CN105069529A - Pre-arranged power failure time predication method and system - Google Patents

Abstract

A pre-arranged power failure time predication method and system are provided. The method comprises the following steps of: acquiring screened production control indicators used for predicating, and acquiring a historical data sample; establishing a linear relation model of the production control indicators and pre-arranged power failure time according to the production control indicators; determining a regression coefficient according to the historical data sample; substituting the regression coefficient into the linear relation model, and determining a predicated value of the pre-arranged power failure time; and the production control indicators comprising a load supply capability type indicator, a network structure level type indicator, an operation management level type indicator, an equipment technical level type indicator and a capital construction technical modification investment type indicator. The method and system consider the plurality of production control factors including a supply capability, a network structure level, an operation management level, an equipment technical level and capital construction technical modification investment and the like, and the predicated value of the determined pre-arranged power failure time is more reliable and reasonable.

Description

Pre-arranged power off time Forecasting Methodology and system

Technical field

The present invention relates to Distribution Management System construction and application, particularly relate to a kind of pre-arranged power off time Forecasting Methodology and system.

Background technology

It is that grid company has the production schedule and before at least 6 hours, notifies the power failure of user that pre-arranged has a power failure.Pre-arranged has a power failure not only relevant with grid structure level, equipment healthy water equality objective factor, is also closely related with the subjective factor such as operation and management level, power failure demand, possesses certain controllability.According to the data statistics of Guangdong Power Grid power-off event in recent years, pre-arranged power off time accounts for more than 60% of the T.T. that has a power failure, and the conservative control of pre-arranged power off time is the key of reliable management.At present 2 kinds of schemes are mainly contained to pre-arranged power failure modeling: 1) Commutation Law, suppose the electric grid investment amount of money and pre-arranged outage rate linear, utilize the electric grid investment amount of money over the years and pre-arranged outage rate, the linear relationship of both measuring and calculating, then converts the pre-arranged outage rate of this year according to the electric grid investment amount of money in prediction year.Finally, more ripe fault outage time forecasting methods are utilized to calculate pre-arranged power off time predicted value; 2) method of determining and calculating, sets up the pre-arranged blackout model of different engineering type according to concrete power failure plan, add up all power failure plans measuring and calculating pre-arranged power off time.

Pre-arranged has a power failure to be affected by many factors, especially affects by subjective factor comparatively large, is difficult to set up complete forecast model.The model that current method is set up often can only reflect grid structure and power failure demand, not yet can reflect the operation and management level of electrical network.The Back ground Information that Commutation Law and method of determining and calculating use comprises network frame topology information and distribution power failure quantities, does not consider operation and management level.Current method Consideration is more unilateral, and can only predict roughly pre-arranged power off time, its reliability, rationality have much room for improvement.

Summary of the invention

Based on this, be necessary to provide a kind of Consideration more comprehensively, predict the outcome more reliably, more reasonably pre-arranged power off time Forecasting Methodology and system.

A kind of pre-arranged power off time Forecasting Methodology, comprises step:

Obtain that screen, for predicting production management and control index, and obtain historical data sample;

According to described production management and control index, set up the linear relationship model of described production management and control index and pre-arranged power off time;

According to described historical data sample determination regression coefficient;

Described regression coefficient is substituted in described linear relationship model, determines the predicted value of described pre-arranged power off time;

Wherein, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.

A kind of pre-arranged power off time prognoses system, comprising:

Acquisition module, for obtain screening, production management and control index for predicting, and obtain historical data sample;

Model building module, for according to described production management and control index, sets up the linear relationship model of described production management and control index and pre-arranged power off time;

Coefficient determination module, for according to described historical data sample determination regression coefficient;

Time prediction module, for described regression coefficient being substituted in described linear relationship model, determines the predicted value of described pre-arranged power off time;

Wherein, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.

Above-mentioned pre-arranged power off time Forecasting Methodology and system, consider many-sided production management and control factors such as deliverability, network structure level, operation and management level, equipment technology level and capital construction investment in technological upgrading, the predicted value of its pre-arranged power off time determined more reliably, more reasonable.

Accompanying drawing explanation

Fig. 1 is a kind of process flow diagram of pre-arranged power off time Forecasting Methodology of embodiment;

Fig. 2 is the relation Cause and Effect matrix of production management and control index and pre-arranged power off time in a kind of embodiment;

Fig. 3 is the particular flow sheet of a step in Fig. 1;

Fig. 4 is a kind of structural drawing of pre-arranged power off time prognoses system of embodiment;

Fig. 5 is the concrete structure figure of a module in Fig. 4.

Embodiment

For the ease of understanding the present invention, below with reference to relevant drawings, the present invention is described more fully.Preferred embodiment of the present invention is given in accompanying drawing.But the present invention can realize in many different forms, is not limited to embodiment described herein.On the contrary, provide the object of these embodiments be make the understanding of disclosure of the present invention more comprehensively thorough.

Unless otherwise defined, all technology used herein and scientific terminology are identical with belonging to the implication that those skilled in the art of the present invention understand usually.The object of term used in the description of the invention herein just in order to describe specific embodiment, is not intended to be restriction the present invention.Term as used herein " or/and " comprise arbitrary and all combinations of one or more relevant Listed Items.

As shown in Figure 1, a kind of pre-arranged power off time Forecasting Methodology of embodiment, comprises the following steps:

S110: obtain that screen, for predicting production management and control index, and obtain historical data sample.

Wherein in an embodiment, as shown in Figure 2, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.So, all kinds of factor can be considered, prediction can be made to arrange the reliability of power off time, rationality better.

Wherein, described load service capability class index comprises the heavy Overflow RateHT of distribution transforming, the heavy Overflow RateHT of the heavy Overflow RateHT of circuit and transformer station; Described network structure horizontal class index comprises distribution line looped network rate, distribution line segments, distribution line wire diameter not Service Efficiency and the monotropic rate of single line; Described operation and management level class index comprises time delay and stops power transmission rate, hot line job rate, defect elimination rate and repeat outage rate; The horizontal class index of described equipment technology comprises distribution line and can turn for rate and robotization coverage rate; Described capital construction investment in technological upgrading class index comprises capital fund (not shown).

Understandably, above-mentioned production management and control index is only recommendation items, according to actual conditions, can increase and decrease according to above-mentioned screening principle.

Production management and control index is various, first needs to screen production management and control index according to screening principle.

Wherein in an embodiment, the screening principle of production management and control index comprises: the production management and control index preferentially selecting existing statistics in infosystem; Rejecting Statistical Criteria, the production management and control index that statistical method is fuzzy; Measurement period is preferentially selected at least to be accurate to the production management and control index of the moon.

Obtain the historical data sample presetting the time limit.The default time limit can be arbitrarily in recent years, any several years or recent months or arbitrarily some months arbitrarily.

Historical data sample comprises the column vector Y of history pre-arranged power off time ₀with history production management and control index matrix X ₀, therefore can pass through historical data sample, determine regression coefficient β.

S130: according to described production management and control index, set up the linear relationship model of described production management and control index and pre-arranged power off time.

Adopt multiple linear regression can set up pre-arranged power off time and production management and control index linear relationship model between the two, its funtcional relationship can be expressed as:

y＝Xβ……(1)

Y represents pre-arranged power off time, and X represents every production management and control indicator vector, and β is the column vector that regression coefficient is formed.

S150: obtain historical data sample, and according to described historical data sample determination regression coefficient.

Refer to Fig. 3, wherein in an embodiment, step S150 comprises step S153 ~ S157.

S153: according to described historical data sample, judges whether there is multicollinearity between described production management and control index.If not, then step S155 is performed; If so, then step S157 is performed.

For reducing the problem causing the distortion of linear relationship model between production management and control index because of multicollinearity, guarantee the validity of linear relationship model, according to described historical data sample, condition number is utilized to judge whether there is multicollinearity between production management and control index.

Wherein in an embodiment, if screened n item historical data sample number, p item production management and control index, has formed production management and control index matrix X ₀for:

$X_0=\left[\begin{array}{cccc}{x}_1& x_2& \mathrm{...}& x_p\end{array}\right]=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \mathrm{...}& x_{1p}\\ x_{21}& x_{22}& \mathrm{...}& x_{2p}\\ .& .& & .\\ .& .& \mathrm{...}& .\\ .& .& & .\\ x_{n1}& x_{n2}& \mathrm{...}& x_{np}\end{array}\right]\mathrm{......}\left(2\right)$

Wherein, x ₁1st production management and control index historical data column vector; x _pit is p item production management and control index historical data column vector.

Square formation X ₀ ^tx ₀conditional number be:

$C_d=\frac{{\mathrm{\λ}}_{max}}{{\mathrm{\λ}}_{min}}\mathrm{......}\left(3\right)$

Wherein, λ _maxand λ _minbe respectively square formation X ₀ ^tx ₀minimum and maximum characteristic root.Due to the mensurable square formation X of conditional number ₀ ^tx ₀characteristic root dispersion level, so can judge whether to there is multicollinearity by conditional number.

When conditional number belongs to the first preset range, then there is not multicollinearity.In the present embodiment, the first preset range is: 0<C _d<100.

When conditional number belongs to the second preset range, then there is multicollinearity.In the present embodiment, the second preset range is: 100≤C _d.Wherein, there is not common range in the second preset range and the first preset range.

S155: adopt least squares estimate determination regression coefficient according to described historical data sample.

The regression coefficient column vector that least squares estimate is determined is:

β＝(X ₀ ^TX ₀) ^-1X ₀ ^TY ₀……(4)

Wherein, X ₀ ^trepresent X ₀transposed matrix, subscript (X ₀ ^tx ₀) ^-1represent X ₀ ^tx ₀inverse matrix.

S157: adopt ridge analysis method to draw ridge mark figure according to described historical data sample, the described production management and control index of screening for predicting, determines regression coefficient further; And the described linear relationship model of described production management and control index and described pre-arranged power off time is re-established according to the described production management and control index of screening further.

Due to when judging to find that production management and control index exists multicollinearity, | X ₀ ^tx ₀| ≈ 0, least squares estimate lost efficacy.Dimension, adopts ridge regression (RidgeRegression) method of estimation, to X ₀ ^tx ₀an additional positive constant matrices kI (k>0, I are unit matrix), then determine that regression coefficient column vector is:

$\widehat{\mathrm{\&beta;}}\left(k\right)={({X_0}^T{X}_0+kI)}^{-1}{X_0}^T{Y}_0\mathrm{......}\left(5\right)$

Wherein, k is ridge parameter.

Determination about ridge parameter: as k=0, adopts the regression coefficient that ride regression estimater method is determined equal the regression coefficient β that conventional least squares estimate is determined.When ridge parameter k changes in (0 ,+∞), regression coefficient for the function of k, obtain change curve, be ridge mark figure.By the analysis to ridge mark figure, can do production management and control index and further judge and screening, and determine suitable ridge parameter k value.

The screening principle of production management and control index is as follows: 1) when ridge parameter k=0, numerical value is very large; K increases slightly, be tending towards 0 rapidly, show x _jvery little on the impact of pre-arranged power off time, can by x _jrejected.2) with in the mark figure of ridge, both sums are substantially constant, show production management and control index x _jand x _j'there is stronger linear dependence, only can retain wherein one.3) not by k variable effect remain on the occasion of or negative value, represent the quality of data not good, can by x _jrejected.Wherein, it is the regression coefficient of jth item production management and control index when taking k as ridge parameter.

S170: described regression coefficient substituted in described linear relationship model, determines the predicted value of described pre-arranged power off time.

Continue referring to Fig. 1, wherein in an embodiment, after step S110, before step S130, also comprise step:

S120: described production management and control index is normalized according to described historical data sample.

In order to eliminate the difference that there is dimension between production management and control index, need being normalized every production management and control index, obtain the matrix after normalization.In the present embodiment, normalized specifically can comprise step: first, carries out centralization process to historical data sample, and namely each element of every production management and control index historical data column vector deducts the average of column vector, as formula (7); Then, carry out standardization, namely each element of every production management and control index historical data column vector deducts the average of column vector, then divided by the standard deviation of column vector, as formula (8).

${\stackrel{\&OverBar;}{x}}_j=\frac{1}{n}\underset{i=1}{\overset{n}{\&Sigma;}}{x}_{ij}\mathrm{......}\left(6\right)$

${{\mathrm{\&sigma;}}_j}^2=\frac{1}{n-1}\underset{i=1}{\overset{n}{\&Sigma;}}{(x_{ij}-{\stackrel{\&OverBar;}{x}}_j)}^2\mathrm{......}\left(7\right)$

${\hat{x}}_j=\frac{x_j-{\stackrel{\&OverBar;}{x}}_j}{{\mathrm{\&sigma;}}_j}\mathrm{......}\left(8\right)$

Wherein, i=1,2 ..., n, n be historical data sample number; J=1,2 ..., p, p be the item number of production management and control index.

Above-mentioned pre-arranged power off time Forecasting Methodology, obtains that screen, for predicting production management and control index, and obtains historical data sample; According to described production management and control index, set up the linear relationship model of production management and control index and pre-arranged power off time; According to described historical data sample determination regression coefficient; Described regression coefficient is substituted in described linear relationship model, determines the predicted value of described pre-arranged power off time; Wherein, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.The method considers many-sided production management and control factors such as deliverability, network structure level, operation and management level, equipment technology level and capital construction investment in technological upgrading, the predicted value of its pre-arranged power off time determined more reliably, more reasonable.

As shown in Figure 4, a kind of pre-arranged power off time prognoses system of embodiment, comprising:

Acquisition module 110, for obtain screening, production management and control index for predicting, and obtain historical data sample.

Wherein in an embodiment, as shown in Figure 2, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.So, all kinds of factor can be considered, prediction can be made to arrange the reliability of power off time, rationality better.

Wherein, described load service capability class index comprises the heavy Overflow RateHT of distribution transforming, the heavy Overflow RateHT of the heavy Overflow RateHT of circuit and transformer station; Described network structure horizontal class index comprises distribution line looped network rate, distribution line segments, distribution line wire diameter not Service Efficiency and the monotropic rate of single line; Described operation and management level class index comprises time delay and stops power transmission rate, hot line job rate, defect elimination rate and repeat outage rate; The horizontal class index of described equipment technology comprises distribution line and can turn for rate and robotization coverage rate; Described capital construction investment in technological upgrading class index comprises capital fund (not shown).

Understandably, above-mentioned production management and control index is only recommendation items, according to actual conditions, can increase and decrease according to above-mentioned screening principle.

Production management and control index is various, first needs to screen production management and control index according to screening principle.

Wherein in an embodiment, the screening principle of production management and control index comprises: the production management and control index preferentially selecting existing statistics in infosystem; Rejecting Statistical Criteria, the production management and control index that statistical method is fuzzy; Measurement period is preferentially selected at least to be accurate to the production management and control index of the moon.

Acquisition module 110 obtains the historical data sample presetting the time limit.The default time limit can be arbitrarily in recent years, any several years or recent months or arbitrarily some months arbitrarily.

Historical data sample comprises the column vector Y of history pre-arranged power off time ₀with history production management and control index matrix X ₀, therefore can pass through historical data sample, determine regression coefficient β.

Model building module 130, for according to described production management and control index, sets up the linear relationship model of described production management and control index and pre-arranged power off time.

Adopt multiple linear regression can set up pre-arranged power off time and production management and control index linear relationship model between the two, its funtcional relationship can be expressed as:

y＝Xβ……(1)

Y represents pre-arranged power off time, and X represents every production management and control indicator vector, and β is the column vector that regression coefficient is formed.

Coefficient determination module 150, for according to described historical data sample determination regression coefficient.

Refer to Fig. 5, wherein in an embodiment, coefficient determination module 150 comprises:

Conllinear judging unit 153, for according to described historical data sample, judges whether there is multicollinearity between described production management and control index.If not, then the first performance element 155 is performed; If so, the second performance element 157 is then performed.

For reducing the problem causing the distortion of linear relationship model between production management and control index because of multicollinearity, guarantee the validity of linear relationship model, according to described historical data sample, condition number is utilized to judge whether there is multicollinearity between production management and control index.

Wherein in an embodiment, if screened n item historical data sample number, p item production management and control index, has formed production management and control index matrix X ₀for:

$X_0=\left[\begin{array}{cccc}{x}_1& x_2& \mathrm{...}& x_p\end{array}\right]=\left[\begin{array}{cccc}{x}_{11}& x_{12}& \mathrm{...}& x_{1p}\\ x_{21}& x_{22}& \mathrm{...}& x_{2p}\\ .& .& & .\\ .& .& \mathrm{...}& .\\ .& .& & .\\ x_{n1}& x_{n2}& \mathrm{...}& x_{np}\end{array}\right]\mathrm{......}\left(2\right)$

Wherein, x ₁1st production management and control index historical data column vector; x _pit is p item production management and control index historical data column vector.

Square formation X ₀ ^tx ₀conditional number be:

$C_d=\frac{{\mathrm{\&lambda;}}_{max}}{{\mathrm{\&lambda;}}_{min}}\mathrm{......}\left(3\right)$

Wherein, λ _maxand λ _minbe respectively square formation X ₀ ^tx ₀minimum and maximum characteristic root.Due to the mensurable square formation X of conditional number ₀ ^tx ₀characteristic root dispersion level, so can judge whether to there is multicollinearity by conditional number.

When conditional number belongs to the first preset range, then there is not multicollinearity.In the present embodiment, the first preset range is: 0<C _d<100.

When conditional number belongs to the second preset range, then there is multicollinearity.In the present embodiment, the second preset range is: 100≤C _d.Wherein, there is not common range in the second preset range and the first preset range.

First performance element 155, for adopting least squares estimate determination regression coefficient according to described historical data sample.

The regression coefficient column vector that least squares estimate is determined is:

β＝(X ₀ ^TX ₀) ^-1X ₀ ^TY ₀……(4)

Wherein, X ₀ ^trepresent X ₀transposed matrix, subscript (X ₀ ^tx ₀) ^-1represent X ₀ ^tx ₀inverse matrix

Second performance element 157, for adopting ridge analysis method to draw ridge mark figure according to described historical data sample, the described production management and control index of screening for predicting, determines regression coefficient further; And the described linear relationship model of described production management and control index and described pre-arranged power off time is re-established according to the described production management and control index of screening further.

Due to when judging to find that production management and control index exists multicollinearity, | X ₀ ^tx ₀| ≈ 0, least squares estimate lost efficacy.Dimension, adopts ridge regression (RidgeRegression) method of estimation, to X ₀ ^tx ₀an additional positive constant matrices kI (k>0, I are unit matrix), then determine that regression coefficient column vector is:

$\widehat{\mathrm{\&beta;}}\left(k\right)={({X_0}^T{X}_0+kI)}^{-1}{X_0}^T{Y}_0\mathrm{......}\left(5\right)$

Wherein, k is ridge parameter.

Determination about ridge parameter: as k=0, adopts the regression coefficient that ride regression estimater method is determined equal the regression coefficient β that conventional least squares estimate is determined.When ridge parameter k changes in (0 ,+∞), regression coefficient for the function of k, obtain change curve, be ridge mark figure.By the analysis to ridge mark figure, can do production management and control index and further judge and screening, and determine suitable ridge parameter k value.

The screening principle of production management and control index is as follows: 1) when ridge parameter k=0, numerical value is very large; K increases slightly, be tending towards 0 rapidly, show x _jvery little on the impact of pre-arranged power off time, can by x _jrejected.2) with in the mark figure of ridge, both sums are substantially constant, show production management and control index x _jand x _j'there is stronger linear dependence, only can retain wherein one.3) not by k variable effect remain on the occasion of or negative value, represent the quality of data not good, can by x _jrejected.Wherein, it is the regression coefficient of jth item production management and control index when taking k as ridge parameter.

Time prediction module 170, for described regression coefficient being substituted in described linear relationship model, determines the predicted value of described pre-arranged power off time.

Continue referring to Fig. 4, wherein in an embodiment, also comprise:

Index normalizing module 120, for being normalized described production management and control index according to described historical data sample.

In order to eliminate the difference that there is dimension between production management and control index, need being normalized every production management and control index, obtain the matrix after normalization.In the present embodiment, normalized specifically can comprise: centralization unit, and for carrying out centralization process to historical data sample, namely each element of every production management and control index historical data column vector deducts the average of column vector, as formula (7); Standardisation Cell, for carrying out standardization, namely each element of every production management and control index historical data column vector deducts the average of column vector, then divided by the standard deviation of column vector, as formula (8).

${\stackrel{\&OverBar;}{x}}_j=\frac{1}{n}\underset{i=1}{\overset{n}{\&Sigma;}}{x}_{ij}\mathrm{......}\left(6\right)$

${{\mathrm{\&sigma;}}_j}^2=\frac{1}{n-1}\underset{i=1}{\overset{n}{\&Sigma;}}{(x_{ij}-{\stackrel{\&OverBar;}{x}}_j)}^2\mathrm{......}\left(7\right)$

${\hat{x}}_j=\frac{x_j-{\stackrel{\&OverBar;}{x}}_j}{{\mathrm{\&sigma;}}_j}\mathrm{......}\left(8\right)$

Wherein, i=1,2 ..., n, n be historical data sample number; J=1,2 ..., p, p be the item number of production management and control index.

Above-mentioned pre-arranged power off time prognoses system, acquisition module 110 obtains that screen, for predicting production management and control index, and obtains historical data sample; Model building module 130, according to described production management and control index, sets up the linear relationship model of production management and control index and pre-arranged power off time; Coefficient determination module 150 is according to described historical data sample determination regression coefficient; Described regression coefficient substitutes in described linear relationship model by time prediction module 170, determines the predicted value of described pre-arranged power off time; Wherein, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.This system considers many-sided production management and control factors such as deliverability, network structure level, operation and management level, equipment technology level and capital construction investment in technological upgrading, the predicted value of its pre-arranged power off time determined more reliably, more reasonable.

Above embodiment only have expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but therefore can not be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make multiple distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.

Claims (10)

1. a pre-arranged power off time Forecasting Methodology, is characterized in that, comprises step:

Obtain that screen, for predicting production management and control index, and obtain historical data sample;

According to described production management and control index, set up the linear relationship model of described production management and control index and pre-arranged power off time;

According to described historical data sample determination regression coefficient;

Described regression coefficient is substituted in described linear relationship model, determines the predicted value of described pre-arranged power off time;

Wherein, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.

2. pre-arranged power off time Forecasting Methodology according to claim 1, is characterized in that,

Described load service capability class index comprises the heavy Overflow RateHT of distribution transforming, the heavy Overflow RateHT of the heavy Overflow RateHT of circuit and transformer station; Described network structure horizontal class index comprises distribution line looped network rate, distribution line segments, distribution line wire diameter not Service Efficiency and the monotropic rate of single line; Described operation and management level class index comprises time delay and stops power transmission rate, hot line job rate, defect elimination rate and repeat outage rate; The horizontal class index of described equipment technology comprises distribution line and can turn for rate and robotization coverage rate; Described capital construction investment in technological upgrading class index comprises capital fund.

3. pre-arranged power off time Forecasting Methodology according to claim 1, is characterized in that, after the step of described acquisition production management and control index, described determine the step of linear relationship model before, also comprise step:

According to described historical data sample, described production management and control index is normalized.

4. pre-arranged power off time Forecasting Methodology according to claim 1, is characterized in that, the described step according to historical data sample determination regression coefficient, comprising:

According to described historical data sample, judge whether there is multicollinearity between described production management and control index;

If not, least squares estimate determination regression coefficient is adopted according to described historical data sample;

If so, adopt ridge analysis method to draw ridge mark figure according to described historical data sample, the described production management and control index of screening for predicting, determines regression coefficient further; And the described linear relationship model of described production management and control index and described pre-arranged power off time is re-established according to the described production management and control index of screening further.

5. pre-arranged power off time Forecasting Methodology according to claim 4, is characterized in that,

Describedly judge that the step that whether there is multicollinearity between production management and control index is specially, according to described historical data sample, utilize condition number to judge whether there is multicollinearity between production management and control index.

6. a pre-arranged power off time prognoses system, is characterized in that, comprising:

Acquisition module, for obtain screening, production management and control index for predicting, and obtain historical data sample;

Model building module, for according to described production management and control index, sets up the linear relationship model of described production management and control index and pre-arranged power off time;

Coefficient determination module, for according to described historical data sample determination regression coefficient;

Time prediction module, for described regression coefficient being substituted in described linear relationship model, determines the predicted value of described pre-arranged power off time;

Wherein, described production management and control index comprises load service capability class index, network structure horizontal class index, operation and management level class index, the horizontal class index of equipment technology and capital construction investment in technological upgrading class index.

7. pre-arranged power off time prognoses system according to claim 6, is characterized in that,

Described load service capability class index comprises the heavy Overflow RateHT of distribution transforming, the heavy Overflow RateHT of the heavy Overflow RateHT of circuit and transformer station; Described network structure horizontal class index comprises distribution line looped network rate, distribution line segments, distribution line wire diameter not Service Efficiency and the monotropic rate of single line; Described operation and management level class index comprises time delay and stops power transmission rate, hot line job rate, defect elimination rate and repeat outage rate; The horizontal class index of described equipment technology comprises distribution line and can turn for rate and robotization coverage rate; Described capital construction investment in technological upgrading class index comprises capital fund.

8. pre-arranged power off time prognoses system according to claim 6, is characterized in that, also comprise:

Index normalizing module, for being normalized described production management and control index according to described historical data sample.

9. pre-arranged power off time prognoses system according to claim 6, it is characterized in that, described coefficient determination module, comprising:

Conllinear judging unit, for according to described historical data sample, judges whether there is multicollinearity between described production management and control index;

First performance element, if for there is not multicollinearity between described production management and control index, adopts least squares estimate determination regression coefficient according to described historical data sample;

Second performance element, if for there is multicollinearity between described production management and control index, adopt ridge analysis method to draw ridge mark figure according to described historical data sample, the described production management and control index of screening for predicting, determines regression coefficient further; And the described linear relationship model of described production management and control index and described pre-arranged power off time is re-established according to the described production management and control index of screening further.

10. pre-arranged power off time prognoses system according to claim 9, is characterized in that,

Described conllinear judging unit, also for according to described historical data sample, utilizes condition number to judge whether there is multicollinearity between production management and control index.