CN106557835B - Power consumption prediction method and system based on scenic gas index - Google Patents
Power consumption prediction method and system based on scenic gas index Download PDFInfo
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Abstract
The invention discloses a method and a system for predicting power consumption based on a scenic index, wherein the method comprises the following steps: acquiring a power index within a preset time period of the power utilization industry to be detected; preprocessing the power index; screening out a prior index from the electric indexes by taking the current industry power consumption as a reference; determining the weight of the index according to the goodness of fit, the time difference correlation coefficient and the autocorrelation coefficient of each index in the prior indexes; determining a leading synthesis scene gas index according to the weight of the index; determining the number of the preceding phases according to the time difference correlation coefficient of the preceding synthesis landscape gas index and the current industry power consumption; performing regression fitting on the advanced synthesis prosperity index of the power utilization industry to be tested and the current industry power consumption of the power utilization industry to be tested according to the advanced number; and predicting the industry power consumption of the power utilization industry to be tested according to the regression fitting result. The method accurately predicts the power consumption of the industry and provides decision basis for the development planning of the future power industry.
Description
Technical field
The present invention relates to electricity demand forecasting technical field, more particularly to a kind of electricity demand forecasting side based on consumer confidence index
Method and system.
Background technology
With the continuous intensification of Chinese Urbanization and process of industrialization, electricity consumption demand drastically changes, the ripple of power consumption
Move closely related in macroeconomic change.Power consumption is the leading indicators for reflecting real economy future operation situation, is closed
Noting development of the social electricity consumption for planning power industry has vital meaning.Electricity needs it is whether accurate, no
The reliable electricity consumption of power grid security can be only influenceed, while the production and management decision-making and economic benefit of enterprises of managing electric wire netting can be influenceed.
Because industry is numerous, trade power consumption characteristic is different etc., reason, traditional electricity demand forecasting method gradually show
Reveal the deficiency of predictive ability, can not meet the needs of existing electricity demand forecasting, it is impossible to which the supply of electric power control to power network provides
Important technical support.
The content of the invention
Based on the above situation, the present invention proposes a kind of electricity demand forecasting method and system based on consumer confidence index, accurately
Trade power consumption amount is predicted, the development plan for future electrical energy industry provides decision-making foundation.
To achieve these goals, the embodiment of technical solution of the present invention is：
A kind of electricity demand forecasting method based on consumer confidence index, comprises the following steps：
Obtain the electric power index in electricity consumption industry preset time period to be measured, the electric power index according to the electricity consumption to be measured
The index of the industry development association of industry determines；
The electric power index is pre-processed, the pretreatment includes normalized, seasonal adjustment and Trend Decomposition；
On the basis of the current industry power consumption of the electricity consumption industry to be measured, sieved from the pretreated electric power index of progress
Select leading indicators；
According to the goodness of fit of each index, time difference coefficient correlation and auto-correlation coefficient in the leading indicators, institute is determined
State the weight of each index in leading indicators；
The leading synthesis boom for determining the electricity consumption industry to be measured according to the weight of each index in the leading indicators refers to
Number；
Used according to the current industry of the leading synthesis consumer confidence index of the electricity consumption industry to be measured and the electricity consumption industry to be measured
The time difference coefficient correlation of electricity, determine the leading synthesis consumer confidence index of the electricity consumption industry to be measured and the electricity consumption industry to be measured
The leading issue of current industry power consumption；
According to the leading issue, leading synthesis consumer confidence index and the electricity consumption row to be measured to the electricity consumption industry to be measured
The current industry power consumption of industry carries out regression fit；
The trade power consumption amount of the electricity consumption industry to be measured is predicted according to regression fit result.
A kind of electricity demand forecasting system based on consumer confidence index, including：
Index selection module, for obtaining the electric power index in electricity consumption industry preset time period to be measured, the electric power index
The index associated according to the industry development with the electricity consumption industry to be measured determines；
Index pretreatment module, for being pre-processed to the electric power index, it is described pretreatment include normalized,
Seasonal adjustment and Trend Decomposition；
Index screening module, on the basis of the current industry power consumption of the electricity consumption industry to be measured, from carrying out in advance
Leading indicators are filtered out in electric power index after reason；
Index weights determining module, for according to the goodness of fit of each index, time difference phase relation in the leading indicators
Number and auto-correlation coefficient, determine the weight of each index in the leading indicators；
Synthesis consumer confidence index determining module in advance, described in being determined according to the weight of each index in the leading indicators
The leading synthesis consumer confidence index of electricity consumption industry to be measured；
Leading issue determining module, for the leading synthesis consumer confidence index according to the electricity consumption industry to be measured with it is described to be measured
The time difference coefficient correlation of the current industry power consumption of electricity consumption industry, determine the leading synthesis consumer confidence index of the electricity consumption industry to be measured
With the leading issue of the current industry power consumption of the electricity consumption industry to be measured；
Regression fit module, for according to the leading issue, the leading synthesis boom to the electricity consumption industry to be measured to refer to
Number and the current industry power consumption of the electricity consumption industry to be measured carry out regression fit；
Electricity demand forecasting module, for being carried out according to regression fit result to the trade power consumption amount of the electricity consumption industry to be measured
Prediction.
Compared with prior art, beneficial effects of the present invention are：Electricity demand forecasting method of the invention based on consumer confidence index
And system, after obtaining the electric power index for influenceing industry development, electric power index is pre-processed, then from pretreated electric power
Leading indicators are filtered out in index, according to the goodness of fit of each index, time difference coefficient correlation and auto-correlation system in leading indicators
The weight of number agriculture products, consumer confidence index, and Accurate Prediction industry on this basis are synthesized according to the weight calculation of index in advance
Power consumption, the reasonability of prediction result is improved, the development plan for future electrical energy industry provides decision-making foundation.
Brief description of the drawings
Fig. 1 is the electricity demand forecasting method flow diagram based on consumer confidence index in one embodiment；
Fig. 2 is based on the electricity demand forecasting method flow diagram based on consumer confidence index in method one shown in Fig. 1 specific example；
Fig. 3 is the electricity demand forecasting system structure diagram based on consumer confidence index in one embodiment.
Embodiment
For the objects, technical solutions and advantages of the present invention are more clearly understood, below in conjunction with drawings and Examples, to this
Invention is described in further detail.It should be appreciated that embodiment described herein is only to explain the present invention,
Do not limit protection scope of the present invention.
Electricity demand forecasting method based on consumer confidence index in one embodiment, as shown in figure 1, comprising the following steps：
Step S101：Obtain the electric power index in electricity consumption industry preset time period to be measured, the electric power index according to institute
The index for stating the industry development association of electricity consumption industry to be measured determines；
Here, obtained in the index that electric power index associates from the industry development with the electricity consumption industry to be measured, to there is coloured gold
Exemplified by category is smelted and rolls processing industry, obtain non-ferrous metal metallurgy and roll the electricity in processing industry in January, 2013 in December, 2015
Power index, the index associated with the industry development of non-ferrous metal metallurgy and calendering processing industry include：Auto output, domestic refrigerator production
Amount, colour TV yield etc., the electric power that non-ferrous metal metallurgy and calendering processing industry can be obtained from these indexs according to being actually needed
Index.
Step S102：The electric power index is pre-processed, it is described pretreatment include normalized, seasonal adjustment and
Trend Decomposition；
Step S103：On the basis of the current industry power consumption of the electricity consumption industry to be measured, from the pretreated electricity of progress
Leading indicators are filtered out in power index；
Here, leading indicators are also known as leading indicator and are also referred to as emissary index, and refer to presage for the future month economic situation and can
The trade cycle property that can occur changes, and the market index of interest rate Long-term change trend early indication is provided for analyst.
Step S104：According to the goodness of fit of each index, time difference coefficient correlation and auto-correlation system in the leading indicators
Number, determine the weight of each index in the leading indicators；
Specifically, the weight of index refers to relative importance of the index in the overall evaluation.
Step S105：The leading conjunction of the electricity consumption industry to be measured is determined according to the weight of each index in the leading indicators
Into consumer confidence index；
Here, the prosperous change of consumer confidence index reflection industry.
Step S106：According to working as the leading synthesis consumer confidence index of the electricity consumption industry to be measured and the electricity consumption industry to be measured
The time difference coefficient correlation of preceding industry power consumption, determine the leading synthesis consumer confidence index of the electricity consumption industry to be measured and the use to be measured
The leading issue of the current industry power consumption of electric industry；
Specifically, coefficient correlation calculate be the same period two indices correlation, time difference coefficient correlation calculate be have it is stagnant
Coefficient correlation between later stage or the index of antephase；
Here, asynchronous synthesis consumer confidence index in advance and the time difference coefficient correlation of current industry power consumption are calculated, such as
By taking hysteresis or leading 4 phase as an example, then 9 time difference coefficient correlations can be calculated, a time difference coefficient correlation of the maximum that takes absolute value
Corresponding antephase is as leading issue.
Step S107：According to the leading issue, leading synthesis consumer confidence index to the electricity consumption industry to be measured and described
The current industry power consumption of electricity consumption industry to be measured carries out regression fit；
Here, regression model is built according to leading issue, in advance synthesis consumer confidence index and current industry power consumption.
Step S108：The trade power consumption amount of the electricity consumption industry to be measured is predicted according to regression fit result.
In addition, in a specific example, it is described according to the goodness of fit of each index, time difference phase in the leading indicators
Relation number and auto-correlation coefficient, determining the mode of the weight of each index in the leading indicators includes：
Successively using each index in the leading indicators as dependent variable, removed in the leading indicators as dependent variable
Index outside remaining each index returned as independent variable, obtained using stepwise regression method each in the leading indicators
The maximum goodness of fit of individual index, the leading indicators are determined according to the goodness of fit that each index in the leading indicators is maximum
In each index while independent information；
Here, using each index successively, other indexs for having theoretical first line of a couplet system therewith are as independent variable as dependent variable
Returned, using the method for successive Regression, take the maximum of which goodness of fitWherein i=1 ..., kjIndex in expression group
Sequence number, j=1,2,3 represent respectively in advance, consistent, lagging indicator group, kjIt is the index number of jth index group, parameter
While independent information SDi：Other indexs for having theoretical first line of a couplet system therewith refer to right to participate in as independent variable
The index of re-computation, refer herein to similar index, i.e. selected whole leading indicators.Wherein successive Regression is to use it
In an index as dependent variable, other indexs are stepped up independent variable number, compare and use different independents variable as independent variable
When the goodness of fit, the maximum regression equation of the selection wherein goodness of fit is as optimum regression equation.
The time difference coefficient correlation of each index and preset reference index in the leading indicators is obtained respectively, it is each from what is obtained
The time difference coefficient correlation of maximum absolute value is obtained in individual time difference coefficient correlation, according to the time difference coefficient correlation of the maximum absolute value
Determine the time difference independent information of each index in the leading indicators；
Specifically, the time difference coefficient correlation of each index and reference index is calculatedWherein xtRepresent each index in leading indicators, ytRepresent benchmark
Index, t represent the epoch number of index,For xtAverage value,For ytAverage value, n represents total epoch number, t=1,2,
3 ... n, select the time difference coefficient R of maximuml'=max | Rl|, work as Rl'When≤0, R is takenl'=0.Judgement is leading or stagnant
Index afterwards, make for leading indicatorsMake R for lagging indicatormi=l'Rl', wherein mi represents leading indicators or hysteresis
The index number of index, as l '>0, it is lagging indicator, l '<0, it is leading indicators, is not processed then for coincidence indicator, especially
Ground, for l=0 index i, due to SDiIts included information, then takes Rl'=0.The time difference for finally obtaining index independently believes
Breath：DDi=1/Rmi, work as RmiDD is taken when=0i=0.
The auto-correlation coefficient of each index in the leading indicators is obtained respectively, is obtained from each auto-correlation coefficient obtained
The auto-correlation coefficient for the maximum that takes absolute value, according to the auto-correlation coefficient of the maximum absolute value, using the time difference as weight, it is determined that
The prediction independent information of each index in the leading indicators；
Here, the auto-correlation coefficient of each index is calculatedL=0,1,2 ..., its
Middle Rls(l+1) individual auto-correlation coefficient of s-th of index is represented, selects maximum absolute value therein：Rl's=max | Rls|, make
Weight is done with the time difference：Rsi=lRl's, here it is the prediction independent information of index：PDi=Rsi。
According to independent information while each index, time difference independent information and prediction independent information in the leading indicators,
Determine the weight of each index in the leading indicators.
Specifically, above-mentioned three kinds of independent informations are added, and normalized, obtain each index weights：From the foregoing, compound independent information enabling legislation proposed by the present invention according to calculate refer to
Three kinds of target independent information, time difference coefficient correlation, auto-correlation coefficient information, the weight of each index is drawn after normalization.
In addition, in a specific example, the use to be measured is determined according to the weight of each index in the leading indicators
The mode of the leading synthesis consumer confidence index of electric industry includes：
Obtain the symmetrical rate of change of each index in the leading indicators；
Here, symmetrical rate of change is different from the direct rate of change of time series, and it is the variable quantity divided by two with two periods
The average value in individual period, rather than divided by initial period value so that positive change has symmetrical shape with negative change
Formula.
Index Y is set in one embodimentij(t) it is i-th of index of jth index group, calculates Yij(t) symmetrical rate of change Cij
(t) specific formula is as follows：
Wherein, T is total epoch number, as composing indexes Yij(t) when having zero or negative value in, or during ratio sequence, Cij
(t) it is equal to Yij(t) first-order difference：
Cij(t)=Yij(t)-Yij(t-1), t=2,3 ..., T
The mark of each index in the leading indicators is determined according to the symmetrical rate of change of each index in the leading indicators
The standardization factor；
In one embodiment, in order to avoid the big index of amplitude of fluctuation occupies ascendancy in composite index number, to each finger
The symmetrical standardized rate of target is standardized, and its average absolute value is equal to 1.
Normalized factors Aij, specific formula is as follows:
According to the standard of each index in the symmetrical rate of change of each index in the leading indicators and the leading indicators
Change the factor, it is determined that the standardization rate of change according to each index in the leading indicators；
Here, A is utilizedijCome to Cij(t) calculating is standardized, obtains standardizing rate of change Sij(t), specific formula is such as
Under：
According to the power of each index in the standardization rate of change of each index in the leading indicators and the leading indicators
Weight, determine the average rate of change of the leading indicators；
In one embodiment, the average rate of change R of leading indicators group is calculatedj(t), specific formula is as follows：
Wherein wijIt is the weight of each index.
The standard of index factor of the leading indicators is determined according to the average rate of change of the leading indicators；
Here, gauge index normalization factor Fj, specific formula is as follows：
Wherein F2=1
According to the average rate of change of the leading indicators and the standard of index factor, the standardization of the leading indicators is determined
The average rate of change；
Here, normalized average rate of change Vj(t), specific formula is as follows：
Determine that the leading synthesis of the electricity consumption industry to be measured is prosperous according to the standardization average rate of change of the leading indicators
Index.
In one embodiment, synthesis consumer confidence index I in advance is calculatedj(t)
The specific formula for calculating the initial consumer confidence index of synthesis in advance is as follows：
Ij(1)=100
In addition, in a specific example, the pretreatment also includes missing values and handled.
Here, missing values processing is to need to carry out when initial data has missing values.
In addition, in a specific example, the mode pre-processed to the electric power index includes：
Using linear interpolation method, the national numerical value based on acquisition, proportion of utilization method determines the numerical value being inserted into, according to determination
The numerical value being inserted into and default value growth rate polishing described in the numerical value that lacks in electric power index；
Specifically, missing values processing typically uses linear interpolation, if missing is more, just uses identical whole nation data,
Then adoption rate method calculatesN is total moon number of degrees, yi,k *For the monthly number of known i-th of index k
Value, yi,kFor the monthly national numerical value of i-th of index k, yi,tFor the monthly national numerical value of i-th of index t, yi,t *Need to insert
Numerical value, for the uneven data of national data, use the constant method of assumed growth rate to carry out polishing.
Effect is carried out to the electric power index after polishing missing values；
In one embodiment, method for normalizing uses efficiency coefficient method, and computational methods are：
The effect of direct index, formula was：
The effect of inverse indicators, formula was：
The effect of intermediate value optimal index, formula was：
yitFor the monthly numerical value of i-th of index t, yi,midIt is located at centre after being sorted from small to large for i-th of index value
Value (i.e. median, numerical value take the average of middle two when having even number).Each monthly each index is done into above-mentioned place
Reason.
To after being normalized electric power index carry out seasonal adjustment, obtain each index seasonal factor sequence and
Trend cyclic sequence；
Using default filter method to the seasonal factor sequence and trend cyclic sequence progress trend point of obtained each index
Solution.
In order to more fully understand the above method, electricity demand forecasting of the present invention based on consumer confidence index detailed below
The application example of method.
This application example is by taking certain province's non-ferrous metal metallurgy and calendering processing industry as an example.
As shown in Fig. 2 it may comprise steps of：
Step S201：Obtain certain electric power for saving non-ferrous metal metallurgy and rolling processing industry in January, 2013 in December, 2015
Index, the index determination that the electric power index associates according to the industry development with certain province's non-ferrous metal metallurgy and calendering processing industry,
The electric power index of acquisition is as shown in table 1, using in January, 2013 as the base period.
Certain the province's non-ferrous metal metallurgy of table 1 and the electric power index for rolling processing industry
Step S202：Above-mentioned electric power index is pre-processed, the pretreatment includes missing values and handled, at normalization
Reason, seasonal adjustment and Trend Decomposition；
(1) missing values processing typically uses linear interpolation, if missing is more, just uses identical whole nation data, then
Adoption rate method calculatesN is total moon number of degrees, yi,k *For the monthly numerical value of known i-th of index k,
yi,kFor the monthly national numerical value of i-th of index k, yi,tFor the monthly national numerical value of i-th of index t, yi,t *Need what is inserted
Numerical value, for the uneven data of national data, polishing is carried out using the constant method of assumed growth rate.
(2) method for normalizing uses efficiency coefficient method, and computational methods are：
The effect of direct index, formula was：
The effect of inverse indicators, formula was：
The effect of intermediate value optimal index, formula was：
yitFor the monthly numerical value of i-th of index t, yi,midIt is located at centre after being sorted from small to large for i-th of index value
Value (i.e. median, numerical value take the average of middle two when having even number).Each monthly each index is done into above-mentioned place
Reason.
(3) seasonal adjustment is carried out using Census X-12 methods, the seasonal factor sequence and trend for obtaining each index are followed
Ring sequence；
(4) Trend Decomposition is carried out using HP filter methods
Step S203：Using time difference Gray Correlation, with certain above-mentioned province's non-ferrous metal metallurgy and roll working as processing industry
On the basis of preceding industry power consumption, leading indicators are filtered out in pretreated electric power index is carried out, the leading indicators filtered out
As shown in table 2；
The index screening result of table 2
| Auto output | In advance |
| Marketable Housing Area Sold | Hysteresis |
| The sum of investments in fixed assets used | In advance |
| M1 | Unanimously |
| Domestic refrigerator yield | Unanimously |
| Domestic cooking fume remover | In advance |
| Colour TV yield | In advance |
| Main business income | In advance |
| The average number of whole practitioners | Unanimously |
| Current assets average balance | Unanimously |
| Copper product import volume | Unanimously |
| Copper product export volume | Hysteresis |
| Ten kinds of non-ferrous metal rate of production and marketing | Unanimously |
| LME base metal indexes | In advance |
Step S204：The weight for the leading indicators for determining to filter out using compound independent information enabling legislation, i.e., according to
The goodness of fit of each index, time difference coefficient correlation and auto-correlation coefficient in leading indicators, are determined each in the leading indicators
The weight of index, as a result as shown in table 3：
The weight calculation result of table 3
| Auto output | 0.175961 |
| The sum of investments in fixed assets used | 0.405183 |
| Domestic cooking fume remover yield | 0.014174 |
| Colour TV yield | 0.217294 |
| Main business income | 0.013767 |
| LME base metal indexes | 0.173621 |
Step S205：Above-mentioned certain province's non-ferrous metal metallurgy and calendering processing are determined according to the weight of the leading indicators filtered out
The leading synthesis consumer confidence index of industry；
(1) the symmetrical rate of change of each index is calculated；
Symmetrical rate of change is different from the direct rate of change of time series, and it is the variable quantity divided by two periods with two periods
Average value, rather than divided by initial period value so that positive change has symmetrical form with negative change.
If index Yij(t) it is i-th of index of jth index group, calculates Yij(t) symmetrical rate of change Cij(t) specific formula
It is as follows：
Wherein, T is total epoch number, as composing indexes Yij(t) when having zero or negative value in, or during ratio sequence, Cij
(t) it is equal to Yij(t) first-order difference：
Cij(t)=Yij(t)-Yij(t-1), t=2,3 ..., T
(2) normalization factor of parameter；
In order to avoid the big index of amplitude of fluctuation occupies ascendancy in composite index number, to the symmetrical standardized rate of each index
It is standardized, its average absolute value is equal to 1.
Normalized factors Aij, specific formula is as follows:
(3) the standardization rate of change of parameter；
Here, A is utilizedijCome to Cij(t) calculating is standardized, obtains standardizing rate of change Sij(t), specific formula is such as
Under：
(4) average rate of change of parameter；
Calculate the average rate of change R of leading indicators groupj(t), specific formula is as follows：
Wherein wijIt is the weight of each index.
(5) standard of index factor of parameter；
Gauge index normalization factor Fj, specific formula is as follows：
Wherein F2=1
(6) the standardization average rate of change of parameter；
Normalized average rate of change Vj(t), specific formula is as follows：
(7) synthesis consumer confidence index in advance is calculated.
Calculate synthesis consumer confidence index I in advancej(t)
The specific formula for calculating the initial consumer confidence index of synthesis in advance is as follows：
Ij(1)=100
The leading synthesis consumer confidence index and current industry power consumption numerical value calculated is as shown in table 4.
Table 4 synthesizes consumer confidence index and current industry power consumption in advance
Step S206：Using time difference correlation coefficient process, calculate certain above-mentioned province's non-ferrous metal metallurgy and roll the elder generation of processing industry
Row synthesis consumer confidence index and the leading issue of current industry power consumption；
Step S207：According to above-mentioned leading issue, to certain above-mentioned province's non-ferrous metal metallurgy and the leading conjunction of calendering processing industry
Regression fit is carried out into consumer confidence index and current industry power consumption；
According to table 4, can be found by calculating the trade power consumption amount of different lag periods and the coefficient correlation of leading composite index number,
When antephase number is 3, coefficient correlation is maximum, is 0.818588.Therefore leading issue is selected as 3, now, structure regression model
It is as follows：
log(Yt)=C+C1*Xt-3+ut, wherein YtFor trade power consumption amount, XtFor synthesis consumer confidence index in advance.
Step S208：According to above-mentioned regression model to certain above-mentioned province's non-ferrous metal metallurgy and the trade power consumption of calendering processing industry
Amount is predicted.
By the way that the leading composite index number in October, 2015 is substituted into regression equation, obtain estimating average：113,
846.35, and by adding and subtracting twice of mean square error3379.72 (whereinTo estimate obtained sequential value), obtain
In January, 2016, the estimation interval of trade power consumption amount was [110,466.63,117,226.07], and in January, 2016 actual row
Industry power consumption is 119,828.19, therefore in the range of estimation interval, therefore it is effective to predict.
By the way that the leading composite index number in 2015 11, December is substituted into regression equation, obtain estimating average：103,
010.86th, 99,040.05, estimation interval [99,631.14,106,390.58], [95,660.33,102,419.77], 2016 years
2nd, actual power consumption in March is respectively 102,935.81,101,135.68, and both of which is fallen into estimation interval, therefore is predicted
It is effective.
It is evidenced from the above discussion that the present embodiment influences the electric power index of industry development by obtaining, and fluctuated for industry
Property big the characteristics of correlation is strong between index, reasonable index weight is obtained using the correlation and autocorrelation of index, according to obtaining
The index weights taken calculate consumer confidence index, and Accurate Prediction trade power consumption amount on this basis, improve the reasonability of prediction result,
Development plan for future electrical energy industry provides decision-making foundation.
Electricity demand forecasting system based on consumer confidence index in one embodiment, as Fig. 3 shows, including：
Index selection module 301, for obtaining the electric power index in electricity consumption industry preset time period to be measured, the electric power refers to
Mark determines according to the index associated with the industry development of the electricity consumption industry to be measured；
Index pretreatment module 302, for being pre-processed to the electric power index, the pretreatment is included at normalization
Reason, seasonal adjustment and Trend Decomposition；
Index screening module 303, it is pre- from carrying out on the basis of the current industry power consumption of the electricity consumption industry to be measured
Leading indicators are filtered out in electric power index after processing；
Index weights determining module 304, for the goodness of fit according to each index in the leading indicators, when difference correlation
Coefficient and auto-correlation coefficient, determine the weight of each index in the leading indicators；
Synthesis consumer confidence index determining module 305 in advance, determined for the weight according to each index in the leading indicators
The leading synthesis consumer confidence index of the electricity consumption industry to be measured；
Leading issue determining module 306, for the leading synthesis consumer confidence index according to the electricity consumption industry to be measured with it is described
The time difference coefficient correlation of the current industry power consumption of electricity consumption industry to be measured, determine that the leading synthesis of the electricity consumption industry to be measured is prosperous
The leading issue of index and the current industry power consumption of the electricity consumption industry to be measured；
Regression fit module 307, for according to the leading issue, the leading synthesis to the electricity consumption industry to be measured to be prosperous
Index and the current industry power consumption of the electricity consumption industry to be measured carry out regression fit；
Electricity demand forecasting module 308, for the trade power consumption amount according to regression fit result to the electricity consumption industry to be measured
It is predicted.
As shown in figure 3, in a specific example, the index weights determining module 304 includes：
Index simultaneously independent information determining unit 3041, for successively using each index in the leading indicators as because
Variable, using remaining each index is returned as independent variable in addition to the index as dependent variable in the leading indicators, adopt
The goodness of fit that each index is maximum in the leading indicators is obtained with stepwise regression method, according to each in the leading indicators
The maximum goodness of fit of index determines independent information while each index in the leading indicators；
Index time difference independent information determining unit 3042, for obtaining each index in the leading indicators respectively and presetting
The time difference coefficient correlation of reference index, the time difference phase relation of maximum absolute value is obtained from each time difference coefficient correlation obtained
Number, the time difference independent information of each index in the leading indicators is determined according to the time difference coefficient correlation of the maximum absolute value；
Index prediction independent information determining unit 3043, for obtain respectively each index in the leading indicators from phase
Relation number, the auto-correlation coefficient of maximum absolute value is obtained from each auto-correlation coefficient obtained, according to the maximum absolute value
Auto-correlation coefficient, using the time difference as weight, determine the prediction independent information of each index in the leading indicators；
Index weights determining unit 3044, for according to independent information while each index in the leading indicators, when
Poor independent information and prediction independent information, determine the weight of each index in the leading indicators.
As shown in figure 3, in a specific example, the determining module of synthesis consumer confidence index in advance 305 includes：
The symmetrical rate of change obtaining unit 3051 of index, for obtaining the symmetrical change of each index in the leading indicators
Rate；
Criterion factor specifying unit 3052, for the symmetrical rate of change according to each index in the leading indicators
Determine the normalization factor of each index in the leading indicators；
Criterion rate of change determining unit 3053, for the symmetrical change according to each index in the leading indicators
The normalization factor of each index in rate and the leading indicators, it is determined that the standardization according to each index in the leading indicators
Rate of change；
Index average rate of change determining unit 3054, change for the standardization according to each index in the leading indicators
The weight of each index in rate and the leading indicators, determine the average rate of change of the leading indicators；
Index index normalization factor determining unit 3055, for determining institute according to the average rate of change of the leading indicators
State the standard of index factor of leading indicators；
Criterion average rate of change determining unit 3056, for the average rate of change according to the leading indicators and refers to
Number normalization factor, determine the standardization average rate of change of the leading indicators；
Synthesis consumer confidence index determining unit 3057 in advance, it is true for the standardization average rate of change according to the leading indicators
The leading synthesis consumer confidence index of the fixed electricity consumption industry to be measured.
In addition, in a specific example, the pretreatment also includes missing values and handled.
As shown in figure 3, in a specific example, the index pretreatment module 302 includes：
Index missing values processing unit 3021, for using linear interpolation method, the national numerical value based on acquisition, proportion of utilization
Method determines the numerical value being inserted into, and is lacked according in electric power index described in the numerical value being inserted into of determination and default value growth rate polishing
The numerical value of mistake；
Index normalized unit 3022, for carrying out effect to the electric power index after polishing missing values；
Index seasonal adjustment unit 3023, for obtain carry out effect after electric power index in each index season because
Prime sequences and trend cyclic sequence；
Index Trend Decomposition unit 3024, seasonal factor sequence and trend cyclic sequence for each index to obtaining
Carry out Trend Decomposition.
It is evidenced from the above discussion that the electricity demand forecasting method and system of the invention based on consumer confidence index, Accurate Prediction industry
Power consumption, the development plan for future electrical energy industry provide decision-making foundation.
Each technical characteristic of embodiment described above can be combined arbitrarily, to make description succinct, not to above-mentioned reality
Apply all possible combination of each technical characteristic in example to be all described, as long as however, the combination of these technical characteristics is not deposited
In contradiction, the scope that this specification is recorded all is considered to be.
Embodiment described above only expresses the several embodiments of the present invention, and its description is more specific and detailed, but simultaneously
Can not therefore it be construed as limiting the scope of the patent.It should be pointed out that come for one of ordinary skill in the art
Say, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to the protection of the present invention
Scope.Therefore, the protection domain of patent of the present invention should be determined by the appended claims.
Claims (6)
- A kind of 1. electricity demand forecasting method based on consumer confidence index, it is characterised in that comprise the following steps：Obtain the electric power index in electricity consumption industry preset time period to be measured, the electric power index according to the electricity consumption industry to be measured Industry development association index determine；The electric power index is pre-processed, the pretreatment includes missing values processing, normalized, seasonal adjustment and become Gesture is decomposed；The mode that the electric power index is pre-processed includes：Gather linear interpolation method, the national numerical value based on acquisition, using than Example method determines the numerical value being inserted into, according in electric power index described in the numerical value being inserted into of determination and default value growth rate polishing The numerical value of missing；Effect is carried out to the electric power index after polishing missing values；Obtain in the electric power index after carrying out effect The seasonal factor sequence and trend cyclic sequence of each index；The seasonal factor sequence of each index and trend circulation to obtaining Sequence carries out Trend Decomposition；On the basis of the current industry power consumption of the electricity consumption industry to be measured, filtered out from the pretreated electric power index of progress Leading indicators；According to the goodness of fit of each index, time difference coefficient correlation and auto-correlation coefficient in the leading indicators, the elder generation is determined The weight of each index in row index；The leading synthesis consumer confidence index of the electricity consumption industry to be measured is determined according to the weight of each index in the leading indicators；According to the current industry power consumption of the leading synthesis consumer confidence index of the electricity consumption industry to be measured and the electricity consumption industry to be measured Time difference coefficient correlation, determine the current of the leading synthesis consumer confidence index of the electricity consumption industry to be measured and the electricity consumption industry to be measured The leading issue of trade power consumption amount；According to the leading issue, leading synthesis consumer confidence index and the electricity consumption industry to be measured to the electricity consumption industry to be measured Current industry power consumption carries out regression fit；The trade power consumption amount of the electricity consumption industry to be measured is predicted according to regression fit result.
- 2. the electricity demand forecasting method according to claim 1 based on consumer confidence index, it is characterised in that described in the basis The goodness of fit of each index, time difference coefficient correlation and auto-correlation coefficient in leading indicators, are determined each in the leading indicators The mode of the weight of index includes：Successively using each index in the leading indicators as dependent variable, the finger as dependent variable will be removed in the leading indicators The outer remaining each index of mark is returned as independent variable, and each finger in the leading indicators is obtained using stepwise regression method The maximum goodness of fit is marked, is determined according to the maximum goodness of fit of each index in the leading indicators each in the leading indicators Independent information while individual index；Obtain the time difference coefficient correlation of each index and preset reference index in the leading indicators respectively, from obtain it is each when The time difference coefficient correlation of maximum absolute value is obtained in difference correlation coefficient, is determined according to the time difference coefficient correlation of the maximum absolute value The time difference independent information of each index in the leading indicators；The auto-correlation coefficient of each index in the leading indicators is obtained respectively, is obtained from each auto-correlation coefficient obtained exhausted To the auto-correlation coefficient that value is maximum, according to the auto-correlation coefficient of the maximum absolute value, using the time difference as weight, it is determined that described The prediction independent information of each index in leading indicators；According to independent information while each index, time difference independent information and prediction independent information in the leading indicators, it is determined that The weight of each index in the leading indicators.
- 3. the electricity demand forecasting method according to claim 1 or 2 based on consumer confidence index, it is characterised in that according to described The weight of each index determines that the mode of the leading synthesis consumer confidence index of the electricity consumption industry to be measured includes in leading indicators：Obtain the symmetrical rate of change of each index in the leading indicators；The standardization of each index in the leading indicators is determined according to the symmetrical rate of change of each index in the leading indicators The factor；According to the standardization of each index in the symmetrical rate of change of each index in the leading indicators and the leading indicators because Son, it is determined that the standardization rate of change according to each index in the leading indicators；According in the leading indicators each index standardization rate of change and the leading indicators in each index weight, really The average rate of change of the fixed leading indicators；The standard of index factor of the leading indicators is determined according to the average rate of change of the leading indicators；According to the average rate of change of the leading indicators and the standard of index factor, determine that the standardization of the leading indicators is averaged Rate of change；The leading synthesis consumer confidence index of the electricity consumption industry to be measured is determined according to the standardization average rate of change of the leading indicators.
- A kind of 4. electricity demand forecasting system based on consumer confidence index, it is characterised in that including：Index selection module, for obtaining the electric power index in electricity consumption industry preset time period to be measured, the electric power index according to The index associated with the industry development of the electricity consumption industry to be measured determines；Index pretreatment module, for being pre-processed to the electric power index, the pretreatment includes missing values processing, normalizing Change processing, seasonal adjustment and Trend Decomposition；The index pretreatment module includes：Index missing values processing unit, for using linear interpolation method, the national numerical value based on acquisition, proportion of utilization method to determine to treat The numerical value of insertion, according to the number lacked in electric power index described in the numerical value being inserted into of determination and default value growth rate polishing Value；Index normalized unit, for carrying out effect to the electric power index after polishing missing values；Index seasonal adjustment unit, for obtaining the seasonal factor sequence of each index in the electric power index after carrying out effect With trend cyclic sequence；Index Trend Decomposition unit, seasonal factor sequence and trend cyclic sequence for each index to obtaining carry out trend Decompose；Index screening module, on the basis of the current industry power consumption of the electricity consumption industry to be measured, after being pre-processed Electric power index in filter out leading indicators；Index weights determining module, for according to the goodness of fit of each index in the leading indicators, time difference coefficient correlation and Auto-correlation coefficient, determine the weight of each index in the leading indicators；Synthesis consumer confidence index determining module in advance, it is described to be measured for being determined according to the weight of each index in the leading indicators The leading synthesis consumer confidence index of electricity consumption industry；Leading issue determining module, for the leading synthesis consumer confidence index according to the electricity consumption industry to be measured and the electricity consumption to be measured The time difference coefficient correlation of the current industry power consumption of industry, determine leading synthesis consumer confidence index and the institute of the electricity consumption industry to be measured State the leading issue of the current industry power consumption of electricity consumption industry to be measured；Regression fit module, for according to the leading issue, leading synthesis consumer confidence index to the electricity consumption industry to be measured and The current industry power consumption of the electricity consumption industry to be measured carries out regression fit；Electricity demand forecasting module, it is pre- for being carried out according to regression fit result to the trade power consumption amount of the electricity consumption industry to be measured Survey.
- 5. the electricity demand forecasting system according to claim 4 based on consumer confidence index, it is characterised in that the index weights Determining module includes：Index while independent information determining unit, for using each index in the leading indicators as dependent variable, inciting somebody to action successively Remaining each index is returned as independent variable in addition to the index as dependent variable in the leading indicators, using progressively returning Method is returned to obtain the goodness of fit that each index is maximum in the leading indicators, it is maximum according to each index in the leading indicators The goodness of fit determine independent information while each index in the leading indicators；Index time difference independent information determining unit, for obtaining each index and preset reference index in the leading indicators respectively Time difference coefficient correlation, from each time difference coefficient correlation obtained obtain maximum absolute value time difference coefficient correlation, according to institute The time difference coefficient correlation for stating maximum absolute value determines the time difference independent information of each index in the leading indicators；Index predicts independent information determining unit, for obtaining the auto-correlation coefficient of each index in the leading indicators respectively, The auto-correlation coefficient of maximum absolute value is obtained from each auto-correlation coefficient obtained, according to the auto-correlation of the maximum absolute value Coefficient, using the time difference as weight, determine the prediction independent information of each index in the leading indicators；Index weights determining unit, for according to independent information, the time difference independently believe while each index in the leading indicators Breath and prediction independent information, determine the weight of each index in the leading indicators.
- 6. the electricity demand forecasting system based on consumer confidence index according to claim 4 or 5, it is characterised in that described leading Synthesis consumer confidence index determining module includes：The symmetrical rate of change obtaining unit of index, for obtaining the symmetrical rate of change of each index in the leading indicators；Criterion factor specifying unit, described in being determined according to the symmetrical rate of change of each index in the leading indicators The normalization factor of each index in leading indicators；Criterion rate of change determining unit, for the symmetrical rate of change according to each index in the leading indicators and described The normalization factor of each index in leading indicators, it is determined that the standardization rate of change according to each index in the leading indicators；Index average rate of change determining unit, for the standardization rate of change according to each index in the leading indicators and described The weight of each index in leading indicators, determine the average rate of change of the leading indicators；Index index normalization factor determining unit, for determining the finger in advance according to the average rate of change of the leading indicators The target standard of index factor；Criterion average rate of change determining unit, for the average rate of change and the standard of index according to the leading indicators The factor, determine the standardization average rate of change of the leading indicators；Synthesis consumer confidence index determining unit in advance, for being treated according to the determination of the standardization average rate of change of the leading indicators Survey the leading synthesis consumer confidence index of electricity consumption industry.
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