CN114077930A - Economic structure change analysis method based on power consumption - Google Patents

Economic structure change analysis method based on power consumption Download PDF

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CN114077930A
CN114077930A CN202111423238.1A CN202111423238A CN114077930A CN 114077930 A CN114077930 A CN 114077930A CN 202111423238 A CN202111423238 A CN 202111423238A CN 114077930 A CN114077930 A CN 114077930A
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文明
李文英
周红
许楚幡
钟原
罗姝晨
廖菁
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State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Hunan Electric Power Co Ltd
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State Grid Hunan Electric Power Co Ltd
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Abstract

A method for predicting economic structure change based on power consumption obtains direct consumption coefficients from i department to j department in a basic year according to input-output table data of the basic year
Figure DDA0003377278130000011
And j department basic year and target year power consumption data obtain target year direct consumption coefficient a containing power consumptionij(ii) a Establishing a manufacturing utility and substitute utility matrix R, S according to the input-output table and RAS method to obtain a direct consumption coefficient matrix A of the target year1(ii) a Optimizing each department direct consumption coefficient weighted w according to least square method, calculating n x n
Figure DDA0003377278130000012
Form the eyesDirect consumption coefficient matrix A of standard years2(ii) a To A1And A2Average and sum to obtain direct consumption coefficient matrix A3According to A3And compiling an energy input-output table of the target year, solving the power consumption increment delta E from the base year to the target year, and decomposing the delta E by adopting a two-stage decomposition method in the SDA structural decomposition according to the six change factors to obtain a calculation formula of the six change factors for judging the change condition of the economic structure. The invention provides a method for reflecting power consumption change factors, which is beneficial to planning the actual power generation.

Description

Economic structure change analysis method based on power consumption
Technical Field
The invention provides an analysis method for economic structure change based on power consumption, and belongs to the application field of predicting economic structure change conditions by taking power consumption as a reference.
Background
Electric power is the most common energy form, is widely applied to the production and living fields, is an important basis for national economic development and normal operation of modern society, and is particularly important for keeping electric power consumption and economic growth and coordinated development. At present, the economy is accelerated, the structure and the increasing power are structurally changed, and the electric power consumption is greatly influenced. The relation between the power consumption and the change of the economic structure in a new stage is explored, factors influencing the power consumption are clarified, and the method has great significance for realizing the coordinated development of the power industry and the economy.
At present, the relation between power consumption and economic growth is mainly researched by adopting time series methods such as a co-integration analysis, a Glange causal test, a vector error correction model and the like, but as the macro-economic operation is a complex system, model setting errors may exist in the regression analysis based on the parameter model. In part of researches, the input-output table and an SDA method are used for analyzing the economic structure change, but due to the blank window period and the time lag compiled by the input-output table, most of the researches use the input-output table of similar years as a substitute, so that the analysis conclusion is greatly different from the actual situation. In addition, due to the influences of various factors such as technical progress, industrial structure change, management optimization and the like, the direct consumption coefficient will change, and the prior art cannot correct the direct consumption coefficient to obtain a direct consumption coefficient matrix capable of reflecting the real situation, and cannot make up for the defect that the data has a blank window period.
Disclosure of Invention
The invention aims to overcome the limitations of time series analysis and the hysteresis of input-output table publishing time in the prior art, and provides an analysis method of economic structure change based on power consumption.
The invention applies a plurality of methods to extrapolate and correct the input-output table of independent years, combines the relation between power consumption and economic growth, adopts an SDA model to combine the two methods, and fills the defects of the lack of time sequence analysis and the blank of real-time power consumption influence factors when a structural decomposition model is used for research.
A method of predicting economic structure changes based on power consumption, comprising the steps of:
the first step is as follows: according to the data of the direct input and consumption matrix in the input and output table of the basic year, the basic year direct consumption flow from the department i to the department j is obtained
Figure RE-GDA0003466969070000021
Calculating the direct consumption coefficient from the basic year i department to the j department according to the formula (1):
Figure RE-GDA0003466969070000022
wherein the content of the first and second substances,
Figure RE-GDA0003466969070000023
representing the jth department of the Bass yearThe total yield of (1); according to the direct consumption coefficient from the i department to the j department in the basic year
Figure RE-GDA0003466969070000024
And j department base year and target year electric power consumption data Ej 0And Ej 1Calculating a target annual direct consumption coefficient including power consumption from the department i to the department j according to the formula (2):
Figure RE-GDA0003466969070000025
will consume the flow directly by each department in the basic year
Figure RE-GDA0003466969070000026
Adding up the sum and adding up the value V of the direct consumption flow of each department in the target yearjAdding to obtain the total initial value X of each department in the target yearj：
Figure RE-GDA0003466969070000027
Setting:
the vector of the total target period value is X ═ X1,x2,...,xn)T，
The final required product vector Y of the target period is (Y)1,y2,...,yn)T，
The column sum vector used in the target period is U ═ U (U)1,u2,...,un)T，
The target intermediate input line and vector are V ═ V (V)1,v2,...,vn)T；
According to the input-output table, the intermediate demand sum and the intermediate input sum of each department can be obtained according to the RAS method:
Figure RE-GDA0003466969070000028
in the formula, n represents the number of departments; performing iterative operation on the formula (4), wherein k represents iterative period number until ri (k)And sj (k)Equals 1, resulting in k periods of n r per periodi (k)And sj (k)：
According to the RAS method, matrices R and S of manufacturing utility and alternative utility are obtained,
Figure RE-GDA0003466969070000031
direct consumption coefficient matrix A of target years according to RAS method1Has the formula (6):
A1＝R×A0×S-----(6)
in the formula (6), A0Is a direct consumption coefficient matrix of the base year, A1Is a direct consumption coefficient matrix for the target year;
the second step is that: solving according to a formula (7) according to a least square method:
Figure RE-GDA0003466969070000032
in the formula (7), the reaction mixture is,
Figure RE-GDA0003466969070000033
is the sum of squares of the direct consumption coefficient differences from the base year to the target year;
and (3) weighting the direct consumption coefficients of all departments in the input-output table, and optimizing the formula (7) to obtain a formula (8):
Figure RE-GDA0003466969070000034
solving the minimum value of the formula (8) to obtain n x n
Figure RE-GDA0003466969070000035
Form a direct consumption coefficient matrix A of the target year2：
Figure RE-GDA0003466969070000036
The third step: a is to be1And A2Average summation is carried out to obtain a direct consumption coefficient matrix A under the RAS-WLS method3；
The fourth step: direct consumption coefficient matrix A according to target year3And electric power consumption data and macroscopic economic statistical data of the target year, and compiling an energy input-output table of the target year;
the fifth step: the target annual energy input-output table obtained in the fourth step has the following formulas (10) and (11):
Figure RE-GDA0003466969070000037
Figure RE-GDA0003466969070000038
wherein the direct power consumption coefficient djThe amount of electrical energy required to be consumed directly by the ith department in producing a unit of product is expressed as
Figure RE-GDA0003466969070000039
1 xn djForming an electric power direct energy consumption coefficient matrix D;
rewritten in matrix form, with:
AX+F＝X-----(12)
DX+H＝E-----(13)
in the formulas (12) and (13), A, X is defined as the same as the previous one, the matrix A is multiplied by the matrix X to represent the intermediate demand sum, F represents the final demand matrix, D represents the direct energy consumption coefficient matrix, and H represents the electricity consumption of residents; can be directly obtained from an energy input-output table; d represents a direct energy consumption coefficient matrix, obtained by
Figure RE-GDA0003466969070000041
ComputingAnd combining to obtain;
transformation of equations (12) and (13) yields equation (14):
E＝D(I-A)-1F+H-----(14)
in the formula (14), (I-A)-1The matrix system is a famous Lyontigo inverse matrix B, and the matrix system discloses the complicated economic association relation among all departments in the national economic system;
setting:
the total population at the end of the year is P;
the average human consumption level is L;
the final demand structure matrix R represents the proportion of the consumption of each department in the final consumption total amount, and is an n multiplied by 1 column matrix;
then, in formula (14), F ═ PRL, formula (15) can be obtained:
E＝PDBRL+H-----(15)
power consumption increment Δ E from the base year to the target year:
ΔE＝Et-E0＝(PtDtBtRtLt-P0D0B0R0L0)+(Ht-H0) ＝(PDBRL+H)t-(PDBRL+H)0-----(16)
the power consumption increment Delta E from the basic year to the target year is composed of six variation factors, namely Delta Ep、ΔED、ΔEB、ΔER、ΔEL、ΔEHWherein: delta Ep、ΔED、ΔEB、ΔER、ΔEL、ΔEHRespectively representing the variation of population, direct power consumption coefficient, economic structure, final demand structure, average human consumption level and direct power consumption level of resident life; having formula (17): Δ E ═ H (PDBRL +)t-(PDBRL+H)0
＝ΔEP+ΔED+ΔEB+ΔER+ΔEL+ΔEH----(17)
And (3) calculating the formula (17) by adopting a two-stage decomposition method in the SDA structure decomposition to obtain a calculation formula of each variable factor:
Figure RE-GDA0003466969070000051
Figure RE-GDA0003466969070000052
Figure RE-GDA0003466969070000053
Figure RE-GDA0003466969070000054
Figure RE-GDA0003466969070000055
ΔEH＝Ht-H0-----(23)
wherein Δ EpIs the influence of population change on the power demand, negative values indicate the population outflow in the target area; delta EDThe change of the direct energy consumption coefficient reflects the energy change condition required by the social production unit product, and the change may be the reduction of unit energy consumption brought by scientific and technological progress, the increase of the unit output energy consumption of the target area is positively explained, and the reduction of the unit output energy consumption of the target area is negatively explained; (ii) a Delta EBThe economic structure coefficient reflects the change situation of the flow direction of the produced products among all the departments in the direct consumption coefficient matrix, reflects the flow rule of the produced products of all the departments in the economic structure, and the relationship between the numerical value and the positive and negative values reflects the situation that the products are put into high energy consumption departments or low energy consumption departments in the middle of the society; delta ERIs a power consumption structure and represents the power consumption of each departmentThe change of the proportion reflects the change of the proportion of consumed electric energy in the production of departments, and laterally reflects the change of the transformation and the upgrade of industries of each department and the change of unit output energy consumption; delta ELThe average human consumption level reflects the influence of the resident consumption level on the overall social economic activity, the positive value indicates the pushing effect of the rise of the resident consumption level on the increase of the power consumption, and the negative value indicates the reverse pulling effect of the resident consumption on the power consumption; delta EHThe scale is the residential power consumption level, which explains the change of the residential power consumption situation, the scale of positive increase of the residential power consumption and the scale of negative reduction of the residential power consumption level.
In the invention, the iterative process of the intermediate demand sum and the intermediate investment sum of each department is as follows:
Figure RE-GDA0003466969070000056
Figure RE-GDA0003466969070000061
Figure RE-GDA0003466969070000062
……
Figure RE-GDA0003466969070000063
Figure RE-GDA0003466969070000064
Figure RE-GDA0003466969070000065
in the invention, the value of W in the WLS method is determined according to the following principle:
carrying out data discrimination on the reference year input-output table by combining the statistical yearbook and the industry report;
the first type is determined data, and a new table can be directly introduced without modification; the value is positive infinity or a great value to ensure that the data updating process is kept unchanged;
the second type is uncertain data, and although the numerical value is uncertain, the reasonable value interval of the updated data is known; assume that it takes on a value space of
Figure RE-GDA0003466969070000066
Then order
Figure RE-GDA0003466969070000067
Make it and
Figure RE-GDA0003466969070000068
the lengths of the physical prediction value intervals are in inverse proportion,
Figure RE-GDA0003466969070000069
expressed as a known target year direct consumption coefficient;
the third type is unknown data, the numerical value and the value space are not clear, and the numerical value and the value space are directly set to be 1;
when solving the minimum value of the formula (8), the invention sets the non-negative constraint condition as follows:
Figure RE-GDA0003466969070000071
Figure RE-GDA0003466969070000072
Figure RE-GDA0003466969070000073
the non-negative constraint condition is set for seeking flow summation balance and guarantee formula meaning.
In the present invention, a zero value constraint condition is set when solving the minimum value of equation (8) to better cope with the complicated situation that affects the coefficient aggregation constraint condition:
Figure RE-GDA0003466969070000074
the zero value constraint condition causes the update coefficient to be zero when the initial coefficient is zero; when the initial coefficient is nonzero, the updating coefficient is also nonzero, so that the target input-output table can effectively inherit the zero value structure of the reference table, and the quality of the target table is better ensured; this results in a direct cost coefficient matrix for the WLS method.
The invention adopts the thinking of extrapolation, decomposition and calculation. Factors influencing power consumption (including economic growth factors) are decomposed, and the effect of each factor is calculated to quantify the influence degree of different factors on power consumption. The method can clearly show all factors influencing power consumption, quantize the influence degree of the factors, enable the influence path of the target variable to be more visual, be more beneficial to analyzing the problems of total amount, structural change, influence degree and the like in the economic system, and enable the analysis to be more systematized and clearer.
RAS can better control the similarity between the coefficient matrix of the target table and the reference table, and WLS rule can better realize the approaching precision between the transaction flow of the target table and the reference table. Before the updating work is started, if the similarity between the target table coefficient matrix and the reference table is high, the WLS method can be directly adopted, and if the target table transaction flow is close to the reference table, the RAS method is used. However, in actual calculation, the balance of the target input-output table cannot be accurately grasped, so that the applicability of the RAS method and the WLS method is difficult to directly judge. Therefore, the invention comprehensively utilizes the advantages of the two methods and ensures that the updating result is more accurate.
In the input-output table, the first quadrant represents the direct consumption coefficient of the society, each row represents the middle input of the ith department, each column represents the middle demand of the jth department, and each element in the first quadrant reflects the social societyIn three major industries, the first industry agriculture 1 department, the second industry 25 department, the third industry service industry 14 department, the input-output relationship of each department, namely aijRepresents the flow condition of the ith department to the jth department in the economic activity of the year, and directly reflects the production structure of social economy. The row summation and the column summation of each department in the first quadrant can obtain the required intermediate demand summation and the required intermediate input summation of each department in the current year, and then the total input and the total output of the row and the column to the society are calculated, so that the occupation ratio of the department to the total input and the output of the society can be obtained, and the structural status of the department in the society is reflected.
ΔEp、ΔED、ΔEB、ΔER、ΔEL、ΔEHThe change of population, direct power consumption coefficient, economic structure, final demand level of per capita and direct consumption of residents are respectively represented. The result feeds back the change of macroscopic economic indexes in social production and life. Wherein Δ EpIs the influence of population change on the power demand, negative values indicate the population outflow in the target area; delta EDThe change of the direct energy consumption coefficient reflects the energy change condition required by the social production unit product, and the change may be the reduction of unit energy consumption brought by scientific and technological progress, the increase of the unit output energy consumption of the target area is positively explained, and the reduction of the unit output energy consumption of the target area is negatively explained; (ii) a Delta EBThe economic structure coefficient reflects the change situation of the flow direction of the produced products among all the departments in the direct consumption coefficient matrix, reflects the flow rule of the produced products of all the departments in the economic structure, and the relationship between the numerical value and the positive and negative values reflects the situation that the products are put into high energy consumption departments or low energy consumption departments in the middle of the society; delta ERIs a power consumption structure, represents the change of power consumption proportion of each department, and reflects the power energy proportion consumed by the department in productionThe serious change reflects the transformation and the upgrade of each department industry and the change of unit output energy consumption laterally; delta ELThe average human consumption level reflects the influence of the resident consumption level on the overall social economic activity, the positive value indicates the pushing effect of the rise of the resident consumption level on the increase of the power consumption, and the negative value indicates the reverse pulling effect of the resident consumption on the power consumption; delta EHThe scale is the residential power consumption level, which explains the change of the residential power consumption situation, the scale of positive increase of the residential power consumption and the scale of negative reduction of the residential power consumption level.
The invention provides a combined use RAS modification method based on electric energy consumption and a WLS method in an optimization updating method. The method is different from the traditional RAS method and a correction method taking the national production total value as the caliber, and the improvement point of the method is that the power consumption index is introduced as the basis of externally deriving the direct consumption matrix. The production and the use of the electric power have two characteristics, namely, the timeliness of the production, the transmission and the use of the electric power; secondly, the electric energy can not be stored in a large scale. The two characteristics ensure the real-time performance of power production and consumption and the consistency with national economic activities. Therefore, the direct consumption coefficient of the target period can be obtained through analysis by an iterative operation method. Meanwhile, the method uses a weight least square method (WLS) in an improved optimization updating method to carry out weight weighted summation on the results of the two methods to obtain an optimized input-output table of the target year, and uses an SDA decomposition method to analyze the economic structure change driving factors. The method combines the RAS correction method and the WLS method, and obtains the most important direct consumption coefficient matrix in the input-output table through the weighted RAS-WLS method. The method combines the advantages of the RAS method and the WLS method, obtains an input-output table which is more similar to the real situation and has more accurate flow direction, not only keeps the consistency of the updated input-output table and the increase of power consumption, but also minimizes the specific change of the flow direction and the value difference of the basic period. Meanwhile, based on the method, the pulling factors of power consumption growth and economic structure change are measured from the power consumption perspective by using the SDA structure decomposition model.
The invention provides a method for reflecting power consumption change factors for a power grid company from the perspective of power consumption, so that power energy can be reasonably distributed to enter each production department and each resident department, and the actual total power generation amount is effectively planned. Meanwhile, the invention provides an index for reflecting the upgrading of social production technology and the degree of structure transformation for governments, and the change of the energy consumption produced by social units and the change of energy consumed by production of all social departments from the basic period to the target period can be intuitively obtained through the second and third decomposition factors.
Drawings
Fig. 1 is a diagram showing the analysis results of the change of the economic structure in the visual sense of power consumption.
Detailed Description
Example 1
According to the method, the direct consumption coefficient of the input-output table of the Hunan province in 2020 is deduced by using the input-output table of the Hunan province in 2017, the power consumption data of the branch industries of the Hunan province in 2017 and the macro economic data of the Hunan province in 2020, and the economic structure analysis in 2020 is completed.
First step of
Table 1 is a 2017 energy input-output table, as shown in tables 1-1 to 1-6 below.
Direct consumption matrix using row variables A001-A019 (intermediate inputs) and column variables A001-A019 (intermediate uses) in Table 1
Figure RE-GDA0003466969070000091
Divided by the row variable XX (total throw)
Figure RE-GDA0003466969070000092
) To obtain the direct consumption coefficient
Figure RE-GDA0003466969070000093
Form a direct consumption coefficient matrix A in 2017 years (basic years)0(19 × 19), see table 2.
Using row variables EP17 and EP20 in table 1 to represent the power consumption of departments in 2017 and 2020, respectively, a target year direct consumption coefficient matrix a is obtained by calculating table 2 with equation (2)1-1See table 3.
Direct consumption matrix using row variables A001-A019 (intermediate inputs) and column variables A001-A019 (intermediate uses) in Table 1
Figure RE-GDA0003466969070000101
Adding with the row variable VA20 column direction of the industry added value to obtain the initial value x of the total investment of the target yearj(1*19)。
Using tables 2 and 3 and the initial value x of the total investment of the target yearjPerforming iterative operation according to the formula (4) to calculate ri (k)And sj (k)Diagonal matrices R (19 × 19) and S (19 × 19) are generated according to equation (5), and a direct consumption matrix a in 2020 under the RAS method is obtained according to equation (6)1-2(19 × 19), see table 4.
Table 5 shows the information weight matrix (19 × 19), whose value principle follows the known degree of information. In this example, the determined value is only the maximum value taken by the power department and is recorded as 1E + 08; taking 1 for the value of the uncertain and uncertain range; since the input and output in 2022 years are not generated and calculated, no type with reasonable value range exists. Using the data in tables 2 and 5, the direct consumption matrix a in 2020 (target year) under the WLS method is obtained by calculation using equation (8)2(19 × 19), see table 6.
The elements in the table 4 and the table 6 are added and averaged to obtain the direct consumption matrix A under the RAS-WLS method3(19 × 19), see table 7.
The direct consumption coefficient D of the power of each department is obtained by dividing the power consumption EP20 of each department in 2020 in table 7, and the final demand portion in table 1 is finally obtained using the total column variable FA as F. Each department power consumption amount EP17, EP20 is divided by the total power consumption amount (E17 is 15815138, E20 is 19292757, and the unit is ten thousand kilowatt hours) to obtain a vector R (19 × 1). Consumption of electricity by residents in 2017 and 2020 is the intersection of row variables EP17 and EP20 and column variables FA001 and FA002 in Table 1. Other scalar specific values are: p17 ═ 7296.26, P20 ═ 7295.58 (population/ten thousand); b is the well-known inverse of Liontigv matrix according to Table 7 and formula (I-A)-1Calculating to obtain a table 8; c17-23162.6, C20-26796.4 (consumption by the residents per yuan).
The description of FIG. 1 can be obtained using SDA decomposition.
As can be seen from fig. 1:
ΔEPis-0.01%, which indicates that the population of Hunan province is in a flowing state from the basic period to the target period and drives the negative increase of power consumption.
ΔEDThe total content of the waste water is 14.97 percent, which indicates that the energy consumption required by the product of the production unit in Hunan province is increased, and the power consumption is increased by 14.97 percent.
ΔEBA value of-19.33% indicates that the flow of products produced in the province of Hunan is moving from the high energy consuming industry to the low energy consuming industry, and this change brings about a power consumption change of-19.33%.
ΔERThe power consumption is-7.95%, which shows the change of the power consumption proportion of each department in the province of Hunan province, and the structural change brings the change of the power consumption to-7.95%.
ΔEL14.53 percent, which is the change brought by the average consumption level of residents, and reflects that the average consumption level of residents in Hunan province pulls the rise of power consumption to 14.53 percent.
ΔEHThe power consumption is 7.81 percent, is the change of the resident power consumption, and reflects the pulling effect of the change of the resident power consumption in Hunan province on the whole social power consumption to be 7.81 percent.
Tables 1 to 1: 2017 energy input-output table
Figure RE-GDA0003466969070000111
Tables 1 to 2: 2017 energy input-output table
Figure RE-GDA0003466969070000121
Tables 1 to 3: 2017 energy input-output table
Figure RE-GDA0003466969070000122
Tables 1 to 4: 2017 energy input-output table
Figure RE-GDA0003466969070000131
Tables 1 to 5: 2017 energy input-output table
Figure RE-GDA0003466969070000132
Tables 1 to 6: 2017 energy input-output table
Figure RE-GDA0003466969070000141
Table 2-1: 2017 direct consumption coefficient matrix
Figure RE-GDA0003466969070000142
Tables 2 to 2: 2017 direct consumption coefficient matrix
Figure RE-GDA0003466969070000151
Table 3-1: direct consumption coefficient matrix including power consumption in 2020
Figure RE-GDA0003466969070000152
Tables 3-2: direct consumption coefficient matrix including power consumption in 2020
Figure RE-GDA0003466969070000161
Table 4-1: RAS-2020 direct consumption matrix
Figure RE-GDA0003466969070000171
Tables 4-2: RAS-2020 direct consumption matrix
Figure RE-GDA0003466969070000172
Tables 4 to 3: RAS-2020 direct consumption matrix
Figure RE-GDA0003466969070000173
Table 5-1: weight matrix
Figure RE-GDA0003466969070000181
Table 6-1: WLS-2020 direct consumption matrix
Figure RE-GDA0003466969070000182
Table 6-2: WLS-2020 direct consumption matrix
Figure RE-GDA0003466969070000183
Tables 6 to 3: WLS-2020 direct consumption matrix
Figure RE-GDA0003466969070000191
Table 7-1: RAS-WLS-2020 direct consumption matrix
Figure RE-GDA0003466969070000192
Table 7-2: RAS-WLS-2020 direct consumption matrix
Figure RE-GDA0003466969070000193
Tables 7 to 3: RAS-WLS-2020 direct consumption matrix
Figure RE-GDA0003466969070000201
Table 8-1: lyontigo inverse matrix
Figure RE-GDA0003466969070000202
Table 8-2: lyontigo inverse matrix
Figure RE-GDA0003466969070000211

Claims (5)

1. A method of predicting economic structure changes based on power consumption, comprising the steps of:
the first step is as follows: according to the data of the direct input and consumption matrix in the input and output table of the basic year, the basic year direct consumption flow from the department i to the department j is obtained
Figure FDA0003377278110000011
Calculating the direct consumption coefficient from the basic year i department to the j department according to the formula (1):
Figure FDA0003377278110000012
wherein the content of the first and second substances,
Figure FDA0003377278110000013
represents the total output of the jth department of the base year; according to the direct consumption coefficient from the i department to the j department in the basic year
Figure FDA0003377278110000014
And j department base year and target year power consumption data
Figure FDA0003377278110000015
And
Figure FDA0003377278110000016
calculating a target annual direct consumption coefficient from the department i to the department j, which contains power consumption, according to the formula (2):
Figure FDA0003377278110000017
will consume the flow directly by each department in the basic year
Figure FDA0003377278110000018
Adding up the sum and adding up the value V of the direct consumption flow of each department in the target yearjAdding to obtain the total initial value X of each department in the target yearj：
Figure FDA0003377278110000019
Setting:
the vector of the total target period value is X ═ X1,x2,...,xn)T，
The final required product vector Y of the target period is (Y)1,y2,...,yn)T，
The column sum vector used in the target period is U ═ U (U)1,u2,...,un)T，
The target intermediate input line and vector are V ═ V (V)1,v2,...,vn)T；
According to the input-output table, the intermediate demand sum and the intermediate input sum of each department can be obtained according to the RAS method:
Figure FDA00033772781100000110
in the formula, n represents the number of departments; performing iterative operation on the formula (4), wherein k represents iterative period number until ri (k)And
Figure FDA0003377278110000021
equals 1, resulting in k periods of n r per periodi (k)And
Figure FDA0003377278110000022
according to the RAS method, matrices R and S of manufacturing utility and alternative utility are obtained,
Figure FDA0003377278110000023
according to RAS method, directly obtaining direct consumption coefficient matrix A of target year1：
A1＝R×A0×S-----(6)
In the formula (6), A0Is a direct consumption coefficient matrix of the base year, A1Is a direct consumption coefficient matrix for the target year;
the second step is that: solving according to a formula (7) according to a least square method:
Figure FDA0003377278110000024
in the formula (7), the reaction mixture is,
Figure FDA0003377278110000025
is the sum of squares of the direct consumption coefficient differences from the base year to the target year;
and (3) weighting the direct consumption coefficients of all departments in the input-output table, and optimizing the formula (7) to obtain a formula (8):
Figure FDA0003377278110000026
solving the minimum value of the formula (8) to obtain n x n
Figure FDA0003377278110000027
Form a direct consumption coefficient matrix A of the target year2：
Figure FDA0003377278110000028
The third step: a is to be1And A2Average summation is carried out to obtain a direct consumption coefficient matrix A under the RAS-WLS method3；
The fourth step: direct consumption coefficient matrix A according to target year3And electric power consumption data and macroscopic economic statistical data of the target year, and compiling an energy input-output table of the target year;
the fifth step: the target annual energy input-output table obtained in the fourth step has the following formulas (10) and (11):
Figure FDA0003377278110000029
Figure FDA00033772781100000210
wherein the direct power consumption coefficient djThe amount of electrical energy required to be consumed directly by the ith department in producing a unit of product is expressed as
Figure FDA0003377278110000031
1 xn djForming an electric power direct energy consumption coefficient matrix D;
rewritten in matrix form, with:
AX+F＝X ----(12)
DX+H＝E ----(13)
in equations (12) and (13), A, X is defined as above, and matrix a multiplied by matrix X represents the intermediate demand sum. F represents the final demand matrix, H represents the electricity consumption of residents, and the electricity consumption can be directly obtained from an energy input-output table; d represents a direct energy consumption coefficient matrix, passing DjCombining to obtain;
transformation of equations (12) and (13) yields equation (14):
E＝D(I-A)-1F+H ----(14)
in the formula (14), (I-A)-1Is the well-known lyon inverse matrix B;
setting:
the total population at the end of the year is P;
the average human consumption level is L;
the final demand structure matrix R represents the proportion of the consumption of each department in the final consumption total amount, and is an n multiplied by 1 column matrix;
then, in formula (14), F ═ PRL, formula (15) can be obtained:
E＝PDBRL+H ----(15)
the incremental power consumption Δ E from the baseline year to the target year is as follows (16):
ΔE＝Et-E0＝(PtDtBtRtLt-P0D0B0R0L0)+(Ht-H0)----(16)
in the formula (16), the superscript 0 represents the base year, and t represents the target year;
setting:
the incremental power consumption Δ E from the base year to the target year is made up of six changing factors, i.e., Δ Ep、ΔED、ΔEB、ΔER、ΔEL、ΔEHWherein: delta Ep、ΔED、ΔEB、ΔER、ΔEL、ΔEHRespectively representing the variation of population, direct power consumption coefficient, economic structure, final demand structure, average human consumption level and direct power consumption level of resident life; having formula (17):
ΔE＝(PDBRL+H)t-(PDBRL+H)0
＝ΔEP+ΔED+ΔEB+ΔER+ΔEL+ΔEH ----(17)
and (3) calculating the formula (16) by adopting a two-stage decomposition method in the SDA structure decomposition to obtain a calculation formula of each variable factor:
Figure FDA0003377278110000041
Figure FDA0003377278110000042
Figure FDA0003377278110000043
Figure FDA0003377278110000044
Figure FDA0003377278110000045
ΔEH＝Ht-H0 ----(23)
when:
ΔEppositive, indicating an increase in population in the target area; negative values indicate a target region population outflow;
ΔEDthe energy consumption per unit output of the target area is increased when the value is a positive value, and the energy consumption per unit output of the target area is reduced when the value is a negative value;
ΔEBif the product flow is positive, the product gradually flows to a high energy consumption department, and if the product flow is negative, the product flow is positive and flows to a low energy consumption department;
ΔERpositive values indicate increased energy consumption per output, negative valuesThe unit output energy consumption is reduced;
ΔELthe positive value indicates that the rising of the consumption level of residents has a positive pushing effect on the increase of the power consumption, and the negative value indicates that the resident consumption has a reverse pulling effect on the power consumption;
ΔEHthe positive value indicates a positive increase in the electricity consumption of the residents, and the negative value indicates a decrease in the electricity level of the residents.
2. The method of claim 1, wherein the method comprises the steps of: the iterative process of the intermediate demand sum and the intermediate investment sum of each department is as follows:
Figure FDA0003377278110000046
Figure FDA0003377278110000051
3. a method of predicting economic structural changes based on electricity consumption according to claim 1 or 2, wherein: the value of W in the WLS method is determined according to the following principle:
carrying out data discrimination on the input-output table of the reference year;
the first type is determined data, and a new table is introduced directly without modification; the value is positive infinity or a great value to ensure that the data updating process is kept unchanged;
the second category is uncertain data, which is assumed to have a space of values of
Figure FDA0003377278110000052
Then order
Figure FDA0003377278110000053
Make it and
Figure FDA0003377278110000054
the lengths of the physical prediction value intervals are in inverse proportion,
Figure FDA0003377278110000055
expressed as a known target year direct consumption coefficient;
the third type is unknown data, the numerical value and the value space are not clear, and the numerical value and the value space are directly set to be 1.
4. A method of predicting economic structural changes based on electricity consumption according to claim 3, wherein: when solving the minimum value of the formula (8), the non-negative constraint condition is set as follows:
Figure FDA0003377278110000056
Figure FDA0003377278110000061
Figure FDA0003377278110000062
5. the method of claim 4 for predicting economic structural changes based on electricity consumption, wherein: when solving the minimum value of the formula (8), a zero value constraint condition is set to better cope with the complicated situation of influencing the coefficient aggregation constraint condition:
Figure FDA0003377278110000063
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