CN106447198A - Power consumption checking method based on business expanding installation data - Google Patents
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
Abstract
The invention discloses a power consumption checking method based on business expanding installation data, and the method comprises the following steps: determining a number of new users and the capacity of new equipment, and calculating the capacity increase rate and the quarter-on-quarter increase rate; building a power utilization trend through an S-shaped growth curve after business expanding installation, obtaining a business expanding power consumption growth curve through a principal component analysis method, taking an instantaneous growth rate as a base value, taking an inflection point as the basis, and carrying out the staged explanation of the curve; carrying out the transformation of power consumption and business expanding installation, carrying out the seasonal decomposition of the explanatory variables and dependent variables through a census X12 algorithm, and decomposing an original component into a trend circulation item (TC), a seasonal factor (SF) and a random item (IR); analyzing the correlation between the trend circulation items (TC) of two variables, and confirming the prediction value of power consumption after the seasonal factors (SF) are considered; comparing the actual power consumption of each user with the prediction value of the power consumption: judging that the user consumes power abnormally if the difference exceeds a threshold value, and marking the user.
Description
Technical field
The present invention relates to a kind of power consumption checking method based on Business Process System data.
Background technology
Due to being affected by national policy, production cost, sales situations, market prospect, industry expansion trend, it is impossible to industry is expanded new
Increase-volume amount, electricity, load are made accurate prediction and are judged.Longer time process is needed as industry expands new clothes, it is impossible to accurately
The release rule for increasing capacity newly is held, when the rule of electricity growth point and electricity reach normal level, the increasing of capacity
Long impact to the whole province's electricity etc..That is, the growth of the actual electricity sales amount of power grid enterprises will often lag behind the growth of Business Process System,
Increase newly and apply to install the growth that capacity also will not absolutely be converted into electricity sales amount.Historical data is counted, finds out newly-increased report
Dress and electricity sales amount increase between relation, contribute to power grid enterprises the growth of following electricity sales amount change is carried out more accurately pre-
Meter.
But at present the correlational study principal concern of Business Process System is the perfect of policy making and management system, seldom
In-depth study is done from measurement technology, mathematical model, algorithm rehearsal, for example the load forecasting method based on S- curve, is based on
Business expands the accuracy for improving the prediction of future 3-5 internal loading of region large electricity consumer's simple, intuitive of information.But to not
Carry out the prediction of several years, if thinking finer being predicted, in addition it is also necessary to carry out monthly analysis.
And accurate predictive value cannot be obtained, cannot just judge electricity consumption user whether normal electricity consumption, if there is power stealing, leakage
The behavior of electricity, normally runs to electric power maintenance and power system and brings larger harm.
Content of the invention
The present invention is in order to solve the above problems, it is proposed that a kind of power consumption checking method based on Business Process System data, should
Method proposes the analysis theory of power sales analyses and prediction index system and overall framework, and has developed market on this basis
Occupy analysis, Market Concentration Ratio, typical user's analysis, the New Set with distinct marketing work characteristic such as tracking applied to install,
To comprehensively, in depth evaluating the overall development situation of power sales.The index system is to realize the standardization of marketing work, section
Huas, efficient providing support.
To achieve these goals, the present invention is adopted the following technical scheme that：
A kind of power consumption checking method based on Business Process System data, comprises the following steps：
(1) determine Add User amount and newly-increased power consumption equipment capacity, calculate capacity rate of increase and sequential growth rate；
(2) the electricity consumption trend after Business Process System being built using S sigmoid growth curve, obtains industry using principle component analysis and expand electricity consumption
Growth curve, with transient growth rate as base value, flex point is basis of characterization, to carry out the deciphering stage by stage of curve；
(3) power consumption and Business Process System are changed, using census X12 algorithm, explanatory variable and dependent variable is entered
Row is seasonal to be decomposed, and original variable is decomposed into trend circulation item (TC), seasonal factor (SF) and random entry (IR)；
(4) dependency between two variable trends items of analysis carries out linear regression again, it is considered to after seasonal factor, confirms electricity consumption
The predictive value of amount；
(5) predictive value by the actual power consumption of each user with its power consumption is compared, if its difference exceedes estimated
Threshold value, then judge that the user has abnormal electricity consumption, which be labeled.
In step (1), quantity and capacity and its rate of increase is increased newly by calculating, to confirm that following electrity market increases
Trend.
In step (1), total numerical quantity is held, while the composition situation of total amount is grasped, from different regions, different electricity consumptions
Classification, different industries, each electric pressure and the multiple angle analysis total amounts of each industry and its constitute the relation of component.
In step (2), from life of Logistic, Gompertz or Von Bertanlanffy curve to power consumption
Long trend is described.
In step (2), power consumption is confirmed based on maximum electricity, parameter, transient growth speed and time scale
Growth tendency.
In step (2), the flex point in curve is exactly the turning point of curvilinear motion speed, and second dervative is zero
Point, which characterizes the variation tendency of curve, when second dervative is zero, reaches respective flex point month, and its corresponding electricity is
Flex point electricity.
In step (3), using the electricity consumption trend curve after Business Process System, in conjunction with instantaneous growth rate and relative growth
Rate, emphasizes the trend characteristic of electricity consumption from growth change rate aspect, with transient growth rate as base value, weighs the wave characteristic of curve.
In step (3), using principle component analysis by different angle analysis variables, using linear combination by original
Beginning information aggregate gets up, and forms aggregate variable that is orthogonal and covering most data information.
In step (3), it is applied to industry and expands growth curve using the electricity of different clients as original variable, carry out timesharing
Between, the two-way extraction of point client, obtain pivot and rise to the typical customers of selection in the overall aspect representated by client, with
Business Process System data are used as explanatory variable, and power consumption is used as dependent variable.
In step (3), it is assumed that it is m that the sample space of data is the index number of s, the observation of each sample, pivot
The mathematical model of analytic process is represented by：
Wherein output matrix y1, y2... ymFor the key message of initial data, it is exactly pivot, coefficient matrices A refers to original
Coefficient of association between variable and pivot, state matrix x1, x2... xmFactor variable, that is, original variable is referred to, is herein referred to
The power consumption of different industries client.
In step (4), power consumption all has certain seasonality with applying to install, using month degree as time observation unit
Time serieses generally there is cyclically-varying in units of year, this is caused due to seasonal factor impact, referred to as season
Property change, analyze objectivity influence factor when, season key element is rejected from former sequence, carries out seasonal adjustment.
In step (5), calculate actual power consumption and the difference of power consumption and the ratio of actual power consumption is estimated, and estimate
Meter threshold range [- 10% ,+10%].
Beneficial effects of the present invention are：
(1) present invention is from numerical quantization, trend deciphering, three kinds of aspects of association analysiss, bent using typical " S " type growth
Electricity growth trend curve after line fitting Business Process System, and electricity trend is expanded using the method analysis industry industry of principle component analysis；
(2) present invention is changed and is decomposed with applying to install to Analyzing Total Electricity Consumption using census X12 algorithm, analysis report
Dressing amount and the association analysiss of Analyzing Total Electricity Consumption, are respectively used to prefectures and cities, different electricity consumption classifications, different industries, each voltage etc.
Level and each industry carry out the analysis of Multi-angle omnibearing, the capacity release rule that quantifies can be obtained and apply to install capacity with
The relation of family electricity, so as to accurately carry out electricity anticipation；
(3) present invention can be compared with actual power consumption according to the anticipation result of electricity, and reasonable judgement is
No have user to carry out the dangerous electricity consumption behaviors such as electric leakage of sneaking current.
Description of the drawings
Fig. 1 is to increase number percent pie chart newly by industry statistic；
Fig. 2 is to increase number percent pie chart newly by electricity consumption classification statistics；
Fig. 3 is to increase number percent pie chart newly by industrial statistics；
Fig. 4 is electricity consumption trend after big commercial power high pressure new clothes；
Fig. 5 (a) is big commercial power transient growth rate schematic diagram；
Fig. 5 (b) is big commercial power relative growth rate schematic diagram；
Fig. 6 is that big commercial power applies to install growth curve；
Fig. 7 is whole industry power consumption and apply to install capacity scatterplot；
Fig. 8 is whole industry power consumption exploded view；
Fig. 9 is that the whole industry applies to install capacity exploded view.
Specific embodiment：
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
Feature analysiss：
1) Add User amount and newly-increased power consumption equipment capacity.Newly-increased quantity and its proportion of total quantity will be accounted for predictive of future
The impetus that power sales increase, also reflects the effect of sales department market development to a certain extent.
Index 1：Newly-increased number percent UR=increases quantity/always newly and increases quantity (1.1) newly
Index 2：Newly-increased capacity rate of increase CR=newly added equipment capacity/total capacity (1.2)
Index 3：New volume reduction amount rate of increase DR=newly subtracts place capacity/total capacity (1.3)
Index 4：Newly-increased capacity sequential growth rate CGR=(newly added equipment capacity-last year newly added equipment capacity)/last year increases newly
Place capacity (1.4)
Index 5：New volume reduction amount sequential growth rate DGR=(newly subtract place capacity-last year newly subtract place capacity)/last year newly subtracts
Place capacity (1.5)
2) total numerical quantity is held, while grasping the composition situation of total amount.Can be from different regions, different electricity consumption classification, no
Electric pressure of the same trade, each and the multiple angle analysis total amounts of each industry and its constitute the relation of component.By such analysis,
Sales department can be expected that the variation tendency of the key indexs such as power sales future income, average price, profit.
1.2.2 trend is understood：
It is widely used in before growth curve describing vegeto-animal growth course, when its correlation analysis is substantially difference
The informix of phase becomes a few parameters.Growth curve can be divided three classes：One is the equation for representing diminishing returns performance, such as refers to
Number function；Another is the smooth S type curve of description, has equation such as Logistic, Gompertz of a fixing flex point, also a class
Smooth S type curve also described, but the variable equation of flex point, such as Von Bertanlanffy.
Electricity consumption trend presents S sigmoid growth curve afterwards Business Process System (based on new clothes) to be completed by the visible client of trend analysiss
Characteristic, obtains industry using principle component analysis and expands electricity consumption growth curve, and with transient growth rate as base value, flex point is basis of characterization, real
The deciphering stage by stage of existing curve, such that it is able to analyze apply to install after the completion of capacity release rule.
1st, S sigmoid growth curve
S type curve is widely used in the analysis of vegeto-animal growth rhythm, is the curve for describing biological growth trend,
Growth curve is, wherein most representative have 3 kinds of curve models：Logistic, Gompertz and Von Bertanlanffy,
Every kind of curve is all Richard curve Qt=α (1- β e-kt)1/(1-γ)Special expression form, its mathematical model and reference index
It is shown in Table 1.1.
Three kinds of nonlinear models of 1.1 Curve of growth fitting of table and its characteristic
Wherein, QtFor electricity；α is maximum electricity；β is parameter；K is transient growth speed；T is time scale, the present invention
With month as cycle.Curvilinear trend is in typical " S " type, and the flex point in curve is exactly the turning point of curvilinear motion speed, and
In mathematical model, second dervative is zero point, can characterize the variation tendency of curve, when second dervative is zero, reaches respective
In flex point month, its corresponding electricity is flex point electricity.The flex point electricity of each model, flex point month and maximum cycle increment
Mathematical model is as shown in table 5.1.Analyze Logistic point of inflexion on a curve electricity be α/2, be the half of maximum electricity, and occur
In (ln β)/k month；Gompertz knee of curve month is consistent with Logistic curve, and flex point electricity is α/e, equivalent to
36.8% α；Von Bertanlanffy curvilinear trend is relatively gentle, flex point month in (3 β of ln)/k, at that time charge value be 8 α/
27, equivalent to 29.6% α.
Expand electricity consumption growth curve characteristic to analyze industry further, except common counter flex point time and flex point value it
Outward, it is also contemplated that other indexs：
1) transient growth rate, embodies emphatically the variation tendency of the speed of growth, withFor basis；
2) relative growth rate, in the unit interval, initial value accounts for the ratio of last value, embodies effective growth of net power consumption.
The trend characteristic of electricity consumption can be emphasized from growth change rate aspect by above index, with transient growth rate as base
Value, weighs the wave characteristic of curve, and the flex point for comparing curve model for base value judges, it is easier to find the inherent law of curve.
2nd, principle component analysis
Principle component analysis are a kind of Multielement statistical analysis methods, multi objective are converted into several aggregative indicatores, by difference
Raw information is gathered by angle analysis variable using linear combination, is formed orthogonal and is covered most numbers
It is believed that the aggregate variable of breath.It is applied to industry and expands growth curve using the electricity of different clients as original variable, carries out between timesharing, divides
The two-way extraction of client, so obtains pivot and the typical customers of selection preferably could be risen to the integral layer representated by client
Come on face.
The sample space of hypothesis data is s, and the index number of the observation of each sample is m, the mathematical modulo of principle component analysis
Type is represented by：
Wherein output matrix y1, y2... ymFor the key message of initial data, it is exactly pivot, coefficient matrices A refers to original
Coefficient of association between variable and pivot, state matrix x1, x2... xmFactor variable, that is, original variable is referred to, is herein referred to
The power consumption of different industries client.
The thought of pivot is exactly to turn parts into the whole, and simplifies, and multidimensional, polytomy variable are integrated according to particular demands,
Realize dimensionality reduction, depression of order.During the asking for of pivot, population variance is constant, and the first pivot refers to the larger unit of variance ratio, and which deviates flat
Weighing apparatus point distance bigger, dispersion degree is bigger, comprising data message more；Second pivot refers to time unit of big variance；Etc.
The like.Then pivot is extracted and a threshold value (as 90%) is set, only to accounting for n pivot of whole variance proportions 90%
Analysis, thus m original aggregation be n index.
1.3.3 association analysiss
The prediction of electricity is the one of which content of load forecast, only carries out careful accurate prediction to electricity
The prediction of other indexs such as electricity, load can be called to provide reference frame for system.Business Process System has for the prediction work of electricity
Leading meaning, is direct acting factor, the relation that both a difinite quality is closed for a long time, it is fixed to realize without effective method
Amount is analyzed and sets up model.
As power consumption all has certain seasonality with applying to install, power consumption is changed with applying to install, the present invention is utilized
Census X12 algorithm carries out seasonal decomposition to explanatory variable and dependent variable.Original variable is decomposed into trend and circulates item
(TC), seasonal factor (SF) and random entry (IR).Linear regression is carried out again by analyzing the dependency between two variable trends items.
After seasonal factor is considered, final predictive value is obtained.Capacity will be applied to install and user's electricity will be connected, and complete to align
True prediction.
As the time serieses of time observation unit, generally there is cyclically-varying in units of year using month degree, this is
Caused due to seasonal factor impact, referred to as seasonal variety.Seasonal fluctuations are not only due to directly affecting for climatic factor, with
When social system and social mores there is also seasonal move.Due to seasonal fluctuation highly significant, it will usually cover developing
Objective law, the analysis and prediction work to us is impacted, it is therefore necessary to remove the impact of seasonal fluctuation, by season key element
Reject from former sequence, carry out seasonal adjustment.
1.3 instance analysis
1.3.1 feature analysiss
1.3.1.1 terrain analysis
A in 2013 saved 17 districts and cities apply to install situation and counted, including newly-increased apply to install amount, existing capacity, increase newly capacity,
New volume reduction amount.Calculate the five indices that introduces in 1.2.1 according to statistical conditions respectively, count in table 1.2.
Table 1.2A saves 17 districts and cities' Business Process System situation statistical tables
Index 1 increases number percent newly：
The quantity that Adds User of five districts and cities of A7, A13, A2, A14 and A6 is most as can be seen from the table, account for total newly-increased
More than half of amount, it is seen that economic growth rate is very fast.
Index 2 increases capacity rate of increase newly：
In the capacity of increase, A6, A2, A17 comprehensively account for existing capacity large percentage for 3 years, and the capacity of increase is more.
The new volume reduction amount rate of increase of index 3：
It can be seen that the volume reduction amount of A6, A4, A12 is relatively more in new volume reduction amount.It can be seen that A6 increase newly volume reduction amount all compare many.
Index 4, index 5 increase/volume reduction amount sequential growth rate newly：
As can be seen from the table, A1, A2, A3, A4, A5 increase capacity newly and increase continuously.
1.3.1.2 category of employment
Table 1.3A saves eight big industry Business Process System situation statistical tables
Index 1 increases number percent newly：
Fig. 1 presses industry statistic and increases number percent pie chart newly
As can be seen from the figure industry accounts for total half for increasing amount newly or so, it is seen that industry saves whole electricity consumption situation in A
Proportion is larger.
Index 2 increases capacity rate of increase newly：
It can be seen that information transfer, Computer Service and software industry increase quickly in the capacity of increase, existing capacity ratio
Larger, the capacity of increase is more.
The new volume reduction amount rate of increase of index 3：
It can be seen that agriculture, forestry, animal husbandry, fisheries and industry are more in the minimizing Capacity Ratio of 3 years in new volume reduction amount, it is seen that the 3rd
Industry development develops more rapid compared with agro-industry.
Index 4, index 5 increase/volume reduction amount sequential growth rate newly：
As can be seen from the table, every profession and trade volume change does not continuously increase and reduces.
1.3.1.3 electricity consumption classification
Table 1.4A saves electricity consumption classification Business Process System situation statistical table
Index 1 increases number percent newly：
Fig. 2 increases number percent pie chart newly by electricity consumption classification statistics
As can be seen from the figure the new clothes situation of general industry and commerce accounts for more than 70 the percent of all electricity consumption classifications, from
Electric situation is it can also be seen that the developing rapidly of general industry and commerce.
Index 2 increases capacity rate of increase newly：
It can be seen that the agricultural production electricity consumption new clothes proportion that accounts for existing capacity is increasing in the capacity of increase, general industrial and commercial
Industry sustainable growth.
The new volume reduction amount rate of increase of index 3：
It can be seen that the capacity that reduces every year of agricultural drainage and irrigation and agricultural production electricity consumption is relative with original total appearance in new volume reduction amount
Amount is a lot.But it is also a lot of that agricultural production electricity consumption increases capacity newly, therefore simply user's variation is more frequent.
Index 4, index 5 increase/volume reduction amount sequential growth rate newly：
As can be seen from the table, only agricultural production was with newly-increased capacity sustainable growth in electrically continuous 2 years.
1.3.1.4 electric pressure
Table 1.5A saves each electric pressure Business Process System situation statistical table
Index 1 increases number percent newly：
10kv new clothes account for the overwhelming majority of all new clothes quantity.
Index 2 increases capacity rate of increase newly：
It can be seen that the equal sustainable growth of the newly-increased capacity of 220kv, 10kv in the capacity of increase.
The new volume reduction amount rate of increase of index 3：
It can be seen that new volume reduction amount 10kv electric pressure is also relatively most in new volume reduction amount, in quantity, feelings are applied to install
It can be seen that 10kv is most active and most complicated in all electric pressures in condition.
Index 4, index 5 increase/volume reduction amount sequential growth rate newly：
As can be seen from the table, without continuous 2 years of electric pressure new/subtract increase-volume amount sustainable growth.Notice 220kv ring
There is mutation than index, be due in data 11 years new volume reduction amounts compare 12,13 years much smaller, it may be possible to because of situation of applying to install by
Prefectures and cities are manually entered has very big random factor.
1.3.1.5 industry is divided
According to industry and the membership relation of industry, primary, secondary and tertiary industries are divided.
The division relation of 1.6 industry of table and every profession and trade
| The primary industry | Agriculture, forestry, animal husbandry and fishery |
| Secondary industry | Industry |
| The tertiary industry | All industries in addition to the industry that the first secondary industry includes |
Table 1.7A is saved with product Business Process System situation statistical table
Index 1 increases number percent newly：
As can be seen from Figure 3 secondary industry new clothes account for all new clothes quantity half in quantity.
It can be seen that the capacity of each industry reduces regularity substantially in new volume reduction amount, but on the whole it can be seen that capacity
Minimizing situation slowed down.
Index 4, index 5 increase/volume reduction amount sequential growth rate newly：
As can be seen from the table, the newly-increased volume reduction amount with regard to the tertiary industry from the point of view of the situation of nearest 3 years all continuously increases,
It can be seen that with economic development, the tertiary industry plays the role for increasingly enlivening in the whole industry.
1.3.2 trend is understood -- apply to install rear power consumption trend analysiss
1st, typical user applies to install rear power consumption trend analysiss
Due to being affected by national policy, production cost, sales situations, market prospect, industry expansion trend, it is impossible to industry is expanded new
Increase-volume amount, electricity, load are made accurate prediction and are judged.Again, industry expands new clothes needs longer time process, current technology
Means and the restriction of decision method, it is impossible to accurately hold and increase the release rule of capacity, the rule of electricity growth point, capacity newly
The impact of growth to the whole province's electricity etc..For new clothes business, client needs after completing to apply to install to carry out each side such as electrical equipment
The debugging in face, can not reach stable use electricity condition at once, and the power consumption of this section of limber up period client is our weights to be analyzed
Point.
For this purpose, the present invention saves the Business Process System data of 2011 to parts of in August, 2014 as foundation with A, with high pressure new clothes industry
Three typical enterprises of big commercial power in data as a example by business, are chosen, analyzes the power consumption rule of a year after its new clothes.By right
The trend analysiss of typical enterprise, as shown in Figure 4：3 typical enterprises complete electricity consumption S-type growth characteristics substantially after new clothes business,
Also comply with the trend of biological growth.
2nd, typical customers industry expands Curve of growth fitting
The present invention chooses the new clothes business that electricity consumption classification is big commercial power, sieve with sales department of A province metric data as comedy
The power consumption of 12 months after power transmission is summarized in choosing, is sorted by electricity, removes the data that customer electricity curve does not meet rule, such as because
It is that the Market Reasons underproduction or policy implication are carried out energy-saving and emission-reduction and cause electricity to decline, is more than on the basis of zero, most by electricity chain rate
After filter out ten typical customers, represent electricity consumption trend after big commercial power new clothes.
Electricity after applying to install to ten clients adopts Logistic, Gompertz and Von Bertanlanffy model, profit
Iteration is circulated with SPSS16.0 statistical analysis software, the Fitting Calculation goes out optimal estimation value A, B of each model parameter, K, convergence
Standard is 10-8, and extrapolates flex point month, flex point electricity and the degree of fitting R2 of model according to estimates of parameters, is shown in Table 1.8.
1.8 growth curves model estimates of parameters of table and degree of fitting
Excessive it can be seen that part garbled data still suffers from certain mistake, possible cause by part flex point month in form
Being the impact power consumption due to mistake or enterprise being manually entered when counting while carrying out the industry expansion project of other classifications, makes part point
Analysis data have obvious careless mistake.But it can be seen that some useful conclusions：In ten clients, there are six clients to Von
Bertanlanffy models fitting degree is preferable, removes flex point month excessive enterprise three and enterprise seven, although each enterprise inclined
Good model is not quite similar, but flex point month is substantially at 3.3 months.
3rd, pivot is extracted
Expand growth curve using SPSS16.0 statistical analysis software to Von Bertanlanffy models fitting industry to lead
Unit extracts.Principal component scores value table can be obtained, is shown in Table 1.9.Then the pivot electricity of the 1-12 month after new clothes is worth to according to fitting
Amount, is shown in Table 1.10.
1.9 principal component scores value table of table
1.10 pivot electricity of table
4th, industrial electric industry expands Curve of growth fitting greatly
Logistic, Gompertz and Von Bertanlanffy models fitting, three kinds of models pair are carried out to pivot electricity
The fitting effect of pivot electricity is all fabulous, and Logistic model-fitting degree is 0.998, Gompertz and Von Bertanlanffy
Model-fitting degree is 1.Von Bertanlanffy model flex point month is April, and flex point electricity is 623.45 ten thousand kwh.For
Von Bertanlanffy model carries out the deciphering of indices, draws transient growth rate and relative growth rate change under the model
Situation, such as Fig. 5 (a), Fig. 5 (b).
Analysis increment percent model understands, Von Bertanlanffy model was in accelerated growth phase before May, located afterwards
In deceleration trophophase.Can be seen which gathers way within accelerated growth phase constantly slowing down, nearby instantaneous rate of increase reaches flex point
Peak value, is not further added by.Constantly decline in deceleration trophophase instantaneous rate of increase and zero is finally may tend to, power consumption tends towards stability.
For the Von Bertanlanffy model that degree of fitting is 1, the growth curve of big commercial power entirety is shown in Fig. 6.Plus
It is the 6-12 month that fast-growing is the 1-5 month, deceleration trophophase for a long time, finally stable in 13,340,000 kwh.Early stage monthly puts into electricity and is respectively
7.94%th, 22.39%, 38.81%, 55.98%, 69.42%.It can be seen that big commercial power just can be realized substantially at five months newly
The release of dressing amount, in terms of electrical equipment debugging, progress is very fast, enters steady statue.
1.3.3 association analysiss -- capacity is applied to install with electricity relation
As power consumption all has certain seasonality with applying to install, power consumption is changed with applying to install, that is, is utilized
Census X12 algorithm carries out seasonal decomposition to explanatory variable and dependent variable.Original variable is decomposed into trend and circulates item
(TC), seasonal factor (SF) and random entry (IR).Linear regression is carried out again by analyzing the dependency between two variable trends items.
After seasonal factor is considered, final predictive value is obtained.Capacity will be applied to install and user's electricity will be connected, and complete to align
True prediction.
Analyses and prediction based on Business Process System whole industry power consumption
Statistics A saves each monthly whole industry of -2014 years first half of the year in 2011 and applies to install capacity and power consumption situation, observes two fingers
Target scatterplot simultaneously finds corresponding relation.
1. pair dependent variable decomposes
By the whole industry power consumption of -2014 years first half of the year in 2011 (Power usage of the whole industry)
Dependent variable QHY is decomposed into trend circulation item QHY_TC, seasonal factor QHY_SF and random using the X12 algorithm in E-view software
Item QHY_IR.As shown in Figure 8.Trend circulation item grows steadily, it can be seen that the expanding economy over time of whole industry power consumption
Constantly it is continuously increased.Seasonal factor assumes low ebb in regular change in 2 months, and this is to be stopped due to the first month of the lunar year whole industry on a large scale
Produce, cause power consumption to decline.
2. pair independent variable decomposes
The whole industry of -2014 years first half of the year in 2011 is applied to install capacity (expansion of the whole industry)
Independent variable EWI is decomposed into trend circulation item EWI_TC, seasonal factor EWI_SF and random using the X12 algorithm in E-view software
Item EWI_IR.As shown in Figure 9.There is slight downward trend from the visible capacity of applying to install of cyclical trend item the second half year in 2012, can
To learn that the whole industry integrally remained basically stable the trend that do not rise appreciably in the second half year in 2012 to new clothes capacity in 2013.At random
Item is followed substantially applies to install curve, and this is that have very strong randomness due to Business Process System itself, adds each electric company's logging data
When there is very big unstable factor.
3. trend circulates item correlation analysiss and linear regression
Independent variable trend circulation item (EWI_TC) and dependent variable trend circulation item (QWI_TC) are carried out correlation analysiss.Meter
Calculate result such as table 1.11.All result of calculations are all significantly correlated under unilateral 0.01 test level.And dependency is all preferable, because
When variable delayed 3 cycles, the dependency of two variables is most strong.
Dependency when most strong be in delayed 6 months of independent variable trend term afterwards.
1.11 liang of variable trends of table circulate item correlation coefficient
Using EWI_TC (- 3) as independent variable, QWI_TC carries out one-variable linear regression as dependent variable.Obtain formula
4.8.1.There is no serial correlation and Singular variance in the residual error of the regression model for obtaining, R2 is higher, can be very good fitted trend
The relation of cyclic variable.In formula, 0.216 can represent increasing quantity coefficient, and constant is represented and can deposit electricity value.
YTC=0.216XTC(-S)+2144598.512
R2=0.885 (1.7)
4. power quantity predicting
The whole industry electricity trend that above-mentioned Equation for Calculating is obtained circulate item match value with decompose the seasonal factor that obtains and
Random entry is summed up, and obtains whole industry electricity match value, and actual comparison, and two curve co-insides degree are very high, and turning point also may be used
Substantially it coincide.
The electricity consumption of in May, 2014 for being obtained by above method fitting is 2986527.214 ten thousand kilowatt hours, and actual power consumption is
3067010.534 ten thousand kilowatt hours, error is 2.62%；June predicts 3019921.841 ten thousand kilowatt hour of power consumption, actual electricity consumption
Measure 3079281.457 kilowatt hours, error 1.93%.As the monthly prediction of short-term forecast, future can be accurately predicted
The electricity of 2-3 month, prediction afterwards should re-start modeling work.
Although the above-mentioned accompanying drawing that combines is described to the specific embodiment of the present invention, not model is protected to the present invention
The restriction that encloses, one of ordinary skill in the art are should be understood that on the basis of technical scheme, and those skilled in the art are not
The various modifications that makes by needing to pay creative work or deformation are still within protection scope of the present invention.
Claims (10)
1. a kind of power consumption checking method based on Business Process System data, is characterized in that：Comprise the following steps：
(1) determine Add User amount and newly-increased power consumption equipment capacity, calculate capacity rate of increase and sequential growth rate；
(2) the electricity consumption trend after Business Process System being built using S sigmoid growth curve, obtains industry using principle component analysis and expand electricity consumption growth
Curve, with transient growth rate as base value, flex point is basis of characterization, to carry out the deciphering stage by stage of curve；
(3) power consumption and Business Process System are changed, using census X12 algorithm, is carried out to explanatory variable and dependent variable season
Section property is decomposed, and original variable is decomposed into trend circulation item, seasonal factor and random entry；
(4) dependency between two variable trends items of analysis carries out linear regression again, it is considered to after seasonal factor, confirms power consumption
Predictive value；
(5) predictive value by the actual power consumption of each user with its power consumption is compared, if its difference exceedes estimates threshold value,
Then judge that the user has abnormal electricity consumption, which is labeled.
2. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(1) in, by calculating newly quantity and capacity and its rate of increase are increased, to confirm trend that following electrity market increases.
3. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(1) in, total numerical quantity is held, while grasp the composition situation of total amount, from different regions, different electricity consumption classifications, different industries, each
Electric pressure and the multiple angle analysis total amounts of each industry constitute the relation of component with which.
4. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(2), in, from Logistic, Gompertz or Von Bertanlanffy curve, the growth tendency of power consumption is described.
5. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(2), in, the growth tendency of power consumption is confirmed based on maximum electricity, parameter, transient growth speed and time scale.
6. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(2) in, the flex point in curve is exactly the turning point of curvilinear motion speed, and the point that second dervative is zero, and which characterizes curve
Variation tendency, when second dervative is zero, reaches respective flex point month, and its corresponding electricity is flex point electricity.
7. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(3) in, using the electricity consumption trend curve after Business Process System, in conjunction with instantaneous growth rate and relative growth rate, from growth change rate layer
The trend characteristic of electricity consumption is emphasized in face, with transient growth rate as base value, weighs the wave characteristic of curve.
8. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(3), in, using principle component analysis by different angle analysis variables, using linear combination, raw information is gathered,
Form aggregate variable that is orthogonal and covering most data information；
In step (3), it is applied to industry and expands growth curve using the electricity of different clients as original variable, carry out between timesharing,
Dividing the two-way extraction of client, obtains pivot and the typical customers of selection are risen in the overall aspect representated by client, with industry
Expanding and data being applied to install as explanatory variable, power consumption is used as dependent variable.
9. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：The step
(3) in, it is assumed that it is m that the sample space of data is the index number of s, the observation of each sample, the mathematical model of principle component analysis
It is represented by：
Wherein output matrix y1, y2... ymFor the key message of initial data, it is exactly pivot, coefficient matrices A refers to original variable
Coefficient of association between pivot, state matrix x1, x2... xmFactor variable, that is, original variable is referred to, herein refers to difference
The power consumption of industry customer.
10. a kind of power consumption checking method based on Business Process System data as claimed in claim 1, is characterized in that：Calculate real
Border power consumption with estimate the difference of power consumption and the ratio of actual power consumption, and estimate threshold range [- 10% ,+10%].
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107067155A (en) * | 2017-02-23 | 2017-08-18 | 武汉烽火技术服务有限公司 | Antitheft electric management system and method based on work order |
| CN107146014A (en) * | 2017-05-02 | 2017-09-08 | 北京中电普华信息技术有限公司 | A kind of industry, which expands, has a net increase of impact analysis method and device of the capacity to electricity sales amount |
| WO2018214629A1 (en) * | 2017-05-25 | 2018-11-29 | 北京中电普华信息技术有限公司 | Electricity sales projection method, device, and computer storage medium |
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| CN112669075A (en) * | 2020-12-30 | 2021-04-16 | 广东电网有限责任公司中山供电局 | Method for checking abnormal fluctuation of electric quantity of electricity customer |
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| CN107067155A (en) * | 2017-02-23 | 2017-08-18 | 武汉烽火技术服务有限公司 | Antitheft electric management system and method based on work order |
| CN107146014A (en) * | 2017-05-02 | 2017-09-08 | 北京中电普华信息技术有限公司 | A kind of industry, which expands, has a net increase of impact analysis method and device of the capacity to electricity sales amount |
| WO2018214629A1 (en) * | 2017-05-25 | 2018-11-29 | 北京中电普华信息技术有限公司 | Electricity sales projection method, device, and computer storage medium |
| CN111819550A (en) * | 2018-03-26 | 2020-10-23 | 华为技术有限公司 | Data processing method and network equipment |
| CN111819550B (en) * | 2018-03-26 | 2022-04-05 | 华为技术有限公司 | Data processing method and network equipment |
| CN112669075A (en) * | 2020-12-30 | 2021-04-16 | 广东电网有限责任公司中山供电局 | Method for checking abnormal fluctuation of electric quantity of electricity customer |
| CN112669075B (en) * | 2020-12-30 | 2022-03-29 | 广东电网有限责任公司中山供电局 | Method for checking abnormal fluctuation of electric quantity of electricity customer |
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