CN109816145A - A kind of supply load management data platform - Google Patents
A kind of supply load management data platform Download PDFInfo
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- CN109816145A CN109816145A CN201811574974.5A CN201811574974A CN109816145A CN 109816145 A CN109816145 A CN 109816145A CN 201811574974 A CN201811574974 A CN 201811574974A CN 109816145 A CN109816145 A CN 109816145A
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Abstract
A kind of supply load management data platform shows that optimization module, customer charge prediction module, user power utilization quality evaluation display module, typical trade power consumption model analysis module and basic data supporting module form by map.Present invention optimizes existing map exhibition method, building load forecasting model, profound levels to portray user model, depth excavates trade power consumption behavior, the research of basic data support；The present invention further refines the information such as load, the electricity consumption of 10KV and the above large user, excavates more effective informations, enriches the displaying content of negative control system；And the present invention can provide theories integration for electric power enterprise load prediction, peak load shifting work, and power consumer can be made to understand the method for load prediction and the part throttle characteristics of each typical industry in depth, there is certain reference significance for users.
Description
Technical field
The present invention relates to information system management field more particularly to a kind of supply load management applied in electric system
Data platform.
Background technique
Currently, Utilities Electric Co. of the national grid in each city/area is provided with load management center, the load management
Center is equipped with negative control display systems data platform, and the display systems data platform is using business as core, with senior business expert
Depth is carried out by the information on load of the high-quality electricity consumption user to ten thousand family of city mileage using big data technology for resources advantage
It excavates, analysis, show, the electrical characteristics such as visualization, three-dimensional enterprise, region, industry, depth analysis excavates each side's electricity consumption row
To realize that collection business monitoring, business integration, user guidance, Governments set up the stage Deng Duo circle one show, improving national grid
Window effect also provides data analysis for the Power marketing management mode of other electricity consumptions user and ensures.
And with the deep development of power business, the negative control display systems data platform under the prior art has been unable to meet prison
The demand of control, analysis, presently, there are following problems:
(1) in the negative control display systems data platform under the prior art, user's map is carried out by GPS map information
It shows, system cannot find out the distribution density of user from map exhibition window, be unfavorable for Utilities Electric Co. and carry out large user's load
Prediction and analysis；
(2) negative control display systems data platform shown showing interface under the prior art daily electricity of user, day are negative
Lotus, daily electricity compare, daily load compares, day peak load data, are not carried out the displaying to future load prediction, cannot be tune
Degree analysis provides effective decision support；
(3) the negative control display systems data platform under the prior art illustrates the basic electricity information of user, and no pair
User carries out deep analysis, such as the quality evaluation of user, the base profile of user are perfect, data analysis of user etc.；
(4) the negative control display systems data platform under the prior art is without the analysis to typical industry, relative to ordinary user
The voltage class of load, large user's load is higher, and power load is very big, and load fluctuation has very big randomness.Certain representative rows
The load of industry has obvious feature, it is necessary to and their part throttle characteristics is analyzed in the electricity consumption behavior for excavating these typical industries,
The displaying content of Customer Quality is formed, electric power enterprise is helped to carry out load prediction and analysis；
(5) the shown content of negative control display systems data platform under the prior art is dull, shows only some basis letters
Breath, excavation, refinement and displaying without carrying out depth.
It is controlled at present for electric loading in conclusion the negative control display systems data platform under the prior art can no longer meet
Demand, now there is an urgent need to a kind of novel data platforms to solve the above problems.
Summary of the invention
In order to solve the problems, such as above-mentioned status present in the negative control display systems data platform under the prior art and, the present invention
A kind of supply load management data platform, one kind of the invention are provided for the purpose of further enriching displaying, analysis content
Supply load management data platform is to carry with the negative big screen display system in control center based on GIS technology, big data technology
Body carries out excavation, analysis and the displaying of depth by the negative control electricity consumption data to 10KV and the above big customer, final to realize: ground
Figure shows mode, the power consumer load forecasting model based on big data and the method for optimization, larger power user client's solid mould
Type building, typical industry model analysis displaying and basic data support research.
A kind of supply load management data platform of the invention, specific composition and method are as described below:
A kind of supply load management data platform, it is characterised in that:
The supply load management data platform shows optimization module, customer charge prediction module, user by map
Power quality evaluates display module, typical trade power consumption model analysis module and basic data supporting module composition；
The map show optimization module according to thermodynamic chart concept, thermodynamic chart drafting and GIS-Geographic Information System in heat
Try hard to relevant information, in conjunction with map exhibition method, construct heating power map, realize the Orientation on map of 10KV and the above large user with
Distribution is shown；
Power consumer load prediction of the customer charge prediction module based on big data, using in demand response system
Prediction model the following 7 days loads of user are predicted；
The structure that the user power utilization quality evaluation display module passes through collection, client information model to customer data
It builds, establishes evaluation points, sketches the contours user model, from multi-angle, the multi-level power quality model for portraying user, realize to user
Accurate effective classification, and the synchronous analysis for realizing user model is shown；
The characteristics of typical trade power consumption model analysis module is according to large user's load variations study large user's load
Characteristic carries out typical industry mould on the basis of based on furtheing investigate to the industries part throttle characteristics such as steel, cement, chemical fibre, papermaking
Type shows analysis；
The basic data supporting module is entered an item of expenditure in the accounts number with power load management system, scheduling system and marketing CMS system
Based on, further the information such as load, the electricity consumption of 10KV and the above large user are refined, excavate more effective informations,
Further enrich the displaying content of negative control system.
A kind of supply load management data platform according to the present invention, which is characterized in that the map shows optimization
The specific method is as follows for module:
2.1) map shows that optimization module is distributed with color change rendering large user, specifically: it is used greatly using color
The rendering of family distribution density, the local color burn more than quantity show, and few local of quantity is shown with light color, not data with white
Color is shown, according to the visual development logic of people, using progressive color rendering means, one mesh of area for concentrating large user
It is clear；
2.2) map shows typical user's Orientation on map of optimization module, specifically: as user is mobile or scaling ground
Figure, corresponding region user density distribution also can Dynamically Announce, with map it is continuous amplify, it can be achieved that single typical user
Orientation on map.
A kind of supply load management data platform according to the present invention, which is characterized in that the customer charge prediction
The specific method is as follows for module:
3.1) calculation process: prediction Load Calculation Method uses the daily load method of average, which includes 3 steps
It is rapid: data selection, data processing, modified result, as follows:
3.11) data select: when predict load day be working day when, should from prediction the same day select forward 10 days history bear
For lotus as the typical day for calculating prediction load, historical load does not include nonworkdays；When predicting load day is nonworkdays, answer
Selected the historical load of corresponding 4 days periods as the typical day for calculating prediction load forward from the prediction same day, historical load is not wrapped
Include working day；
3.12) data processing: according to selection typical day 96 point load average data, as prediction 96 point load of day
Value, as uncorrected prediction daily load；
3.13) modified result: prediction day-load regulating factor calculation method are as follows: with 18 DEG C for median, typical daily temperature
The factor=MAX (| the highest temperature-median |, | the lowest temperature-median |), the prediction daily temperature factor=MAX (| the highest temperature-
Median |, | the lowest temperature-median |), Dynamic gene=1+ (the prediction daily temperature factor-typical case's daily temperature factor)/100 is adjusted
Integral divisor value is limited to 0.8-1.2, and threshold value variableization adjustment, user in predicting load should be according to calculated uncorrected prediction
Load is obtained multiplied by Dynamic gene；
3.2) following one week load tendency of typical user is predicted, using the prediction of all average loads, all peaks load
The mode that prediction, all paddy load predictions and the prediction of daily 96 point load combine is shown, specific as follows:
3.21) current 7 days daily actual average loads of the typical user and 7 days futures all average load predictions: are shown
Daily prediction average load；
3.22) all peak load forecastings: switching shows following 7 days peak load forecasting values；
3.23) all paddy load predictions: switching shows following 7 days peak load forecasting values；
3.24) daily 96 point load prediction: any day average load is taken, peak load, paddy load can directly obtain the day
96 point load values and show.
A kind of supply load management data platform according to the present invention, which is characterized in that the user power utilization quality
Display module combination disturbance degree, part throttle characteristics, electricity consumption credit, growth four class factor of characteristic are evaluated, establishes maximum monthly load, the moon is used
Electricity, monthly load factor, power factor, the examination of contract limit, load factor, maximum monthly load on year-on-year basis, the moon year-on-year 8 fingers of electricity consumption
Evaluation points are marked, user model indicator evaluation system is constructed respectively and user model is shown, specific as follows:
4.1) user model indicator evaluation system is constructed:
4.11) maximum monthly load calculates:
All user's maximum monthly load values for participating in ranking are ranked up, it is maximum to rank the first, it is minimum the last,
This month is not involved in ranking without the user of electricity consumption, specifically, peak load is worth maximum highest scoring, it is 20 points；It is of that month useless
Electricity, be 0 point；There is electricity consumption in this month, but it is 5 points that peak load, which is worth minimum,；Other users then according to peak load ranking, use
Differential technique carries out scoring calculating, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring；Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 20；N is the user locating ranking at present, and m is total participation ranking amount；
4.12) moon electricity consumption calculates:
All users month electricity consumption for participating in ranking is ranked up, electricity consumption is maximum to rank the first, and electricity consumption is minimum
The last, the of that month user without electricity consumption is not involved in ranking, specifically, the moon maximum highest scoring of electricity consumption, is 20 points；When
The moon without electricity consumption, is 0 point, is not involved in ranking；There is electricity consumption in this month, but it is 5 points that electricity consumption is minimum；Other users are according to moon electricity consumption
Ranking is measured, scoring calculating is carried out using differential technique, calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 20；N is the user locating ranking at present, and m is total participation ranking amount；
4.13) monthly load factor calculates:
All user's monthly average rate of load condensates for participating in ranking are ranked up, monthly average rate of load condensate is maximum to rank the first,
Monthly average rate of load condensate minimum is the last, specifically, the maximum highest scoring of monthly average rate of load condensate, is 25 points；Monthly average load
The minimum score of rate is minimum, is 0 point；Other users carry out scoring calculating according to monthly average rate of load condensate ranking, using differential technique, meter
Calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
If user's scoring is F, F calculation method is as follows:
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, the value
It is 25；N is the user locating ranking at present, and m is total participation ranking amount；
4.14) power factor calculates:
The power factor of all users for participating in ranking is ranked up, power factor is maximum to rank the first, power because
Number is minimum the last, specifically, the maximum highest scoring of power factor, is 5 points；The minimum score of power factor is minimum, is 0
Point；Other users carry out scoring calculating according to power factor size ranking, using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, the value
It is 5；N is the user locating ranking at present, and m is total participation ranking amount；
4.15) contract limit examining computation:
The contract limit examination absolute value for all users for participating in ranking is ranked up according to numerical values recited, absolute value is most
Small ranks the first, maximum absolute value it is the last, specifically, contract limit examine the smallest highest scoring of absolute value, be 5
Point；Of that month useless electrographic recording, the examination of contract limit is 0 point；Of that month useful electrographic recording, but contract limit examination absolute value is most
Big scoring is 2 points；Other users examine order of magnitude ranking according to contract limit, carry out scoring calculating using differential technique,
Calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: if user's scoring is F, F calculation method is as follows: F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >
=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 2；Fmax is the highest score of this, the value
It is 5；N is the user locating ranking at present, and m is total participation ranking amount；
4.16) load factor calculates:
Load factor is greater than 1 user, is 0 point；Load factor is more than or equal to 0.8 user for being less than or equal to 1, this score value is most
Height is 5 points；Remaining user carries out scoring calculating using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (A-Amin)/(Amax-Amin)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, the value
It is 5；A is that the user works as monthly load factor, and Amin is to participate in load factor minimum value in ranked users, and Amax is to participate in ranked users
Load factor maximum value；
4.17) maximum monthly load calculates on year-on-year basis:
All user's maximum monthly loads for participating in ranking are ranked up on year-on-year basis, maximum monthly load maximum ranking the on year-on-year basis
One, maximum monthly load is minimum the last on year-on-year basis, specifically, maximum monthly load maximum highest scoring on year-on-year basis, is 10 points；When
The moon useless electrographic recording, the year-on-year score 0 of maximum monthly load is divided；Of that month useful electrographic recording, but being worth the smallest scoring is 5 points；Its
His user carries out scoring calculating using differential technique, calculation method is as follows according to the year-on-year ranking of maximum monthly load:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 10；N is the user locating ranking at present, and m is total participation ranking amount；
4.18) moon, electricity consumption calculated on year-on-year basis:
All users month electricity consumption for participating in ranking is ranked up on year-on-year basis, the moon electricity consumption it is maximum on year-on-year basis rank the first,
Month electricity consumption is minimum the last on year-on-year basis, specifically, moon electricity consumption maximum highest scoring on year-on-year basis, is 10 points；It is of that month useless
Electrographic recording, the moon the year-on-year score 0 of electricity consumption divide；Of that month useful electrographic recording, but being worth the smallest scoring is 5 points；Other users root
According to moon electricity consumption ranking, scoring calculating is carried out using differential technique, calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 10；N is the user locating ranking at present, and m is total participation ranking amount；
4.2) user model is shown, include the following:
4.21) user's evaluation is shown:
The user model evaluation result of typical user is shown, main presentation content includes:
4.21a) typical user's overall score, system ranking, industry ranking；
4.21b) individual event pixel evaluation result show: including index value, score value, score value last month, compared on score value last month
Decline situation, index ranking are risen, it is more than 5% user in industry which, which is subject to,；
4.22) files on each of customers is shown:
4.22a) user base file data: showing the base profile data of typical user, shows that content user is believed substantially
Breath, negative control acquisition facility information, bill information；
4.22b) user's Orientation on map: actual location situation of the user on map can be inquired；
4.22c) user picture: gate, antenna are shown, totally 4 photos at terminal and substation；
4.23) data analysis is shown:
4.23a) load trend analysis: tendency is carried out to the year of typical user total power load and monthly total power load
Analysis and year, monthly load trend analysis function；
4.23b) the year-on-year ring of load is than analysis: the year-on-year ring of optimization load is clear by histogram mode than analysis exhibition method
It is clear to show year-on-year ring than analysis result；
4.23c) the year-on-year ring of daily electricity is than analysis: realizing that the year-on-year ring of typical user's daily electricity than analysis, passes through histogram
Mode clearly shows year-on-year ring than analysis result；
4.23d) daily electricity is shown: being shown to peak electricity, paddy electricity amount, flat 1 electricity, flat 2 electricity, is stacked figure pair using column
The daily electricity service condition of typical user is shown, wherein a complete piling post represents the total electricity of the ammeter.
A kind of supply load management data platform according to the present invention, which is characterized in that the typical trade power consumption
Model analysis module analysis illustrate industry contribution rate, daily load trend analysis, the year-on-year ring of daily load than analysis, daily load rate,
Day peak-valley difference, industry user score statistics, customer charge ranking list, user's scoring ranking list, specific as follows:
5.1) industry contribution rate:
It is equally divided into basis with all users of industry, contribution degree ranking is carried out to all industries, based on ranking, meter
It is calculated more than how many industry；
5.2) daily load trend analysis:
The 96 point load tendencies on the typical industry same day are analyzed, and are shown with curve form；
5.3) the year-on-year ring of daily load is than analysis:
The average load on the typical industry same day and the per day load of yesterday, last month, the same period last year are compared and analyzed,
The percentage for establishing the sector same day average load rise/fall by comparison, so that grasping typical case's industry same day load makes
Rationality；
5.4) peak load is analyzed:
Show daily load rate, peak load, paddy load and the day peak-valley difference of typical industry；
5.5) typical industry heating power map:
Orientation on map is carried out to current typical industry user by heating power map, is rendering hand with color when map reduces
Section, the intensive local color burn of user show that the local color of user's rareness desalinates display；And it can be to single when map amplification
User carries out locating and displaying, which can grasp the distribution situation of user on the whole；
5.6) industry user, which scores, counts:
The scoring situation of all users in current industry is counted, all types of user's accountings are established；
5.7) user's scoring ranking list:
All users in current industry are ranked up according to scoring, show preceding 15~20 users that score, and arranged
Name is compared with scoring last month, shows that ranking goes up or decline situation；
5.8) customer charge ranking list:
All users in current industry are ranked up according to per day load height, are shown 15~20 before per day load
Name user, and its ranking and scoring yesterday are compared, show that its ranking goes up or decline situation.
A kind of supply load management data platform according to the present invention, which is characterized in that the basic data support
Module specifically includes following:
6.1) customer charge fundamentals of forecasting data:
Based on power load management system, by data pick-up, basic data needed for customer charge is predicted is obtained, it is main
Wanting data content includes: voltage, current information, information on load, metering device information；
6.2) user's daily load basic data:
Based on OMS scheduling system, by data pick-up, obtains required daily load basic data and 96 points of the whole network negative
Lotus, total electricity, peak electricity, flat 1 electricity, flat 2 electricity and paddy electricity amount, and on this basis, can by the peak-valley difference that is calculated,
The related datas such as rate of load condensate, peak load, paddy load and average load；
6.3) user model basic data:
Based on data enter an item of expenditure in the accounts by the CMS that markets, by data pick-up, data needed for negative control is shown are obtained, in key data
Hold includes user basic information, negative control acquisition facility information, monthly bill.
A kind of supply load management data platform of the invention is concluded are as follows:
(1) optimize map exhibition method, realize that the map of 10KV and the above large user are shown:
Existing map exhibition method is advanced optimized, it is real by progressive rendering mode based on heating power map
The Orientation on map of existing 10KV and the above large user are shown；
(2) load forecasting model is constructed, realizes the prediction and displaying of load:
Using load forecasting model technology, the factors such as temperature, time are considered, break through load and show limitation, to user's future
Load behavior precisely predicted, provide significantly more efficient decision support for lexical analysis；
(3) user's evaluation system is constructed, profound level portrays user model:
The index factor and score-system of user power utilization feature are characterized by establishing, and construct user model appraisement system, from
Multi-angle, the accurate effective classification used electric model, realize to user for portraying user at many levels, and synchronous realization user model
Analysis is shown；
(4) industry dimensional analysis is expanded, three-dimensional trade power consumption characteristic:
Industry dimension electrical energy consumption analysis is expanded, the use electrical characteristics of three-dimensional industry user are realized and are based on enterprise, region, industry
The electricity consumption behavior aggregate analysis of equal various dimensions, depth excavate each side's electricity consumption behavior；
(5) further docking marketing CMS system, abundant data show content:
Based on data enter an item of expenditure in the accounts by negative control acquisition system and marketing CMS system, further to 10KV and the above large user
The information such as load, electricity consumption are refined, and more effective informations are excavated, and further enrich the displaying content of negative control system.
Using a kind of supply load management of the invention with data platform obtain it is following the utility model has the advantages that
1. a kind of supply load management of the invention with data platform optimize existing map exhibition method, building load it is pre-
Survey model, profound level portrays user model, depth excavates trade power consumption behavior, the research of basic data support；
2. a kind of supply load management of the invention with data platform further to the load of 10KV and the above large user, use
The information such as electricity are refined, and more effective informations are excavated, and enrich the displaying content of negative control system；
3. a kind of supply load management data platform of the invention can work for electric power enterprise load prediction, peak load shifting
Theories integration is provided, power consumer can be made to understand the method for load prediction and the part throttle characteristics of each typical industry in depth, to
There is certain reference significance for family.
Detailed description of the invention
Fig. 1 is that a kind of map of supply load management data platform of the invention shows the simulation signal of optimization module
Figure；
Fig. 2 is a kind of simulation signal of the customer charge prediction module of supply load management data platform of the invention
Figure；
Fig. 3 is a kind of the negative of the user power utilization quality evaluation display module of supply load management data platform of the invention
The simulation schematic diagram that lotus trend analysis is shown；
Fig. 4 is a kind of mould of the user power utilization quality evaluation display module of supply load management data platform of the invention
Quasi- schematic diagram；
Fig. 5 is a kind of use of the user power utilization quality evaluation display module of supply load management data platform of the invention
The simulation schematic diagram that family essential information is shown；
Fig. 6 is a kind of mould of the typical trade power consumption model analysis module of supply load management data platform of the invention
Quasi- schematic diagram.
Specific embodiment
A kind of supply load management of the invention is further retouched with data platform with reference to the accompanying drawings and examples
It states.
Embodiment
(it should be noted that these attached drawings are data platform simulation schematic diagram, information is simulation effect in figure as shown in Fig. 1~Fig. 6
For reference, actual functional capability is not limited only to the figure to fruit), a kind of supply load management data platform, by map show optimization module,
Customer charge prediction module, user power utilization quality evaluation display module, typical trade power consumption model analysis module and basic data
Supporting module composition；
Map show optimization module according to thermodynamic chart concept, thermodynamic chart drafting and GIS-Geographic Information System in thermodynamic chart phase
Information is closed, in conjunction with map exhibition method, constructs heating power map, realizes Orientation on map and the distribution exhibition of 10KV and the above large user
Show；
Power consumer load prediction of the customer charge prediction module based on big data, using the prediction in demand response system
Model predicts the following 7 days loads of user；
Building, the establishment that user power utilization quality evaluation display module passes through collection, client information model to customer data
Evaluation points sketch the contours user model, from multi-angle, the multi-level power quality model for portraying user, realize to the accurate of user
Effectively classification, and the synchronous analysis for realizing user model is shown；
The characteristics of typical trade power consumption model analysis module is according to large user's load variations study large user's part throttle characteristics,
On the basis of based on furtheing investigate to the industries part throttle characteristics such as steel, cement, chemical fibre, papermaking, typical business models exhibition is carried out
Show analysis；
Basic data supporting module enters an item of expenditure in the accounts data as base using power load management system, scheduling system and marketing CMS system
Plinth further refines the information such as load, the electricity consumption of 10KV and the above large user, excavates more effective informations, further
Enrich the displaying content of negative control system.
As shown in Figure 1, map shows optimization module, the specific method is as follows:
Map shows that optimization module is distributed with color change rendering large user, specifically: utilize color to carry out large user point
Cloth density renders, and the local color burn more than quantity is shown, the few place of quantity is shown with light color, and data is not aobvious with white
Show, according to the visual development logic of people, using progressive color rendering means, one mesh of area for concentrating large user
So；
Map shows typical user's Orientation on map of optimization module, specifically: it is right as user is mobile or scaling map
Answer the user density in region be distributed also can Dynamically Announce, as the continuous amplification of map is, it can be achieved that ground to single typical user
Figure positioning.
As shown in Figures 2 and 3, the specific method is as follows for customer charge prediction module:
Calculation process: prediction Load Calculation Method uses the daily load method of average, which includes 3 steps:
Data selection, data processing, modified result, as follows:
Data select: when predict load day be working day when, should from predict the same day select 10 days historical loads to make forward
For the typical day for calculating prediction load, historical load does not include nonworkdays；It, should be from pre- when predicting load day is nonworkdays
The survey same day selects forward the historical load of corresponding 4 days periods as the typical day for calculating prediction load, and historical load does not include work
Make day；
Data processing: according to selection typical day 96 point load average data, as prediction 96 point load value of day, i.e.,
For uncorrected prediction daily load；
Modified result: prediction day-load regulating factor calculation method are as follows: with 18 DEG C for median, the typical daily temperature factor=
MAX (| the highest temperature-median |, | the lowest temperature-median |), the prediction daily temperature factor=MAX (| the highest temperature-median
|, | the lowest temperature-median |), Dynamic gene=1+ (the prediction daily temperature factor-typical case's daily temperature factor)/100, Dynamic gene
Value is limited to 0.8-1.2, threshold value variableization adjustment, and user in predicting load should multiply according to calculated uncorrected prediction load
It is obtained with Dynamic gene；
One week following to typical user load tendency predicts, using the prediction of all average loads, week peak load forecasting,
The mode that all paddy load predictions and the prediction of daily 96 point load combine is shown, specific as follows:
The prediction of all average loads: the daily of current 7 days daily actual average loads of the typical user and 7 days futures is shown
Predict average load；
All peak load forecastings: switching shows following 7 days peak load forecasting values；
All paddy load predictions: switching shows following 7 days peak load forecasting values；
Daily 96 point load prediction: any day average load is taken, peak load, paddy load can directly obtain the 96 of this day
Point load value is simultaneously shown.
As shown in Figure 4 and Figure 5, user power utilization quality evaluation display module combination disturbance degree, part throttle characteristics, electricity consumption credit,
Growth four class factor of characteristic establishes maximum monthly load, moon electricity consumption, monthly load factor, power factor, the examination of contract limit, load
Rate, maximum monthly load on year-on-year basis, the moon year-on-year 8 metrics evaluation factors of electricity consumption, construct user model indicator evaluation system respectively
It is shown with user model, specific as follows:
Construct user model indicator evaluation system:
Maximum monthly load calculates:
All user's maximum monthly load values for participating in ranking are ranked up, it is maximum to rank the first, it is minimum the last,
This month is not involved in ranking without the user of electricity consumption, specifically, peak load is worth maximum highest scoring, it is 20 points；It is of that month useless
Electricity, be 0 point；There is electricity consumption in this month, but it is 5 points that peak load, which is worth minimum,；Other users then according to peak load ranking, use
Differential technique carries out scoring calculating, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring；Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 20；N is the user locating ranking at present, and m is total participation ranking amount；
Moon electricity consumption calculating:
All users month electricity consumption for participating in ranking is ranked up, electricity consumption is maximum to rank the first, and electricity consumption is minimum
The last, the of that month user without electricity consumption is not involved in ranking, specifically, the moon maximum highest scoring of electricity consumption, is 20 points；When
The moon without electricity consumption, is 0 point, is not involved in ranking；There is electricity consumption in this month, but it is 5 points that electricity consumption is minimum；Other users are according to moon electricity consumption
Ranking is measured, scoring calculating is carried out using differential technique, calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 20；N is the user locating ranking at present, and m is total participation ranking amount；
Monthly load factor calculates:
All user's monthly average rate of load condensates for participating in ranking are ranked up, monthly average rate of load condensate is maximum to rank the first,
Monthly average rate of load condensate minimum is the last, specifically, the maximum highest scoring of monthly average rate of load condensate, is 25 points；Monthly average load
The minimum score of rate is minimum, is 0 point；Other users carry out scoring calculating according to monthly average rate of load condensate ranking, using differential technique, meter
Calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
If user's scoring is F, F calculation method is as follows:
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, the value
It is 25；N is the user locating ranking at present, and m is total participation ranking amount；
Power factor calculates:
The power factor of all users for participating in ranking is ranked up, power factor is maximum to rank the first, power because
Number is minimum the last, specifically, the maximum highest scoring of power factor, is 5 points；The minimum score of power factor is minimum, is 0
Point；Other users carry out scoring calculating according to power factor size ranking, using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, the value
It is 5；N is the user locating ranking at present, and m is total participation ranking amount；
Contract limit examining computation:
The contract limit examination absolute value for all users for participating in ranking is ranked up according to numerical values recited, absolute value is most
Small ranks the first, maximum absolute value it is the last, specifically, contract limit examine the smallest highest scoring of absolute value, be 5
Point；Of that month useless electrographic recording, the examination of contract limit is 0 point；Of that month useful electrographic recording, but contract limit examination absolute value is most
Big scoring is 2 points；Other users examine order of magnitude ranking according to contract limit, carry out scoring calculating using differential technique,
Calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: if user's scoring is F, F calculation method is as follows: F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >
=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 2；Fmax is the highest score of this, the value
It is 5；N is the user locating ranking at present, and m is total participation ranking amount；
Load factor calculates:
Load factor is greater than 1 user, is 0 point；Load factor is more than or equal to 0.8 user for being less than or equal to 1, this score value is most
Height is 5 points；Remaining user carries out scoring calculating using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (A-Amin)/(Amax-Amin)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, the value
It is 5；A is that the user works as monthly load factor, and Amin is to participate in load factor minimum value in ranked users, and Amax is to participate in ranked users
Load factor maximum value；
Maximum monthly load calculates on year-on-year basis:
All user's maximum monthly loads for participating in ranking are ranked up on year-on-year basis, maximum monthly load maximum ranking the on year-on-year basis
One, maximum monthly load is minimum the last on year-on-year basis, specifically, maximum monthly load maximum highest scoring on year-on-year basis, is 10 points；When
The moon useless electrographic recording, the year-on-year score 0 of maximum monthly load is divided；Of that month useful electrographic recording, but being worth the smallest scoring is 5 points；Its
His user carries out scoring calculating using differential technique, calculation method is as follows according to the year-on-year ranking of maximum monthly load:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 10；N is the user locating ranking at present, and m is total participation ranking amount；
Month electricity consumption calculates on year-on-year basis:
All users month electricity consumption for participating in ranking is ranked up on year-on-year basis, the moon electricity consumption it is maximum on year-on-year basis rank the first,
Month electricity consumption is minimum the last on year-on-year basis, specifically, moon electricity consumption maximum highest scoring on year-on-year basis, is 10 points；It is of that month useless
Electrographic recording, the moon the year-on-year score 0 of electricity consumption divide；Of that month useful electrographic recording, but being worth the smallest scoring is 5 points；Other users root
According to moon electricity consumption ranking, scoring calculating is carried out using differential technique, calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, the value
It is 10；N is the user locating ranking at present, and m is total participation ranking amount；
User model displaying, include the following:
User's evaluation is shown:
The user model evaluation result of typical user is shown, main presentation content includes:
Typical user's overall score, system ranking, industry ranking；
Individual event pixel evaluation result show: including index value, score value, score value last month, compared with score value rise and fall last month
Situation, index ranking, it is more than 5% user in industry which, which is subject to,；
Files on each of customers is shown:
User base file data: showing the base profile data of typical user, shows content user essential information, negative control
Acquire facility information, bill information；
User's Orientation on map: actual location situation of the user on map can be inquired；
User picture: gate, antenna are shown, totally 4 photos at terminal and substation；
Data analysis is shown:
Load trend analysis: to typical user year total power load and monthly total power load carry out trend analysis with
And year, monthly load trend analysis function；
The year-on-year ring of load is than analysis: the year-on-year ring of optimization load is clearly showed than analysis exhibition method by histogram mode
Year-on-year ring is than analysis result；
The year-on-year ring of daily electricity is than analysis: realize the year-on-year ring ratio analysis of typical user's daily electricity, it is clear by histogram mode
It is clear to show year-on-year ring than analysis result；
Daily electricity is shown: being shown to peak electricity, paddy electricity amount, flat 1 electricity, flat 2 electricity, is stacked figure using column and use typical case
The daily electricity service condition at family is shown, wherein a complete piling post represents the total electricity of the ammeter.
(related for chemical industry in the present embodiment) typical trade power consumption model analysis module analysis illustrates as shown in Figure 6
Industry contribution rate, daily load trend analysis, the year-on-year ring of daily load score than analysis, daily load rate, day peak-valley difference, industry user and unite
Meter, customer charge ranking list, user's scoring ranking list, specific as follows:
Industry contribution rate:
It is equally divided into basis with all users of industry, contribution degree ranking is carried out to all industries, based on ranking, meter
It is calculated more than how many industry；
Daily load trend analysis:
The 96 point load tendencies on the typical industry same day are analyzed, and are shown with curve form；
The year-on-year ring of daily load is than analysis:
The average load on the typical industry same day and the per day load of yesterday, last month, the same period last year are compared and analyzed,
The percentage for establishing the sector same day average load rise/fall by comparison, so that grasping typical case's industry same day load makes
Rationality；
Peak load analysis:
Show daily load rate, peak load, paddy load and the day peak-valley difference of typical industry；
Typical industry heating power map:
Orientation on map is carried out to current typical industry user by heating power map, is rendering hand with color when map reduces
Section, the intensive local color burn of user show that the local color of user's rareness desalinates display；And it can be to single when map amplification
User carries out locating and displaying, which can grasp the distribution situation of user on the whole；
Industry user, which scores, to be counted:
The scoring situation of all users in current industry is counted, all types of user's accountings are established；
User's scoring ranking list:
All users in current industry are ranked up according to scoring, show preceding 15~20 users that score, and arranged
Name is compared with scoring last month, shows that ranking goes up or decline situation；
Customer charge ranking list:
All users in current industry are ranked up according to per day load height, are shown 15~20 before per day load
Name user, and its ranking and scoring yesterday are compared, show that its ranking goes up or decline situation.
Basic data supporting module specifically includes following:
Customer charge fundamentals of forecasting data:
Based on power load management system, by data pick-up, basic data needed for customer charge is predicted is obtained, it is main
Wanting data content includes: voltage, current information, information on load, metering device information；
User's daily load basic data:
Based on OMS scheduling system, by data pick-up, obtains required daily load basic data and 96 points of the whole network negative
Lotus, total electricity, peak electricity, flat 1 electricity, flat 2 electricity and paddy electricity amount, and on this basis, can by the peak-valley difference that is calculated,
The related datas such as rate of load condensate, peak load, paddy load and average load；
User model basic data:
Based on data enter an item of expenditure in the accounts by the CMS that markets, by data pick-up, data needed for negative control is shown are obtained, in key data
Hold includes user basic information, negative control acquisition facility information, monthly bill.
A kind of supply load management of the invention optimizes existing map exhibition method, building load prediction with data platform
Model, profound level portray user model, depth excavates trade power consumption behavior, the research of basic data support；The present invention is further
The information such as load, electricity consumption to 10KV and the above large user are refined, and more effective informations are excavated, and enrich the exhibition of negative control system
Show content；And the present invention can provide theories integration for electric power enterprise load prediction, peak load shifting work, and power consumer can be made deep
Enter to understand the method for load prediction and the part throttle characteristics of each typical industry, there is certain reference significance for users.
Claims (6)
1. a kind of supply load management data platform, it is characterised in that:
The supply load management data platform shows optimization module, customer charge prediction module, user power utilization by map
Quality evaluation display module, typical trade power consumption model analysis module and basic data supporting module composition；
The map show optimization module according to thermodynamic chart concept, thermodynamic chart drafting and GIS-Geographic Information System in thermodynamic chart
Relevant information constructs heating power map in conjunction with map exhibition method, realizes Orientation on map and the distribution of 10KV and the above large user
It shows；
Power consumer load prediction of the customer charge prediction module based on big data, using pre- in demand response system
Model is surveyed to predict the following 7 days loads of user；
The user power utilization quality evaluation display module by the building of collection, client information model to customer data, really
Vertical evaluation points sketch the contours user model, from multi-angle, the multi-level power quality model for portraying user, realize the essence to user
Quasi- effectively classification, and the synchronous analysis for realizing user model is shown；
The characteristics of typical trade power consumption model analysis module is according to large user's load variations, research large user's load are special
Property, on the basis of based on furtheing investigate to the industries part throttle characteristics such as steel, cement, chemical fibre, papermaking, carry out typical business models
Show analysis；
The basic data supporting module is with power load management system, scheduling system and marketing CMS system data of entering an item of expenditure in the accounts
Basis further refines the information such as load, the electricity consumption of 10KV and the above large user, excavates more effective informations, into one
Step enriches the displaying content of negative control system.
2. a kind of supply load management data platform as described in claim 1, which is characterized in that the map shows excellent
Changing module, the specific method is as follows:
2.1) map shows that optimization module is distributed with color change rendering large user, specifically: utilize color to carry out large user point
Cloth density renders, and the local color burn more than quantity is shown, the few place of quantity is shown with light color, and data is not aobvious with white
Show, according to the visual development logic of people, using progressive color rendering means, one mesh of area for concentrating large user
So；
2.2) map shows typical user's Orientation on map of optimization module, specifically: it is right as user is mobile or scaling map
Answer the user density in region be distributed also can Dynamically Announce, as the continuous amplification of map is, it can be achieved that ground to single typical user
Figure positioning.
3. a kind of supply load management data platform as described in claim 1, which is characterized in that the customer charge is pre-
Surveying module, the specific method is as follows:
3.1) calculation process: prediction Load Calculation Method uses the daily load method of average, which includes 3 steps:
Data selection, data processing, modified result, as follows:
3.11) data select: when predict load day be working day when, should from predict the same day select 10 days historical loads to make forward
For the typical day for calculating prediction load, historical load does not include nonworkdays；It, should be from pre- when predicting load day is nonworkdays
The survey same day selects forward the historical load of corresponding 4 days periods as the typical day for calculating prediction load, and historical load does not include work
Make day；
3.12) data processing: according to selection typical day 96 point load average data, as prediction 96 point load value of day,
As uncorrected prediction daily load；
3.13) modified result: prediction day-load regulating factor calculation method are as follows: with 18 DEG C for median, the typical daily temperature factor
=MAX (| the highest temperature-median |, | the lowest temperature-median |), the prediction daily temperature factor=MAX (| the highest temperature-centre
Value |, | the lowest temperature-median |), Dynamic gene=1+ (prediction the daily temperature factor-typical case daily temperature factor)/100, adjust because
Sub- value is limited to 0.8-1.2, and threshold value variableization adjustment, user in predicting load should be according to calculated uncorrected prediction load
It is obtained multiplied by Dynamic gene；
3.2) one week following to typical user load tendency is predicted, using the prediction of all average loads, week peak load forecasting,
The mode that all paddy load predictions and the prediction of daily 96 point load combine is shown, specific as follows:
3.21) all average load predictions: the every of current 7 days daily actual average loads of the typical user and 7 days futures is shown
Day prediction average load；
3.22) all peak load forecastings: switching shows following 7 days peak load forecasting values；
3.23) all paddy load predictions: switching shows following 7 days peak load forecasting values；
3.24) daily 96 point load prediction: any day average load is taken, peak load, paddy load can directly obtain the 96 of this day
Point load value is simultaneously shown.
4. a kind of supply load management data platform as described in claim 1, which is characterized in that the user power utilization matter
Amount evaluation display module combination disturbance degree, part throttle characteristics, electricity consumption credit, growth four class factor of characteristic, establish maximum monthly load, the moon
Electricity consumption, monthly load factor, power factor, the examination of contract limit, load factor, maximum monthly load on year-on-year basis, the moon year-on-year 8 of electricity consumption
The metrics evaluation factor, constructs user model indicator evaluation system respectively and user model is shown, specific as follows:
4.1) user model indicator evaluation system is constructed:
4.11) maximum monthly load calculating:
All user's maximum monthly load values for participating in ranking are ranked up, it is maximum to rank the first, it is minimum the last, it is of that month
The user of no electricity consumption is not involved in ranking, specifically, peak load is worth maximum highest scoring, it is 20 points；This month without electricity consumption,
It is 0 point；There is electricity consumption in this month, but it is 5 points that peak load, which is worth minimum,；Other users are then according to peak load ranking, using difference
Method carries out scoring calculating, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring；Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, which is
20；N is the user locating ranking at present, and m is total participation ranking amount；
4.12) moon electricity consumption calculates:
All users month electricity consumption for participating in ranking is ranked up, electricity consumption is maximum to rank the first, electricity consumption minimum ranking
Finally, the of that month user without electricity consumption is not involved in ranking, specifically, the moon maximum highest scoring of electricity consumption, is 20 points；Of that month nothing
Electricity consumption, it is 0 point, is not involved in ranking；There is electricity consumption in this month, but it is 5 points that electricity consumption is minimum；Other users are arranged according to moon electricity consumption
Name, carries out scoring calculating using differential technique, calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, which is
20；N is the user locating ranking at present, and m is total participation ranking amount；
4.13) monthly load factor calculates:
All user's monthly average rate of load condensates for participating in ranking are ranked up, monthly average rate of load condensate is maximum to rank the first, and the moon is flat
Equal rate of load condensate minimum is the last, specifically, the maximum highest scoring of monthly average rate of load condensate, is 25 points；Monthly average rate of load condensate is most
Low score is minimum, is 0 point；Other users carry out scoring calculating, calculating side according to monthly average rate of load condensate ranking, using differential technique
Method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
If user's scoring is F, F calculation method is as follows:
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, which is
25；N is the user locating ranking at present, and m is total participation ranking amount；
4.14) power factor calculates:
The power factor for all users for participating in ranking is ranked up, power factor is maximum to rank the first, and power factor is most
Small ranking is finally, specifically, the maximum highest scoring of power factor, is 5 points；The minimum score of power factor is minimum, is 0 point；
Other users carry out scoring calculating according to power factor size ranking, using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, which is 5；
N is the user locating ranking at present, and m is total participation ranking amount；
4.15) contract limit examining computation:
The contract limit examination absolute value for all users for participating in ranking is ranked up according to numerical values recited, absolute value is the smallest
Rank the first, maximum absolute value it is the last, specifically, contract limit examine the smallest highest scoring of absolute value, be 5 points；
Of that month useless electrographic recording, the examination of contract limit is 0 point；Of that month useful electrographic recording, but contract limit examines maximum absolute value
Scoring be 2 points；Other users examine order of magnitude ranking according to contract limit, carry out scoring calculating using differential technique, count
Calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: if user's scoring is F, F calculation method is as follows: F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 2；Fmax is the highest score of this, which is 5；
N is the user locating ranking at present, and m is total participation ranking amount；
4.16) load factor calculates:
Load factor is greater than 1 user, is 0 point；Load factor is more than or equal to 0.8 user for being less than or equal to 1, this score value highest is 5
Point；Remaining user carries out scoring calculating using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (A-Amin)/(Amax-Amin)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 0；Fmax is the highest score of this, which is 5；
A is that the user works as monthly load factor, and Amin is to participate in load factor minimum value in ranked users, and Amax is to participate in loading in ranked users
Rate maximum value；
4.17) maximum monthly load calculates on year-on-year basis:
All user's maximum monthly loads for participating in ranking are ranked up on year-on-year basis, maximum monthly load is maximum on year-on-year basis to rank the first,
Maximum monthly load is minimum the last on year-on-year basis, specifically, maximum monthly load maximum highest scoring on year-on-year basis, is 10 points；It is of that month
Useless electrographic recording, the year-on-year score 0 of maximum monthly load is divided；Of that month useful electrographic recording, but being worth the smallest scoring is 5 points；Other
User carries out scoring calculating according to the year-on-year ranking of maximum monthly load, using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, which is
10；N is the user locating ranking at present, and m is total participation ranking amount；
4.18) moon, electricity consumption calculated on year-on-year basis:
All users month electricity consumption for participating in ranking is ranked up on year-on-year basis, the moon electricity consumption it is maximum on year-on-year basis rank the first, the moon is used
Electricity is minimum the last on year-on-year basis, specifically, moon electricity consumption maximum highest scoring on year-on-year basis, is 10 points；This month is remembered without electricity consumption
Record, the moon the year-on-year score 0 of electricity consumption divide；Of that month useful electrographic recording, but being worth the smallest scoring is 5 points；Other users are according to the moon
Electricity consumption ranking carries out scoring calculating using differential technique, and calculation method is as follows:
F=Fmin+ (Fmax-Fmin) * (1- (n-1)/m) (n >=1)
Wherein: F is user's scoring, and Fmin is the minimum score of this, which is 5；Fmax is the highest score of this, which is
10；N is the user locating ranking at present, and m is total participation ranking amount；
4.2) user model is shown, include the following:
4.21) user's evaluation is shown:
The user model evaluation result of typical user is shown, main presentation content includes:
4.21a) typical user's overall score, system ranking, industry ranking；
4.21b) individual event pixel evaluation result show: including index value, score value, score value last month, compared with score value last month rise under
Situation, index ranking drop, and it is more than 5% user in industry which, which is subject to,；
4.22) files on each of customers is shown:
4.22a) user base file data: showing the base profile data of typical user, shows content user essential information, bears
Control acquisition facility information, bill information；
4.22b) user's Orientation on map: actual location situation of the user on map can be inquired；
4.22c) user picture: gate, antenna are shown, totally 4 photos at terminal and substation；
4.23) data analysis is shown:
4.23a) load trend analysis: trend analysis is carried out to the year of typical user total power load and monthly total power load
And year, monthly load trend analysis function；
4.23b) the year-on-year ring of load is than analysis: the year-on-year ring of optimization load is clearly opened up than analysis exhibition method by histogram mode
Now year-on-year ring is than analysis result；
4.23c) the year-on-year ring of daily electricity is than analysis: realizing that the year-on-year ring of typical user's daily electricity than analysis, passes through histogram mode
Clearly show year-on-year ring than analysis result；
4.23d) daily electricity is shown: being shown to peak electricity, paddy electricity amount, flat 1 electricity, flat 2 electricity, is stacked figure to typical case using column
The daily electricity service condition of user is shown, wherein a complete piling post represents the total electricity of the ammeter.
5. a kind of supply load management data platform as described in claim 1, which is characterized in that the typical industry is used
The analysis of electric model analysis module illustrates the year-on-year ring of industry contribution rate, daily load trend analysis, daily load than analysis, daily load
Rate, day peak-valley difference, industry user's scoring statistics, customer charge ranking list, user's scoring ranking list, specific as follows:
5.1) industry contribution rate:
It is equally divided into basis with all users of industry, contribution degree ranking is carried out to all industries and calculates it based on ranking
More than how many industry；
5.2) daily load trend analysis:
The 96 point load tendencies on the typical industry same day are analyzed, and are shown with curve form；
5.3) the year-on-year ring of daily load is than analysis:
The average load on the typical industry same day and the per day load of yesterday, last month, the same period last year are compared and analyzed, established
The percentage of the sector same day average load rise/fall by comparison, to grasp what typical case's industry same day load used
Rationality；
5.4) peak load is analyzed:
Show daily load rate, peak load, paddy load and the day peak-valley difference of typical industry；
5.5) typical industry heating power map:
Orientation on map is carried out to current typical industry user by heating power map, when map reduces, using color as rendering means, is used
The intensive local color burn in family shows that the local color of user's rareness desalinates display；And it can be to sole user when map amplification
Locating and displaying is carried out, which can grasp the distribution situation of user on the whole；
5.6) industry user, which scores, counts:
The scoring situation of all users in current industry is counted, all types of user's accountings are established；
5.7) user's scoring ranking list:
All users in current industry are ranked up according to scoring, show preceding 15~20 users of scoring, and by its ranking and
Last month, scoring compared, and showed that ranking goes up or decline situation；
5.8) customer charge ranking list:
All users in current industry are ranked up according to per day load height, show 15~20 use before per day load
Family, and its ranking and scoring yesterday are compared, show that its ranking goes up or decline situation.
6. a kind of supply load management data platform as described in claim 1, which is characterized in that the basic data branch
Hold mode block specifically includes following:
6.1) customer charge fundamentals of forecasting data:
Based on power load management system, by data pick-up, basic data needed for customer charge is predicted, main number are obtained
It include: voltage, current information, information on load, metering device information according to content；
6.2) user's daily load basic data:
Based on OMS scheduling system, by data pick-up, 96 point load of daily load basic data and the whole network needed for obtaining,
Total electricity, peak electricity, flat 1 electricity, flat 2 electricity and paddy electricity amount, and on this basis, can be by the peak-valley difference that is calculated, negative
The related datas such as lotus rate, peak load, paddy load and average load；
6.3) user model basic data:
Based on data enter an item of expenditure in the accounts by the CMS that markets, by data pick-up, data needed for negative control is shown, primary data content packet are obtained
Include user basic information, negative control acquisition facility information, monthly bill.
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