy source in the transfer matrix is ​​the share at time n+1 / the share at time n.

[0040] Then calculate the transfer probability of the row in the transfer matrix where the element with a retention probability of 1 is located. If the retention probability of a column in the transfer matrix is ​​less than 1, it means that the energy consumption share represented is decreasing, so it is impossible to absorb the consumption share of other energy sources, and the absorption probability of this column is 0;

[0041] Determine the transfer probability of the row in the transfer matrix where the element with retention probability less than 1 is located. If the retention probability of a column is less than 1, it means that the energy consumption share represented by the column is decreasing, so it is impossible to absorb the consumption share of other energy sources, and the absorption probability of the column is 0;

[0042] Determine the non-zero transition probabilities of the rows in the transition matrix where the elements with retention probability less than 1 are located;

[0043] Prediction of energy consumption structure: Predict the energy consumption structure using the prediction model based on the transfer matrix and retention probability;

[0044] Carbon emissions forecasting and management: Based on the forecast of energy consumption structure, carbon emissions forecast is made, and the energy consumption structure is adjusted according to the results of carbon emissions forecast.

[0045] The state vector of energy consumption structure is A(n),

[0046] A(n)={A c (n), A o (n), A g (n), A e (n)};

[0047] Among them, Ac (n), A o (n), A g (n), A e (n) represent the proportion of coal, oil, natural gas, hydropower, wind power and nuclear power in the total primary energy consumption.

[0048] The transfer matrix B(n) of the energy consumption structure from time n to time n+1 is expressed as:

[0049]

[0050] Among them, the elements on the main diagonal of B(n) are called retention probabilities, which indicate the probability that the proportions of the four energy consumption remain unchanged; the row elements outside the main diagonal are called transfer probabilities, which indicate the probability that the share of this type of energy consumption will transfer to other energy consumption; the column elements outside the main diagonal are called absorption probabilities, which indicate the probability that this type of energy consumption will absorb the share of other energy consumption; and the sum of the elements in each row of the transfer matrix is ​​1.

[0051] The average transition probability of energy consumption structure is:

[0052] B=[B1*B2*B3*…*B n ] 1 / n ;

[0053] Among them, B1*B2*B3*…*B n Represents the transfer matrix of energy consumption structure at each step from the initial moment to moment n.

[0054] Based on the average transition probability and state vector of the energy consumption structure, the energy consumption structure at time n+k can be predicted. The formula is as follows:

[0055] A(n+k)=A(n)*B k .

[0056] It should be noted that in this embodiment, the energy consumption of Province A is taken as an example.

[0057] like Figure 2 As shown in Figure 2, the energy consumption structure data from 2004 to 2011 is used to calculate the transition probability matrix. The steps for calculating the transition probability matrix are as follows:

[0058] 1. Calculate the retention probability: When the energy consumption structure shifts from time n to time n+1, if the consumption share of a certain energy remains unchanged or increases, the retention probability of this energy in the transfer matrix is ​​1; if the share is decreasing, the retention probability of this energy in the transfer matrix is ​​the share at time n+1 / the share at time n;

[0059] 2. Calculate the transition probability of the row where the element with a retention probability of 1 is located: If the retention probability of a certain energy is 1, it means that the energy consumption share represented by the row remains unchanged or increases, so it is impossible to transfer to other energy sources, and the sum of the elements in each row of the transition probability matrix is ​​1, so the transition probability of this row is 0.

[0060] 3. Determine the transfer probability of the row where the element with retention probability less than 1 is located: If the retention probability of a column is less than 1, it means that the energy consumption share represented by the column is decreasing, so it is impossible to absorb the consumption share of other energy sources, and the absorption probability of the column is 0.

[0061] 4. Determine the non-zero transition probabilities of the rows where the elements with retention probability less than 1 are located: Taking the scenario where the retention probability of coal is less than 1 as an example, the following is shown:

[0062] Probability of coal to oil transition:

[0063]

[0064] Probability of coal to gas transition:

[0065]

[0066] Probability of coal-to-water, wind and nuclear power conversion:

[0067]

[0068] According to the above four steps, the transfer probability matrix of the primary energy consumption structure of Province A from 2004 to 2011 can be calculated respectively. The average transfer probability matrix is:

[0069]

[0070] like Figure 3 As shown, it can be seen from the average transition probability matrix:

[0071] 1. From the first row of the average transfer probability matrix, we can see that the probability of oil, natural gas, hydropower, wind power and nuclear power replacing coal is 30%, 0.80% and 0.45% respectively. It can be seen that in reducing the share of coal consumption, oil contributes relatively more. This is because before 2011, there was no nuclear power plant in Province A, and by 2011, the development and utilization of hydropower in Province A had reached 80%, so there was little potential for further development of hydropower. At the same time, Province A is a coastal city, and oil import and export is relatively convenient.

[0072] 2. From the second, third and fourth rows of the average transfer probability matrix, it can be seen that the probability of transfer from oil, natural gas, hydropower, wind power and nuclear power to coal is relatively large. This is because compared with other energy sources, China's coal is relatively abundant and relatively cheap, so it is more likely to absorb other energy sources.

[0073] 3. From the high retention probability on the main diagonal, it can be seen that the behavior of consumers and producers in choosing energy is relatively fixed, and the primary energy consumption structure of Province A is relatively stable. Between 2004 and 2011, the probability of the annual consumption of coal, oil, natural gas, and hydropower, wind and nuclear power remaining the same as the previous year was 97.40%, 94.94%, 91.02% and 86.96%. Coal has the highest retention probability, indicating that the energy consumption structure dominated by coal is difficult to change in the short term. Hydropower, wind and nuclear power have the lowest retention probability, only 86.96%, because before 2011, no nuclear power plant in Province A had been built and put into operation, and the degree of hydropower development and utilization had reached 80%, and the potential for further development of hydropower was not great, which led to a high possibility of the transfer of hydropower, wind and nuclear power to other energy consumption. However, with the completion and operation of nuclear power plants in the other three urban areas of Province A, the consumption proportion of hydropower, wind and nuclear power will increase.

[0074] Based on the energy consumption structure data of 2004, this paper uses the above average transition probability matrix to predict the energy consumption structure of 2011, and obtains that the consumption proportions of coal, oil, natural gas, hydropower, wind power and nuclear power are 64.4013, 22.4999, 4.2949 and 8.7971, which are basically consistent with the actual values ​​of 64.4, 22.5, 4.3 and 8.8, and the error is within the control range, indicating that the model has good applicability.

[0075] like Figure 4 As shown in the figure, the forecast of primary energy consumption structure is as follows:

[0076] 1. No planning constraints: If we ignore the various policies and guidelines of the country and Province A and only predict the spontaneous change of energy structure in the future based on the evolution law of the past, then we can use formula (2) to make predictions based on the primary energy consumption structure vector and the average transition probability matrix B in 2011. For example, the primary energy consumption structure prediction for 2012 is: A(2012) = A(2011)*B1.

[0077] 2. With planning constraints: Considering policy factors, by 2015, the consumption of hydropower, wind power, and nuclear power is planned to reach 20% of the total primary energy consumption; by 2020, the consumption of hydropower, wind power, and nuclear power is planned to reach 25.1% of the total primary energy consumption. The previous forecast results without energy development planning constraints showed that the proportion of hydropower, wind power, and nuclear power in primary energy consumption remained at around 8.6% during 2012-2020, with a minimum of 8.53% and a maximum of 8.71%, which is far from the energy planning targets of 20% (2015) and 25.1% (2020). Therefore, the following will use the targets set by the energy development plan of Province A as constraints, and adjust the transfer matrix in two stages according to the different targets in 2015 and 2020 to correct the forecast results.

[0078] like Figure 5 As shown, according to the target adjustment transfer matrix and forecast for 2015, this paper assumes that during the period 2012-2015, the changes in the proportion of oil and natural gas consumption in Province A will still maintain the basic trend of energy structure adjustment in the past; and the increase in the proportion of hydropower, wind and nuclear power will be supplemented by the reduction in the proportion of coal.

[0079] Assume that under the energy planning constraint scenario, from 2011 to 2015, the adjusted average one-step transfer probability matrix is ​​B1, then the formula A(2015)=A(2011)*(B1)4 holds, and then assume C=(B1)4. According to the prediction results of natural gas under the scenario without planning constraints, it can be seen that the proportion of natural gas will continue to rise, so the third row vector of C is [0, 0, 1, 0]; according to the energy planning of Province A, it can be seen that the proportion of hydropower, wind power and nuclear power will continue to rise, so the third row vector of C is [0, 0, 0, 1]; because the proportion of coal and oil will decrease, the first and second row vectors of C can be calculated according to the formula. The calculated C is:

[0080]

[0081] The adjusted average one-step transition probability matrix B1 can be calculated by Matlab software as follows:

[0082]

[0083] According to the average one-step transfer probability matrix B1 and the energy consumption structure vector A(2011) in 2011, the primary energy consumption structure with planning constraints from 2012 to 2015 can be predicted, as follows: Figure 4 As shown; it can be seen that:

[0084] (1) The difference between the predicted oil and gas consumption ratios under the two scenarios is very small, which is only caused by calculation errors. This shows that the calculation results are consistent with the previous assumptions when adjusting the transition probability matrix that the changes in the oil and gas consumption ratios still maintain the basic trend of the past.

[0085] (2) The changing trends of the proportion of coal consumption predicted in the two scenarios are completely opposite. Under no planning constraints, the proportion of coal consumption increases year by year, while under planning constraints, it decreases year by year. The proportion of natural gas consumption also has a similar situation. Under no planning constraints, it first decreases slightly and then increases slowly, but the fluctuation is not large. Under planning constraints, the average annual growth rate reaches 9.91%. This shows that in order to improve the energy structure and achieve the planning goals, it is necessary to accelerate the development of hydropower, wind power, nuclear power and other clean energy.

[0086] The working principle of the present invention is:

[0087] Markov chain refers to a random process in which the future state of a system is only related to the current state and has nothing to do with the past state, that is, it has the characteristic of no aftereffect. Its characteristic of no aftereffect makes the Markov prediction model immune to the influence of the accuracy of prediction due to the non-timeliness of data. As a technology for predicting the changing law of random processes, Markov prediction uses the current state and change trend of one variable to predict the future state and change trend of the variable. It predicts carbon dioxide emissions through energy consumption structure prediction, which is more accurate. At the same time, it provides a basis for predicting the trend of future energy consumption based on the results of carbon dioxide emissions.

[0088] The above embodiments are only for illustrating the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for predicting and managing atmospheric carbon dioxide emissions, characterized in that: The following steps are involved: S1. Establishment of prediction model: Construct a prediction model of energy consumption structure based on Markov chain; the energy includes coal, oil, natural gas, water, wind and nuclear power; S2. Determine the transfer matrix: first calculate the retention probability. When the energy consumption structure shifts from time n to time n+1, if the consumption share of any energy source remains unchanged or increases, the retention probability of this energy source in the transfer matrix is ​​1; if the share is decreasing, the retention probability of this energy source in the transfer matrix is ​​the share at time n+1 / the share at time n. Then calculate the transfer probability of the row in the transfer matrix where the element with a retention probability of 1 is located. If the retention probability of a column in the transfer matrix is ​​less than 1, it means that the energy consumption share represented is decreasing, so it is impossible to absorb the consumption share of other energy sources, and the absorption probability of this column is 0; Determine the transfer probability of the row in the transfer matrix where the element with retention probability less than 1 is located. If the retention probability of a column is less than 1, it means that the energy consumption share represented by the column is decreasing, so it is impossible to absorb the consumption share of other energy sources, and the absorption probability of the column is 0; Determine the non-zero transition probabilities of the rows in the transition matrix where the elements with retention probability less than 1 are located; S3. Prediction of energy consumption structure: Predict the energy consumption structure using the prediction model based on the transfer matrix and retention probability; S4. Carbon emissions forecasting and management: Based on the forecast of energy consumption structure, carbon emissions forecast is made, and the energy consumption structure is adjusted according to the results of carbon emissions forecast.

2. A method for predicting and managing atmospheric carbon dioxide emissions according to claim 1, characterized in that: The state vector of the energy consumption structure is A(n), A(n)={A c (n),A o (n),A g (n),A e (n)}; Among them, A c (n), A o (n), A g (n), A e (n) represent the proportion of coal, oil, natural gas, hydropower, wind power and nuclear power in the total primary energy consumption.

3. A method for predicting and managing atmospheric carbon dioxide emissions according to claim 1, characterized in that: The transfer matrix B(n) of the energy consumption structure from time n to time n+1 is expressed as: Among them, the elements on the main diagonal of B(n) are called retention probabilities, which indicate the probability that the proportions of the four energy consumption remain unchanged; the row elements outside the main diagonal are called transfer probabilities, which indicate the probability that the share of this type of energy consumption will transfer to other energy consumption; the column elements outside the main diagonal are called absorption probabilities, which indicate the probability that this type of energy consumption will absorb the share of other energy consumption; and the sum of the elements in each row of the transfer matrix is ​​1.

4. A method for predicting and managing atmospheric carbon dioxide emissions according to claim 1, characterized in that: The average transition probability of the energy consumption structure is: <h2 style=";text-align:left;direction:ltr">B=[B1*B2*B3*…*B<h2 style=";text-align:left;direction:ltr"> n <h2 style=";text-align:left;direction:ltr"> ]<h2 style=";text-align:left;direction:ltr"> 1 / n <h2 style=";text-align:left;direction:ltr"> ; Among them, B1*B2*B3*…*B n Represents the transfer matrix of energy consumption structure at each step from the initial moment to moment n.

5. A method for predicting and managing atmospheric carbon dioxide emissions according to claim 4, characterized in that: Based on the average transition probability and state vector of the energy consumption structure, the energy consumption structure at time n+k can be predicted. The formula is as follows: A(n+k)=A(n)*B k 。

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