CN105913366A - Industrial electric power big data-based regional industry business climate index building method - Google Patents

Industrial electric power big data-based regional industry business climate index building method Download PDF

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CN105913366A
CN105913366A CN201610222221.2A CN201610222221A CN105913366A CN 105913366 A CN105913366 A CN 105913366A CN 201610222221 A CN201610222221 A CN 201610222221A CN 105913366 A CN105913366 A CN 105913366A
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荣秀婷
叶彬
葛斐
李周
王宝
杨敏
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Economic and Technological Research Institute of State Grid Anhui Electric Power Co Ltd
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Abstract

The invention relates to an industrial electric power big data-based regional industry business climate index building method which comprises the following steps: an industrial sector electric power big data database is built; regional industrial added value of a historical sample range, electricity consumption of all industrial sectors and monthly data of a number of economic indicators are obtained and year-on-year growth rates thereof are calculated; a leading indicator of a year-on-year growth rate of the regional industrial added value is chosen; based on the leading indicator, a regional industry business climate index is built. According to the regional industry business climate index building method, information relevant to economic situation change is extracted from industrial sector electric power big data, consideration is given to time-lag effect between industrial sector power consumption indicators and the regional industrial added value, the industry business climate index that can be used for predicting the regional industrial added value is built, instant monitoring of regional economy can be realized, and basis for decision making can be provided for enterprises and government sectors to take corresponding measures.

Description

A kind of regional industry consumer confidence index construction method based on the big data of industrial electrical
Technical field
The present invention relates to regional industry value added electric powder prediction, be specifically related to a kind of big based on industrial electrical The regional industry consumer confidence index construction method of data.
Background technology
Application one side and macroeconomy, people's lives, social security, the road traffic etc. of the big data of electric power Information fusion, promotes socio-economic development;On the other hand, it is power industry or enterprises, multi-disciplinary, Across unit, interdepartmental data fusion, it is possible to help to promote industry, enterprise management level and economic benefit. The key technology of the big data research of electric power and application, including high-performance calculation, data mining, statistical analysis, Data visualization etc..Wherein in economic research field, data mining and statistical analysis technique can be passed through, sieve Select and extract the electric power index relevant to Regional Economic Development, building industry prosperity index, further investigate electric power With the mutual relation of industrial economy, hold operation trend and the variation characteristic of national economy further.
Summary of the invention
It is an object of the invention to provide a kind of regional industry consumer confidence index structure based on the big data of industrial electrical Construction method, to realize estimation range industrial added value and externally to issue the function of industrial added value variation tendency.
For achieving the above object, present invention employs techniques below scheme: a kind of based on the big data of industrial electrical Regional industry consumer confidence index construction method, comprise the following steps:
(1) obtain industrial trade monthly TV university data from Utilities Electric Co., and obtain region and two, the whole nation The economic indicator of yardstick, sets up industrial trade electricity consumption and economic indicator data base;
(2) historical sample interval regional industry value added, every industrial trade power consumption and the whole nation are obtained With the monthly data of some economic indicators in region, and calculate its monthly year-on-year growth rate;
(3) filter out the economic leading indicators of regional industry value added year-on-year growth rate, and determine every elder generation The advanced issue of row index;
(4) based on leading indicators, the method for composite index number is used to build regional industry consumer confidence index.
Described regional industry consumer confidence index construction methods based on the big data of industrial electrical, in step (2), The computing formula of described monthly year-on-year growth rate is:
Monthly year-on-year growth rate=(data preceding year data in this month in this month in the current year) * 2/ (this year Of that month data+preceding year the data in this month of degree).
Described regional industry consumer confidence index construction methods based on the big data of industrial electrical, in step (3), Time difference dependency method is used to filter out the economic leading indicators of regional industry value added year-on-year growth rate, this time difference Dependency method realizes by comparing time difference correlation coefficient, and described time difference Calculation of correlation factor formula is as follows:
If y={y1,y2,…,ynIndex on the basis of }, x={x1,x2,…,xnFor being chosen index, then:
r ( y t , x t - l ) = Σ t = 1 n l ( x t - l - x ‾ ) ( y t - y ‾ ) Σ t = 1 n l ( x t - l - x ‾ ) 2 Σ t = 1 n l ( y t - y ‾ ) 2 , l = 0 , ± 1 , ± 2 , ... , ± L
Wherein, r (yt,xt-l) representing time difference correlation coefficient, l represents the time difference or postpones number, the table when l takes negative Showing advanced, represent delayed when taking positive number, L represents maximum delay number, and t represents issue and t≤n, nlRepresent Data even up after data amount check, xt-lRepresent current criteria,Represent the meansigma methods being chosen index, ytTable Show reference index during t phase,Represent the average of reference index.
Described regional industry consumer confidence index construction methods based on the big data of industrial electrical, step (4) is wrapped Include following steps:
(A) each leading indicators of reference index and the symmetrical rate of change of coincidence indicator are calculated:
C i j ( t ) = [ x i j ( t ) - x i j ( t - 1 ) x i j ( t ) + x i j ( 1 - 1 ) ] × 200
Wherein, CijT () represents the symmetrical rate of change of certain index, xijT () represents the increasing on year-on-year basis of this index current period Long rate, xij(t-1) year-on-year growth rate that this index is last, i=1,2,3, L, k, index in expression group are represented Sequence number, j=1,2 represent leading indicators and coincidence indicator, t=2,3..., n respectively;
(B) leading indicators and the standardization average rate of change of this two classes index of coincidence indicator are calculated:
(B1) calculating group internal standardization factors Aij:
A i j = Σ t = 2 n | C i j ( t ) | n - 1 , t = 2 , 3 , L , n ;
(B2) A is usedijBy CijT () standardization, obtains standardization rate of change Sij(t):
S i j ( t ) = C i j ( t ) A i j , t = 2 , 3 , L , n ;
(B3) each index group internal standardization average rate of change R is calculatedj(t):
R j ( t ) = Σ t = 2 n S i j ( t ) × W i j / Σ t = 2 n W i j , i = 1 , 2 , 3 , L , k ; j = 1 , 2 ; t = 2 , 3 , L , n ;
Wherein, WijIt is the flexible strategy of the i-th index of jth group, in this model, the flexible strategy, i.e. W such as employsij=1;
(B4) normalization factor F between calculating groupj:
F j = [ Σ t = 2 n | R j ( t ) | / n - 1 ] / [ Σ t = 2 n | R 2 ( t ) | / n - 1 ] , j = 1 , 2 ; t = 2 , 3 , L , n ;
Wherein, sync index F2=1;
(B5) standardization average rate of change V between the group of leading indicators is calculatedj(t):
Vj(t)=Rj(t)/Fj, t=2,3, L, n;J=1,2;
(C) leading composite index number CI is calculated1(t):
CI 1 ( t ) = ( I 1 ( t ) / I 1 ‾ ) × 100
Wherein,And I1(1)=100,It is I1(t) base The meansigma methods in quasi-time.
As shown from the above technical solution, the present invention by extensive collecting zone industrial trade power consumption, the whole nation and Two, region yardstick economic indicator, goes out the letter relevant with economic situation change from the big extracting data of industrial electrical Breath, and consider the time-lag effect between industrial trade index on power consumption and regional industry value added, build available Industry prosperity index in estimation range industrial added value tendency, it is achieved that the at-once monitor to regional economy, Counter-measure is taked to provide decision-making foundation for business and government department.
Accompanying drawing explanation
Fig. 1 is the method flow diagram of the present invention;
Tu2Shi Anhui Province industrial added value speedup and the comparison diagram of leading consumer confidence index thereof.
Detailed description of the invention
Building process and practice using Anhui Province's industry prosperity index are entered as specific embodiment below The one step explanation present invention:
As it is shown in figure 1, a kind of regional industry consumer confidence index construction method based on the big data of industrial electrical, bag Include following steps:
S1, obtain industrial trade monthly TV university data from Utilities Electric Co., and from region and national two yardsticks Choose some economic indicators, set up industrial trade electricity consumption and economic indicator data base:
Obtain Anhui Province branch trade index on power consumption from Utilities Electric Co., totally 70, including national economy each Industry and industry-specific power consumption, national economy is divided into the primary industry, secondary industry and the tertiary industry, the Two industries are divided into again mining industry, manufacturing industry, electric power, heating power, combustion gas and water production and supply industry and build Building industry, the trade division of secondary industry is shown in Table 1.
Table 1
Choosing 35 economic indicators altogether from Anhui Province and the whole nation two each and every one yardsticks, wherein, Anhui Province's economy refers to Mark type includes: 1) regional economy overall objective;2) finance class;3) investment type: investment in fixed assets Total value, Investment by Various Sectors complete volume;4) real estate investment volume;5) industrial added value;6) Consumption of resident: state revenue, state financial spending;7) foreign trade;8) industrial goods yield; 9) industrial added value;10) whole society's total volume of retail sales of consumer goods.Whole nation pointer type includes: 1) currency supplies Ying Liang;2) international trade and international investment;3) the branch trade value added in Shanghai City, developed area.
S2, acquisition historical sample interval regional industry value added and electricity consumption, the monthly data of economic indicator, and Zoning industrial added value and the monthly year-on-year growth rate of each economic target:
Certain index current year of that month year-on-year growth rate=(this month in the current year, data preceding year was of that month Data) * 2/ (this month in the current year data+preceding year data in this month).
Interval with in January, 2008~in July, 2014 for historical sample, obtain Anhui Province's industrial added value with 105 power consumptions and the monthly data of economic indicator, and zoning industrial added value and each economic target Monthly year-on-year growth rate.In order to reduce the impact of random factor and specific factor, the present invention calculates and increases on year-on-year basis Long rate is through processing, and i.e. this month in the current year, data were constant, but comparison other is before and after this month the previous year three months The meansigma methods of part.
S3, filter out the leading indicators of regional industry value added year-on-year growth rate, and determine every leading indicators Advanced issue, this step uses time difference dependency method to filter out the warp of regional industry value added year-on-year growth rate Ji leading indicators:
If y={y1,y2,…,ynIndex on the basis of }, x={x1,x2,…,xnFor being chosen index, then:
r ( y t , x t - l ) = Σ t = 1 n l ( x t - l - x ‾ ) ( y t - y ‾ ) Σ t - 1 n l ( x t - l - x ‾ ) 2 Σ t - 1 n l ( y t - y ‾ ) 2 , l = 0 , ± 1 , ± 2 , ... , ± L
Wherein, r (yt,xt-l) representing time difference correlation coefficient, l represents the time difference or postpones number, the table when l takes negative Showing advanced, represent delayed when taking positive number, L represents maximum delay number, and t represents issue and t≤n, nlRepresent Data even up after data amount check, xt-lRepresent current criteria,Represent the meansigma methods being chosen index, ytTable Show reference index during t phase,Represent the average of reference index.
To Anhui Province's industrial added value year-on-year growth rate and 105 industrial trade power consumptions and economic indicator Year-on-year growth rate carries out time-difference correlation analysis, filters out the economy of Anhui Province's industrial added value year-on-year growth rate Leading indicators, result and parameter thereof (note: as without mark especially, index is the most all Anhui Province as shown in table 2 Index of correlation).
Note: in code, A represents power consumption index, and B represents economic indicator, and C represents investment index number.
Table 2
Time difference correlation analysis utilizes correlation coefficient checking economic time series leading, consistent or lagged relationship A kind of common method.The computational methods of time difference correlation coefficient are with important can reflecting sensitively currently Index on the basis of the economic indicator of economic activity, if then making to be chosen index advanced or delayed dry spell, calculates Their correlation coefficient.When selecting leading indicators, the general time difference correlations calculating several different delay numbers Coefficient, then compares, and maximum of which time difference correlation coefficient is considered to reflect alternative index and benchmark The time difference dependency relation of index, the corresponding number that postpones represents the advanced or lag period.
The implementation case is index on the basis of Anhui Province's industrial added value year-on-year growth rate, with trade power consumption index It is alternative index with economic indicator, calculates reference index and each alternative index respectively from-12 rank to+12 The time difference correlation coefficient on rank, and select correlation coefficient and the time lag exponent number of correspondence thereof of maximum, thus obtain institute There is the alternative index optimal lag period relative to reference index.If the optimal lag period of certain alternative index is less than 0, Then this alternative index is screened for economic leading indicators.
S4, based on leading indicators, use the method for composite index number to build the process of regional industry consumer confidence index such as Under:
The first step: in order to eliminate the impact of radix, first calculates each leading indicators of reference index with consistent The symmetrical rate of change C of indexijT (), the i.e. average with current period and last issue try to achieve rate of change for radix.
(A) each leading indicators of reference index and the symmetrical rate of change of coincidence indicator are calculated:
C i j ( t ) = [ x i j ( t ) - x i j ( t - 1 ) x i j ( t ) + x i j ( 1 - 1 ) ] × 200
Wherein, CijT () represents the symmetrical rate of change of certain index, xijT () represents the increasing on year-on-year basis of this index current period Long rate, xij(t-1) year-on-year growth rate that this index is last, i=1,2,3, L, k, index in expression group are represented Sequence number, j=1,2 represent leading indicators and coincidence indicator, t=2,3..., n respectively;
Work as xijWhen () is less than or equal to zero t, above formula simplifies:
Cij(t)=powij(t)-powij(t-1), t=2,3, L, n
Second step: ask leading indicators and the standardization average rate of change of this two classes index of coincidence indicator.
1. group internal standardization factors A is soughtij:
A i j = Σ t = 2 n | C i j ( t ) | n - 1 , t = 2 , 3 , L , n
2. A is usedijBy CijT () standardization, obtains standardization rate of change Sij(t):
S i j ( t ) = C i j ( t ) A i j , t = 2 , 3 , L , n
3. each index group internal standardization average rate of change R is soughtj(t):
R j ( t ) = Σ t = 2 n S i j ( t ) × W i j / Σ t = 2 n W i j , i = 1 , 2 , 3 , L , k ; j = 1 , 2 ; t = 2 , 3 , L , n
Wherein, WijIt is the flexible strategy of the i-th index of jth group, in this model, the flexible strategy, i.e. W such as employsij=1.
4. normalization factor F between group is soughtj:
F j = [ Σ t = 2 n | R j ( t ) | / n - 1 ] / [ Σ t = 2 n | R 2 ( t ) | / n - 1 ] , j = 1 , 2 ; t = 2 , 3 , L , n
Wherein, sync index F2=1.
5. standardization average rate of change V between the group of leading indicators is sought1(t):
V1(t)=R1(t)/F1, t=2,3, L, n
3rd step: seek leading composite index number CI1(t)。
CI 1 ( t ) = ( I 1 ( t ) / I 1 ‾ ) × 100
Wherein,And I1(1)=100,It is I1(t) base The meansigma methods in quasi-time.This leading composite index number CI1T () is industry prosperity index.
The leading indicators of the Anhui Province's industrial added value speedup filtered out is synthesized Anhui Province's industry prosperity refer to Number, can be described industrial economy speedup equally, and predict its future trend by industry prosperity index.Such as Fig. 2 Shown in, the variation tendency of Anhui Province's industrial added value speedup consumer confidence index leading with it is the most identical, and work is described Industry value added speedup consumer confidence index can directly reflect the variation tendency of industrial added value speedup.
The present invention based on industrial added value to a certain extent by each industrial trade power consumption and domestic economy The impact of situation, and there is certain time-lag effect, thus build regional industry by screening leading indicators Consumer confidence index, thus the tendency of estimation range industrial added value, have the most ageing and degree of accuracy.
Embodiment described above is only to be described the preferred embodiment of the present invention, not to this Bright scope is defined, on the premise of designing spirit without departing from the present invention, and those of ordinary skill in the art The various deformation making technical scheme and improvement, all should fall into claims of the present invention and determine Protection domain in.

Claims (5)

1. a regional industry consumer confidence index construction method based on the big data of industrial electrical, it is characterised in that Comprise the following steps:
(1) obtain industrial trade monthly TV university data from Utilities Electric Co., and obtain region and two, the whole nation The economic indicator of yardstick, sets up industrial trade electricity consumption and economic indicator data base;
(2) historical sample interval regional industry value added, every industrial trade power consumption and the whole nation are obtained With the monthly data of some economic indicators in region, and calculate its monthly year-on-year growth rate;
(3) filter out the economic leading indicators of regional industry value added year-on-year growth rate, and determine every elder generation The advanced issue of row index;
(4) based on leading indicators, the method for composite index number is used to build regional industry consumer confidence index.
Regional industry consumer confidence indexes based on the big data of industrial electrical the most according to claim 1 build Method, it is characterised in that in step (2), the computing formula of described monthly year-on-year growth rate is:
Monthly year-on-year growth rate=(data preceding year data in this month in this month in the current year) * 2/ (this year Of that month data+preceding year the data in this month of degree).
Regional industry consumer confidence indexes based on the big data of industrial electrical the most according to claim 1 build Method, it is characterised in that in step (3), uses time difference dependency method to filter out regional industry value added The economic leading indicators of year-on-year growth rate.
Regional industry consumer confidence indexes based on the big data of industrial electrical the most according to claim 3 build Method, it is characterised in that described time difference dependency method realizes by comparing time difference correlation coefficient, the described time difference Calculation of correlation factor formula is as follows:
If y={y1,y2,…,ynIndex on the basis of }, x={x1,x2,…,xnFor being chosen index, then:
r ( y t , x t - l ) = Σ t = 1 n l ( x t - l - x ‾ ) ( y t - y ‾ ) Σ t = 1 n l ( x t - l - x ‾ ) 2 Σ t = 1 n l ( y t - y ‾ ) 2 , l = 0 , ± 1 , ± 2 , ... , ± L
Wherein, r (yt,xt-l) representing time difference correlation coefficient, l represents the time difference or postpones number, the table when l takes negative Showing advanced, represent delayed when taking positive number, L represents maximum delay number, and t represents issue and t≤n, nlRepresent Data even up after data amount check, xt-lRepresent current criteria,Represent the meansigma methods being chosen index, ytTable Show reference index during t phase,Represent the average of reference index.
Regional industry consumer confidence indexes based on the big data of industrial electrical the most according to claim 1 build Method, it is characterised in that step (4) comprises the following steps:
(A) each leading indicators of reference index and the symmetrical rate of change of coincidence indicator are calculated:
C i j ( t ) = [ x i j ( t ) - x i j ( t - 1 ) x i j ( t ) + x i j ( t - 1 ) ] × 200
Wherein, CijT () represents the symmetrical rate of change of certain index, xijT () represents the increasing on year-on-year basis of this index current period Long rate, xij(t-1) year-on-year growth rate that this index is last, i=1,2,3, L, k, index in expression group are represented Sequence number, j=1,2 represent leading indicators and coincidence indicator, t=2,3..., n respectively;
(B) leading indicators and the standardization average rate of change of this two classes index of coincidence indicator are calculated:
(B1) calculating group internal standardization factors Aij:
A i j = Σ t = 2 n | C i j ( t ) | n - 1 , t = 2 , 3 , L , n ;
(B2) A is usedijBy CijT () standardization, obtains standardization rate of change Sij(t):
S i j ( t ) = C i j ( t ) A i j , t = 2 , 3 , L , n ;
(B3) each index group internal standardization average rate of change R is calculatedj(t):
R j ( t ) = Σ t = 2 n S i j ( t ) × W i j / Σ t = 2 n W i j , i = 1 , 2 , 3 , L , k ; j = 1 , 2 ; t = 2 , 3 , L , n ;
Wherein, WijIt is the flexible strategy of the i-th index of jth group, in this model, the flexible strategy, i.e. W such as employsij=1;
(B4) normalization factor F between calculating groupj:
F j = [ Σ t = 2 n | R j ( t ) | / n - 1 ] / [ Σ t = 2 n | R 2 ( t ) | / n - 1 ] , j = 1 , 2 ; t = 2 , 3 , L , n ;
Wherein, sync index F2=1;
(B5) standardization average rate of change V between the group of leading indicators is calculated1(t):
V1(t)=R1(t)/F1, t=2,3, L, n
(C) leading composite index number CI is calculated1(t):
CI 1 ( t ) = ( I 1 ( t ) / I ‾ 1 ) × 100
Wherein,And I1(1)=100,It is I1(t) base The meansigma methods in quasi-time.
CN201610222221.2A 2016-04-08 2016-04-08 Industrial electric power big data-based regional industry business climate index building method Pending CN105913366A (en)

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CN111476030A (en) * 2020-05-08 2020-07-31 中国科学院计算机网络信息中心 Prospective factor screening method based on deep learning
CN112116265A (en) * 2020-09-25 2020-12-22 国网上海市电力公司 Industry landscape index construction method driven by electric power data
CN112598227A (en) * 2020-12-03 2021-04-02 国家电网有限公司大数据中心 Power economic index construction method and system based on power data
CN113344737A (en) * 2021-06-04 2021-09-03 北京国电通网络技术有限公司 Device control method, device, electronic device and computer readable medium
CN113919564A (en) * 2021-10-09 2022-01-11 国网福建省电力有限公司经济技术研究院 Foreign trade enterprise development evaluation system based on electric power prosperity index mining and working method thereof
WO2023197502A1 (en) * 2022-04-11 2023-10-19 广西电网有限责任公司 Comprehensive power evaluation method and apparatus

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111476030A (en) * 2020-05-08 2020-07-31 中国科学院计算机网络信息中心 Prospective factor screening method based on deep learning
CN111476030B (en) * 2020-05-08 2022-03-15 中国科学院计算机网络信息中心 Prospective factor screening method based on deep learning
CN112116265A (en) * 2020-09-25 2020-12-22 国网上海市电力公司 Industry landscape index construction method driven by electric power data
CN112598227A (en) * 2020-12-03 2021-04-02 国家电网有限公司大数据中心 Power economic index construction method and system based on power data
CN113344737A (en) * 2021-06-04 2021-09-03 北京国电通网络技术有限公司 Device control method, device, electronic device and computer readable medium
CN113344737B (en) * 2021-06-04 2023-11-24 北京国电通网络技术有限公司 Device control method, device, electronic device and computer readable medium
CN113919564A (en) * 2021-10-09 2022-01-11 国网福建省电力有限公司经济技术研究院 Foreign trade enterprise development evaluation system based on electric power prosperity index mining and working method thereof
WO2023197502A1 (en) * 2022-04-11 2023-10-19 广西电网有限责任公司 Comprehensive power evaluation method and apparatus

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