CN104881718B - Region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators - Google Patents
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Abstract
The present invention provides a kind of region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators, including：Establish multiple dimensioned economic indicator database；Obtain the monthly data of historical sample section region electricity consumption and each economic target；Filter out the economic leading indicators of region electricity consumption；Build the monthly prediction regression model of region electricity consumption；Predict the monthly region electricity consumption of target；Calculate the monthly region electric power consumer confidence index of target.The present invention extracts the information related with international and domestic economic situation variation from several economic indicators, and consider the time-lag effect between economic indicator and region electricity consumption, electric power consumer confidence index of the structure available for estimation range electricity consumption tendency, it efficiently solve the problems, such ass that independent variable index needs artificial selection, optimal models to be difficult to determine, scientific basis is provided for grid company programmed decision-making.
Description
Technical field
The present invention relates to power grid electricity demand forecasting technical field, the region electricity specifically based on multiple dimensioned economic leading indicators
Power consumer confidence index construction method.
Background technology
The study found that electricity consumption is very sensitive to national economy variation, therefore, the variation of power demand can be according to macroscopic view
The variation of Economic Status is predicted.And leading indicators can be divided into, synchronize and refer to by reflecting the index of macroeconomic operation situation
Mark and lagging indicator, wherein only leading indicators have directive significance to the prediction of power demand.In economic situation complication, warp
Under the Ji background that vibration frequency is higher and higher, amplitude is increasing, traditional electricity demand forecasting method gradually shows prediction
Deficiency in ability, and the development of data mining technology and econometrics, then for electricity demand forecasting provide new thinking and
Method is conducive to improve the timeliness and accuracy of electricity demand forecasting.
Invention content
The purpose of the present invention is to provide a kind of region electric power consumer confidence index structures based on multiple dimensioned economic leading indicators
By establishing the multivariate regression models of region electricity consumption and multiple dimensioned economic leading indicators, it is prosperous further to build electric power for method
Index, to realize estimation range electricity consumption and externally issue the function of electricity consumption variation tendency.
The technical scheme is that：
A kind of region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators, includes the following steps：
(1) several economic indicators are chosen from region, the whole nation and international three scales, establishes multiple dimensioned economic indicator data
Library；
(2) monthly data of historical sample section region electricity consumption and each economic target is obtained, and to region electricity consumption
Monthly data carry out seasonal adjustment；
(3) the economic leading indicators of region electricity consumption are filtered out, and determine the advanced issue of every economic leading indicators；
(4) monthly data based on historical sample section region electricity consumption and every economic leading indicators, structure region are used
The monthly prediction regression model of electricity；
(5) according to the advanced issue of every economic leading indicators, the monthly phase of every economic advanced target of leading indicators is obtained
The monthly data of issue is answered, further according to the monthly prediction regression model of region electricity consumption, the monthly region electricity consumption of prediction target；
(6) the monthly region electric power consumer confidence index of target is calculated using the following formula：
Wherein, EBItRepresent the monthly region electric power consumer confidence index of target, YtRepresent the monthly region electricity consumption of target,
Yt-ΔtRepresent the region electricity consumption of advanced target monthly Δ t phases.
The region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators, in the step (2),
Seasonal adjustment is carried out to the monthly data of region electricity consumption using seasonal adjustment multiplied model.
The region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators, in the step (3),
The economic leading indicators of region electricity consumption are filtered out using K-L information Contents Methods.
The region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators, step (4) packet
It includes：
(41) based on akaike information criterion AIC and Bayesian Information amount criterion BIC, one is built respectively with region electricity consumption
It measures as dependent variable, the monthly prediction regression model of alternative area electricity consumption that economic leading indicators are independent variable；
(42) prediction regression model monthly to two alternative area electricity consumptions is tested, and the fitting for comparing two models misses
Difference, using the smaller model of wherein error of fitting as the monthly prediction regression model of final region electricity consumption.
As shown from the above technical solution, the present invention is whole by collecting international, the domestic and multiple dimensioned economic indicator in region extensively
He Liao developed countries and domestic macroeconomy, industrial economy data, brisk market data etc., extract from several economic indicators
The information related with international and domestic economic situation variation, and consider the time-lag effect between economic indicator and region electricity consumption, structure
Build the electric power consumer confidence index available for estimation range electricity consumption tendency, efficiently solve independent variable index need artificial selection,
The problem of optimal models are difficult to determine, scientific basis is provided for grid company programmed decision-making.
Description of the drawings
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is contrast curve before and after Anhui Province's Analyzing Total Electricity Consumption seasonal adjustment；
Fig. 3 is the monthly prediction Regression Model Simulator figure of Anhui Province's Analyzing Total Electricity Consumption.
Specific embodiment
Come furtherly as specific embodiment using the building process of Anhui electric power saving consumer confidence index and practice below
The bright present invention.
As shown in Figure 1, a kind of region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators, including with
Lower step：
S1, several economic indicators are chosen from region, the whole nation and international three scales, establishes multiple dimensioned economic indicator data
Library：
143 economic indicators are chosen altogether from Anhui Province, the whole nation and international three scales, wherein, Anhui Province's economic indicator class
Type includes：(1) the economic overall objective of the whole province；(2) financial class；(3) investment type：Gross fixed assets investment, every profession and trade fixed assets
Produce finished value of investment；(4) real estate investment volume；(5) industrial added value；(6) consumption of resident：State revenue, state revenue and expenditure
Expenditure；(7) foreign trade；(8) industrial goods yield etc..National pointer type includes：(1) index number of operation of manufacturing industry；(2) nonmanufacturing industry
Index；(3) money supply；(4) international trade and international investment；(5) communications and transportation；(6) key industry consumer confidence index；(7)
The branch trade value added in developed area Shanghai City etc..International indicator type includes：(1) U.S., Japan and euro area disengaging
Mouth trade：Total import value, total export；(2) U.S., Japan and euro area unemployment rate.
S2, the monthly data for obtaining historical sample section region electricity consumption and each economic target, and to region electricity consumption
Monthly data carry out seasonal adjustment：
Using in January, 2008~2014 year July as historical sample section, Anhui Province's Analyzing Total Electricity Consumption and 143 warps are obtained
The monthly data for index of helping, and seasonal adjustment is carried out to the monthly data of Anhui Province's Analyzing Total Electricity Consumption, to deduct seasonality
The influence of factor.
The growth trend of electricity consumption and seasonal trend are simultaneously deposited, and the two is with additive or multiplication form forms composite model.For
Eliminate influence of the seasonal factor to electricity demand forecasting model, the present invention is using seasonal adjustment multiplied model to the use that is collected into
Electricity data carries out seasonal adjustment, as shown in Fig. 2, the correlation curve front and rear for the adjustment of Anhui Province's Analyzing Total Electricity Consumption.It needs
Illustrate, the present invention is when building electricity demand forecasting regression model using the data after seasonal adjustment, model prediction result
Not consider the value of seasonal factor, finally also need to by predicted value be multiplied by corresponding seasonal factor can just obtain finally predict knot
Fruit.
S3, the economic leading indicators for filtering out region electricity consumption, and determine the advanced issue of every economic leading indicators：
K-L information content analysis is carried out to Anhui Province's Analyzing Total Electricity Consumption and 143 economic indicators, filters out Anhui Province Quan She
The economic leading indicators of meeting electricity consumption, as a result and its parameter (is noted as shown in table 1：" Anhui " after index name represents that the index is
Anhui Province's economic indicator, " Shanghai " represent the index as Shanghai City economic indicator, other are the whole nation or international economy index).
Table 1
K-L information Contents Methods measure the distance between they weighing the similarity degree of two probability distribution, it is smaller i.e.
The two is represented closer to more similar.Its principle is using reference index as theoretical distribution, and alternative index is sample distribution, is constantly changed
Alternative index and the reference index time difference, calculate K-L information content.Difference is determined as alternatively referring to when corresponding during K-L information content minimums
The target final time difference.When alternative index and reference index are completely the same, K-L information content is equal to 0；Alternative index and reference index
Closer, K-L information content absolute values are smaller, closer to 0.
The present embodiment index on the basis of Anhui Province's Analyzing Total Electricity Consumption, using 143 economic indicators as alternative index, respectively
Calculating benchmark index and each alternative index select minimum K-L information from the K-L information content sizes of -12 ranks to+12 ranks
Amount and its corresponding time lag exponent number, so as to obtain best lag period of all alternative indexs relative to reference index.If certain is alternative
The best lag period of index is less than 0, then the alternative index is screened as economic leading indicators.
S4, the monthly data based on historical sample section region electricity consumption and every economic leading indicators, structure region are used
The monthly prediction regression model of electricity：
Historical sample section is divided into two parts, wherein the historical data in January, 2008~2014 year April is pacified for establishing
The monthly prediction regression model of emblem province Analyzing Total Electricity Consumption, the historical data in May, 2014~2014 year July are used for the peace to foundation
The monthly prediction regression model of emblem province Analyzing Total Electricity Consumption is tested, is assessed.
The 32 economic leading indicators (as shown in table 1) filtered out are converted according to its advanced issue, then conduct
Independent variable, using Anhui Province's Analyzing Total Electricity Consumption as dependent variable, with akaike information criterion AIC (Akaike Information
Criterion), two kinds of screening principles point of Bayesian Information amount criterion BIC (Bayesian Information Criterion)
Not Xuan Qu optimum prediction regression model, further according to the fit solution of two kinds of optimum prediction regression models, it is smaller to choose error of fitting
Model, by comparing, the optimum prediction regression model selected based on AIC criterion becomes the final whole society of Anhui Province electricity consumption
Monthly prediction regression model, output model parameter and fitting result are measured, as shown in table 2.Wherein, conspicuousness：" * * * " is represented
Notable in 0.001 level, " * * " represents that in 0.01 level significantly, " * " is represented in 0.05 level significantly.
Table 2
S5, the advanced issue according to every economic leading indicators, obtain the monthly phase of every economic advanced target of leading indicators
The monthly data of issue is answered, further according to the monthly prediction regression model of region electricity consumption, the monthly region electricity consumption of prediction target；
The fitting result of the monthly prediction regression model of Anhui Province's Analyzing Total Electricity Consumption is as shown in Figure 3.It tests to model
And Fitting Analysis, the results show that the regression criterion of model meets white noise characteristics, plan of the analog result after seasonal adjustment restores
Conjunction value is consistent with actual value variation tendency, and fitting average error rate is 1.8%.Fitting result is preferable, which can use
To predict Anhui Province's Analyzing Total Electricity Consumption monthly.
S6, the monthly region electric power consumer confidence index of target is calculated using the following formula：
Wherein, EBItRepresent the monthly region electric power consumer confidence index of target, YtRepresent the monthly region electricity consumption of target,
Yt-ΔtRepresent the region electricity consumption of advanced target monthly Δ t phases.
By taking Δ t=2 as an example, the electric power consumer confidence index in Anhui Province in April, 2013~2014 year March is calculated, such as 3 institute of table
Show.
| Month | Amplification | Consumer confidence index | Month | Amplification | Consumer confidence index |
| In May, 2013 | 3% | 101 | In November, 2013 | - 9% | 95 |
| In June, 2013 | 7% | 103 | In December, 2013 | 11% | 105 |
| In July, 2013 | 22% | 111 | In January, 2014 | 14% | 107 |
| In August, 2013 | 29% | 114 | 2 months 2014 | - 16% | 92 |
| In September, 2013 | - 9% | 95 | In March, 2014 | - 2% | 99 |
| In October, 2013 | - 22% | 89 | In April, 2014 | 7% | 103 |
Table 3
It is influenced, and is existed certain by international and domestic economic situation to a certain extent the present invention is based on region electricity consumption
Time-lag effect, so as to by economic leading indicators estimation range electricity consumption build region electric power consumer confidence index.Region electric power
Consumer confidence index is in addition to can be other than the turning point of estimation range electricity consumption cyclic swing, moreover it is possible to reflect electricity consumption period wave in some sense
Dynamic amplitude has preferable timeliness and accuracy.
Embodiment described above is only that the preferred embodiment of the present invention is described, not to the model of the present invention
It encloses and is defined, under the premise of design spirit of the present invention is not departed from, those of ordinary skill in the art are to the technical side of the present invention
The various modifications and improvement that case is made should all be fallen into the protection domain that claims of the present invention determines.
Claims (3)
1. a kind of region electric power consumer confidence index construction method based on multiple dimensioned economic leading indicators, which is characterized in that including with
Lower step：
(1) several economic indicators are chosen from region, the whole nation and international three scales, establishes multiple dimensioned economic indicator database；
(2) monthly data of historical sample section region electricity consumption and each economic target is obtained, and to the moon of region electricity consumption
Degrees of data carries out seasonal adjustment；
(3) the economic leading indicators of region electricity consumption are filtered out, and determine the advanced issue of every economic leading indicators；
(4) monthly data based on historical sample section region electricity consumption and every economic leading indicators, builds region electricity consumption
Monthly prediction regression model；
(5) according to the advanced issue of every economic leading indicators, the every economic advanced target of leading indicators monthly corresponding phase is obtained
Several monthly data, further according to the monthly prediction regression model of region electricity consumption, the monthly region electricity consumption of prediction target；
(6) the monthly region electric power consumer confidence index of target is calculated using the following formula：
Wherein, EBItRepresent the monthly region electric power consumer confidence index of target, YtRepresent the monthly region electricity consumption of target, Yt-ΔtTable
Show the region electricity consumption of advanced target monthly Δ t phases；
The step (4) includes：
(41) based on akaike information criterion AIC and Bayesian Information amount criterion BIC, build respectively one using region electricity consumption as
Dependent variable, the monthly prediction regression model of alternative area electricity consumption that economic leading indicators are independent variable；
(42) prediction regression model monthly to two alternative area electricity consumptions is tested, and compares the error of fitting of two models,
Using the smaller model of wherein error of fitting as the monthly prediction regression model of final region electricity consumption.
2. the region electric power consumer confidence index construction method according to claim 1 based on multiple dimensioned economic leading indicators,
It is characterized in that：In the step (2), the monthly data of region electricity consumption is carried out using seasonal adjustment multiplied model seasonal
Adjustment.
3. the region electric power consumer confidence index construction method according to claim 1 based on multiple dimensioned economic leading indicators,
It is characterized in that：In the step (3), the economic leading indicators of region electricity consumption are filtered out using K-L information Contents Methods.
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| CN106022525B (en) * | 2016-05-24 | 2018-03-30 | 中国南方电网有限责任公司电网技术研究中心 | Power planning scheme based on consumer confidence index index determines method and device |
| CN106355306A (en) * | 2016-08-18 | 2017-01-25 | 中国南方电网有限责任公司电网技术研究中心 | Analysis method of economic climate index based on regional electricity characteristics and system thereof |
| CN106447075B (en) * | 2016-08-18 | 2017-09-15 | 中国南方电网有限责任公司电网技术研究中心 | Trade power consumption needing forecasting method and system |
| CN108287867B (en) * | 2017-12-19 | 2021-11-23 | 顺丰科技有限公司 | Industry prosperity index generation method, device, equipment and storage medium thereof |
| CN109727155A (en) * | 2018-08-02 | 2019-05-07 | 平安科技（深圳）有限公司 | Power consumption control method, apparatus, equipment and storage medium based on power quantity predicting |
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