CN106228296A - Screening preprocess method for the meteorological big data of overhead transmission line load evaluation - Google Patents
Screening preprocess method for the meteorological big data of overhead transmission line load evaluation Download PDFInfo
- Publication number
- CN106228296A CN106228296A CN201610569074.6A CN201610569074A CN106228296A CN 106228296 A CN106228296 A CN 106228296A CN 201610569074 A CN201610569074 A CN 201610569074A CN 106228296 A CN106228296 A CN 106228296A
- Authority
- CN
- China
- Prior art keywords
- data
- interpolation
- screening
- transmission line
- meteorological
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 52
- 230000005540 biological transmission Effects 0.000 title claims abstract description 32
- 238000012216 screening Methods 0.000 title claims abstract description 22
- 238000011156 evaluation Methods 0.000 title claims abstract description 21
- 239000013589 supplement Substances 0.000 claims abstract description 19
- 230000008569 process Effects 0.000 claims abstract description 12
- 241001269238 Data Species 0.000 abstract description 3
- 230000008859 change Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 240000002853 Nelumbo nucifera Species 0.000 description 2
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 2
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 239000000470 constituent Substances 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 239000004575 stone Substances 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000004040 coloring Methods 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000007689 inspection Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000002203 pretreatment Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Theoretical Computer Science (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Complex Calculations (AREA)
Abstract
The invention discloses the screening preprocess method of a kind of meteorological big data for overhead transmission line load evaluation, comprise the following steps: S1, source data is screened；S2, in source data lose the big data of data separate improve interpolation fitting method supplement.The meteorological data affecting power line load ability is screened and denoising by the method, by the pretreatment from original meteorological data, supplement including denoising and interpolation, improve utilization rate and the accuracy of source data, on implementing, calls tool case is programmed, it is achieved get up succinct convenient by MATLAB.The present invention can effectively process screening and the pretreatment of a large amount of meteorological datas for overhead transmission line Load Evaluation, and as the basis of the meteorological big data association relation effectively analyzing overhead transmission line load evaluation.
Description
Technical field
The present invention relates to the technical field of meteorological big data screening pretreatment, bear for overhead transmission line particularly to one
The screening preprocess method of the meteorological big data of lotus assessment.
Background technology
Along with the progress of science and technology, work about electric power personnel are more and more higher to the requirement of load prediction order of accuarcy, it is desirable to internally
The factor of portion and external action carries out comprehensive combing, sets up rational mathematical model, formulates rational computational methods, makes load pre-
Survey conclusion and accomplish fast, smart, thin, accurate, accomplish rational management electric load.Electric load is affected especially weather by various factors
The impact of factor is the most obvious, it is therefore necessary to work out the relation between electric load to each major influence factors and relevant journey
Degree, thus it is greatly promoted the precision of load forecast, it is ensured that power supply and demand reaches balance, no longer occurs that " power cuts to limit consumption " is existing
As.
By the follow-up investigation to electric load, find that the change of meteorological factor affects the change of network load, particularly
Along with the construction of intelligent grid, the universal of intelligent appliance and application and the continuous lifting of people's living standard, to inhabitation
The requirement of environmental degree of comfort is more and more higher, and the change of meteorological factor is the most increasing to electric load change influence degree.Therefore
It is necessary to study the rule that electric load is affected by meteorological factor, thus improves the essence of load forecast to a certain extent
Degree, it is ensured that electric power well serves local economic development, it is provided that electrical network supports reliably, it is provided that the electrical power services of high-quality.
Model to preferably relevant key factor be carried out the process of big data, it is necessary to initial data is carried out one
Fixed correction with supply, and then improve accuracy and the integrity of whole model.Wherein interpolation arithmetic is the distribution according to data
Rule, finds a function expression can connect known each point, and with this function expression predict at 2 between any position
The functional value put.And if able to the method utilizing the multi-dimensional interpolation in interpolation, just can big data meteorological to multidimensional unite
The pretreatment of one, is greatly improved whole modeling efficiency.
Summary of the invention
It is an object of the invention to the shortcoming overcoming prior art with not enough, it is provided that a kind of overhead power transmission specific electric load that is used for is commented
The screening preprocess method of the meteorological big data estimated, carries out denoising and standardization by data big to meteorology, can promote to set up not
With the incidence relation between factor, and then it is effectively improved power line load and monitors in real time and dynamic compatibilization ability.
The purpose of the present invention is achieved through the following technical solutions:
A kind of screening preprocess method of the meteorological big data for overhead transmission line load evaluation, described screening pretreatment
Method comprises the following steps:
S1, screening source data, this step specifically includes:
S101, from source data analyze find out noise data and bad data；
S102, employing similarity method arrange the rational threshold range of data, reject off-limits data；
S2, in source data lose the big data of data separate improve interpolation fitting method supplement, this step
Suddenly specifically include:
S201, analyze from source data and find out the data of loss；
S202, determine regional extent and the relevant parameter carrying out interpolation fitting；
S203, the order of use multi-dimensional interpolation carry out interpolation and supplement, and mend on the basis of the discrete big data of original meteorology
Insert continuous function；
S204, the method using two-dimensional interpolation on each two coordinate axes, be modified, and contrast different parameters and set
Put, select preferred plan；
S205, draw interpolation fitting image；
S206, original image is painted and smooth treatment.
Further, described step S101, from source data, analyze that to find out noise data as follows with bad data detailed process:
By arranging the threshold value of different temperatures, if the scope varying more than threshold value setting between temperature, i.e.Wherein α (t), β (t) are the threshold value arranged, then judge that these data are noise data and bad number
According to.
Further, described step S102, employing similarity method arrange the rational threshold range of data, to going beyond the scope
Data carry out reject detailed process as follows:
For given initial data:
If hadWherein α (t), β (t) are threshold value, then with following formula, to x, (i t) is carried out
Repair:
Wherein
And x (i t) represents the data of i-th day t.
Further, described step S203, using the order of multi-dimensional interpolation to carry out interpolation, to supplement detailed process as follows:
The n in Matlab is used to tie up interpolating function
VI=interpn (X1, X2, X3...V, Y1, Y2, Y3 ..., method)
Source data carries out interpolation supplement.
The present invention has such advantages as relative to prior art and effect:
The screening preprocess method of the meteorological big data for overhead transmission line load evaluation that the present invention proposes, by from
The pretreatment of original meteorological data, supplements including denoising and interpolation, improves utilization rate and the accuracy of source data, specifically
In realization, program calls tool case by MATLAB, it is achieved get up succinct convenient.The present invention can effectively process for overhead power transmission
The screening of a large amount of meteorological datas that line load is evaluated and pretreatment, and as effectively analyzing the gas of overhead transmission line load evaluation
Basis as big data association relation.
Accompanying drawing explanation
Fig. 1 is the screening preprocess method of the meteorological big data for overhead transmission line load evaluation disclosed in the present invention
Circulating step figure；
Fig. 2 (a) is to use MATLAB to realize the schematic diagram that the interpolation of one group of three-dimensional initial data is supplemented；
Fig. 2 (b) be use MATLAB realize one group of three-dimensional initial data interpolation supplement tint after schematic diagram；
Fig. 2 (c) is supplementary the tinting and carry out smooth treatment of interpolation using MATLAB to realize one group of three-dimensional initial data
After schematic diagram；
Fig. 3 (a) is after the data after data prediction carry out the colouring also smooth treatment that three-dimensional interpolation supplements
Schematic diagram；
Fig. 3 (b) is to carry out showing in the projection of bottom surface coordinate after three-dimensional interpolation supplements through the data after data prediction
It is intended to.
Detailed description of the invention
For making the purpose of the present invention, technical scheme and advantage clearer, clear and definite, develop simultaneously embodiment pair referring to the drawings
The present invention further describes.Should be appreciated that specific embodiment described herein, and need not only in order to explain the present invention
In limiting the present invention.
Embodiment
For overwhelming majority area, meteorological factor is the major influence factors affecting overhead transmission line load.Just
Really analyze about between the meteorological big data of overhead power transmission specific electric load and meteorological big data and overhead transmission line data mutual
Incidence relation is significant for the correctness increasing overhead transmission line load evaluation.Affect the gas of overhead power transmission specific electric load
As a lot of because have.In order to calculate between the big data of the meteorology of overhead transmission line load and meteorological data and overhead transmission line greatly
The premise of the interrelated relation of data must be by being to extract meteorological factor key message, such as temperature, humidity, wind direction, wind speed etc.,
And be standardized processing to the data of these key messages, enter simultaneously for overhead transmission line leading indicator and conventional load
Row filter and denoising, just can carry out later stage modeling analysis.
Refer to the sieve that Fig. 1, Fig. 1 are the meteorological big data for overhead transmission line load evaluation disclosed in the present embodiment
Select the circulating step figure of preprocess method.The three-dimensional data representing position and temperature is used to be described in the present embodiment.This
Patent uses interpolation method to supplement initial data, and takes to arrange the method for level threshold value for abnormal data therein
Reject.The screening preprocess method of the meteorological big data for overhead transmission line load evaluation shown in Fig. 1, specifically includes
Following steps:
S1, source data is screened；
This step includes the most again substep:
S101, from source data analyze find out noise data and bad data；
Select by analyzing the region studied and final application, tentatively find out for problem to be studied has
Interference and irrational data.
In concrete application, by arranging the threshold value of different temperatures, if the scope varying more than threshold value setting between temperature, i.e.Wherein α (t), β (t) are the threshold value arranged, then it is assumed that these data are asked for study
Topic is noisy and irrational noise data and bad data.
S102, employing similarity method arrange the rational threshold range of data, reject off-limits data；
In concrete application, the process of this step is as follows:
For given initial data:
If hadWherein α (t), β (t) are threshold value, then need with following formula to x (i, t)
Repair:
Wherein
And x (i t) represents the data of i-th day t.Here coordinate in X-axis, Y-axis representative graph respectively, can pass through
The value of X, Y uniquely determines data-oriented position in the drawings.
S2, in source data lose the big data of data separate improve interpolation fitting method supplement；
This step includes again substep:
S201, analyze from source data and find out the data of loss；
Analyze the region and final research purpose studied, find out and count lacking in problem to be studied
According to this and necessary data.I.e. do not appear in initial data for certain coordinate of research or the meteorological data in region, then recognize
Data for this coordinate or region are the data lost.
S202, determine regional extent and the relevant parameter carrying out interpolation fitting；
In the present embodiment, randomly select one piece of region to illustrate as specific embodiment, after processed as above
Form in Matlab of the concrete numerical value of data lattice as follows:
X=0:400:5600；
Y=0:400:4800；
Z=[370 470 550 600 670 690 670 620 580 450 400 300 100 150 250；...
510 620 730 800 850 870 850 780 720 650 500 200 300 350 320；...
650 760 880 970 1020 1050 1020 830 900 700 300 500 550 480 350；...
740 880 1080 1130 1250 1280 1230 1040 900 500 700 780 750 650 550；...
830 980 1180 1320 1450 1420 1400 1300 700 900 850 840 380 780 750；...
880 1060 1230 1390 1500 1500 1400 900 1100 1060 950 870 900 930
950；...
910 1090 1270 1500 1200 1100 1350 1450 1200 1150 1010 880 1000 1050
1100；...
950 1190 1370 1500 1200 1100 1550 1600 1550 1380 1070 900 1050 1150
1200；...
1430 1430 1460 1500 1550 1600 1550 1600 1600 1600 1550 1500 1500 1550
1550；...
1420 1430 1450 1480 1500 1550 1510 1430 1300 1200 980 850 750 550
500；...
1380 1410 1430 1450 1470 1320 1280 1200 1080 940 780 620 460 370
350；...
1370 1390 1410 1430 1440 1140 1110 1050 950 820 690 540 380 300
210；...
1350 1370 1390 1400 1410 960 940 880 800 690 570 430 290 210 150]；
The most here, X represents in the range of [0,5600], and Y represents that a piece in the range of [0,4800] is random
The region chosen, all splits with 400 for interval, and Z axis data represent that the numerical value of the temperature after conversion is (all with random number
Represent, be replaced according to different real data).
S203, use the order of multi-dimensional interpolation in Matlab to carry out interpolation to supplement, in the discrete big data of original meteorology
On the basis of interpolation continuous function；
Owing to data volume only has limited multiple spot, for obtaining the data in more unknown place then by the multidimensional in Matlab
The order of interpolation carries out interpolation and supplements, interpolation continuous function on the basis of the discrete big data of original meteorology so that this company
The discrete data point that continuous curve negotiating all gives, obtains more unknown place temperature data with this.
On the basis of the main method n dimension interpolation using multi-dimensional interpolation:
VI=interpn (X1, X2, X3...V, Y1, Y2, Y3 ..., method)
S204, the method using two-dimensional interpolation on each two coordinate axes, be modified, and contrast different parameters and set
Put, select preferred plan；
S205, draw interpolation fitting image；
In concrete application, in order to the result making matching is more directly perceived, the Picture function surfc carried by Matlab is in three-dimensional
Space draws the data after interpolation fitting.
S206, original image is painted and smooth treatment so that final result is more smooth and accurate.
Having in application, original image is painted and smooth treatment by this step so that final result more smooth with
Accurately.Simultaneously as shown in Fig. 2 (a), Fig. 2 (b) and Fig. 2 (c), the multi-dimensional interpolation method using different parameters is put into same figure
Compare, be embodied as the Picture function subplot by Matlab carries and implement.
As shown in Fig. 3 (a) and Fig. 3 (b), the data prediction through above-mentioned data big for original meteorology obtains one group
The interpolation of the three-dimensional big data of original meteorology for overhead transmission line load evaluation is supplemented.
Additionally in addition to using above-mentioned interpn function and carrying out interpolation fitting, can be by following for initial data
Process of fitting treatment carries out the pretreatment of initial data, and directly draws the image after data prediction:
[X, Y, Z]=griddata (x, y, z, linspace (min (x), max (x)) ', linspace (min (y), max
(y)),'v4')
Finally using Ndgrid function to produce the grid on n-dimensional space, specifically used form is as follows:
[X1, X2, X3]=ndgrid (x1, x2, x3 ...)
It is generalized to other regions according to above-mentioned denoising and preprocess method and is fitted inspection, the main constituent after carrying out
(PCA) analyze method, and then the meteorological big data association relation for effect analysis overhead transmission line load evaluation lays the first stone.)
Initial data is carried out pretreatment, after eliminating data " noise " and supplying the data lacked, just can enter further
Method is analyzed in main constituent (PCA) after row, and then the meteorological big data association for effect analysis overhead transmission line load evaluation closes
System lays the first stone.
In sum, the screening pretreatment side of the meteorological big data for overhead transmission line load evaluation that the present invention proposes
Method, by the pretreatment from original meteorological data, supplements including denoising and interpolation, improves the utilization rate of source data with accurate
Property, on implementing, program calls tool case by MATLAB, it is achieved get up succinct convenient.The present invention can effectively process use
In the screening of a large amount of meteorological datas and the pretreatment of overhead transmission line Load Evaluation, and bear as effectively analyzing overhead transmission line
The basis of the meteorological big data association relation of lotus assessment.
Above-described embodiment is the present invention preferably embodiment, but embodiments of the present invention are not by above-described embodiment
Limit, the change made under other any spirit without departing from the present invention and principle, modify, substitute, combine, simplify,
All should be the substitute mode of equivalence, within being included in protection scope of the present invention.
Claims (4)
1. the screening preprocess method for the meteorological big data of overhead transmission line load evaluation, it is characterised in that described
Screening preprocess method comprises the following steps:
S1, screening source data, this step specifically includes:
S101, from source data analyze find out noise data and bad data；
S102, employing similarity method arrange the rational threshold range of data, reject off-limits data；
S2, in source data lose the big data of data separate improve interpolation fitting method supplement, this step have
Body includes:
S201, analyze from source data and find out the data of loss；
S202, determine regional extent and the relevant parameter carrying out interpolation fitting；
S203, the order of use multi-dimensional interpolation carry out interpolation and supplement, and on the basis of the discrete big data of original meteorology, interpolation is even
Continuous function；
S204, the method using two-dimensional interpolation on each two coordinate axes, be modified, and contrasts different parameter settings,
Select preferred plan；
S205, draw interpolation fitting image；
S206, original image is painted and smooth treatment.
The screening preprocess method of the meteorological big data for overhead transmission line load evaluation the most according to claim 1,
It is characterized in that, described step S101, from source data, analyze that to find out noise data as follows with bad data detailed process:
By arranging the threshold value of different temperatures, if the scope varying more than threshold value setting between temperature, i.e.Wherein α (t), β (t) are the threshold value arranged, then judge that these data are noise data and bad number
According to.
The screening preprocess method of the meteorological big data for overhead transmission line load evaluation the most according to claim 2,
It is characterized in that, described step S102, employing similarity method arrange the rational threshold range of data, to off-limits data
Carry out rejecting detailed process as follows:
For given initial data:
If hadWherein α (t), β (t) are threshold value, then with following formula, to x, (i t) repaiies
Multiple:
Wherein
And x (i t) represents the data of i-th day t.
The screening preprocess method of the meteorological big data for overhead transmission line load evaluation the most according to claim 1,
It is characterized in that,
Described step S203, using the order of multi-dimensional interpolation to carry out interpolation, to supplement detailed process as follows:
The n in Matlab is used to tie up interpolating function
VI=interpn (X1, X2, X3...V, Y1, Y2, Y3 ..., method)
Source data carries out interpolation supplement.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610569074.6A CN106228296A (en) | 2016-07-18 | 2016-07-18 | Screening preprocess method for the meteorological big data of overhead transmission line load evaluation |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610569074.6A CN106228296A (en) | 2016-07-18 | 2016-07-18 | Screening preprocess method for the meteorological big data of overhead transmission line load evaluation |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN106228296A true CN106228296A (en) | 2016-12-14 |
Family
ID=57531960
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610569074.6A Pending CN106228296A (en) | 2016-07-18 | 2016-07-18 | Screening preprocess method for the meteorological big data of overhead transmission line load evaluation |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106228296A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108133317A (en) * | 2017-12-20 | 2018-06-08 | 长安大学 | A kind of mountainous area highway equals the evaluation method of vertical combination level of security |
| CN111352617A (en) * | 2020-03-16 | 2020-06-30 | 山东省物化探勘查院 | Magnetic method data auxiliary arrangement method based on Fortran language |
| CN112946398A (en) * | 2021-03-04 | 2021-06-11 | 国网浙江省电力有限公司嘉兴供电公司 | Capacity capacity increasing estimation method for power transmission and transformation line combined with meteorological prediction data |
| CN117196353A (en) * | 2023-11-07 | 2023-12-08 | 山东省济宁生态环境监测中心(山东省南四湖东平湖流域生态环境监测中心) | Environmental pollution assessment and monitoring method and system based on big data |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004072900A (en) * | 2002-08-06 | 2004-03-04 | Mitsubishi Heavy Ind Ltd | System and method for electric power network management |
| CN103985065A (en) * | 2014-05-20 | 2014-08-13 | 天津大学 | Method for evaluating electric power system risk based on fault pre-scanning |
-
2016
- 2016-07-18 CN CN201610569074.6A patent/CN106228296A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004072900A (en) * | 2002-08-06 | 2004-03-04 | Mitsubishi Heavy Ind Ltd | System and method for electric power network management |
| CN103985065A (en) * | 2014-05-20 | 2014-08-13 | 天津大学 | Method for evaluating electric power system risk based on fault pre-scanning |
Non-Patent Citations (1)
| Title |
|---|
| 朱倩雯等: "风电场输出功率异常数据识别与重构方法研究", 《电力系统保护与控制》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108133317A (en) * | 2017-12-20 | 2018-06-08 | 长安大学 | A kind of mountainous area highway equals the evaluation method of vertical combination level of security |
| CN108133317B (en) * | 2017-12-20 | 2021-12-14 | 长安大学 | Method for evaluating safety level of horizontal and vertical combination of mountain expressway |
| CN111352617A (en) * | 2020-03-16 | 2020-06-30 | 山东省物化探勘查院 | Magnetic method data auxiliary arrangement method based on Fortran language |
| CN112946398A (en) * | 2021-03-04 | 2021-06-11 | 国网浙江省电力有限公司嘉兴供电公司 | Capacity capacity increasing estimation method for power transmission and transformation line combined with meteorological prediction data |
| CN117196353A (en) * | 2023-11-07 | 2023-12-08 | 山东省济宁生态环境监测中心(山东省南四湖东平湖流域生态环境监测中心) | Environmental pollution assessment and monitoring method and system based on big data |
| CN117196353B (en) * | 2023-11-07 | 2024-02-27 | 山东省济宁生态环境监测中心(山东省南四湖东平湖流域生态环境监测中心) | Environmental pollution assessment and monitoring method and system based on big data |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Luo et al. | Real-time anomaly detection for very short-term load forecasting | |
| CN106228296A (en) | Screening preprocess method for the meteorological big data of overhead transmission line load evaluation | |
| CN109657844B (en) | Electric power short-term load prediction method and device | |
| CN105374209B (en) | A kind of urban area road network running status characteristics information extraction method | |
| CN104715156B (en) | A kind of building energy conservation potentiality dynamic assessment method | |
| CN106650959A (en) | Power distribution network repair ability assessment method based on improved grey clustering | |
| CN106056233A (en) | Power load prediction method | |
| CN105184673A (en) | Power distribution network graph/ model file visualized offline checking method | |
| Tao et al. | Input dimension reduction for load forecasting based on support vector machines | |
| CN106202707A (en) | A kind of structural stress Strength Interference Model set analysis method for reliability based on Lycoperdon polymorphum Vitt confidence interval | |
| Ahmad et al. | Statistical distributions of load profiling data | |
| CN105785954A (en) | Manufacturing system task reliability modeling method based on quality state task network | |
| KR101764758B1 (en) | System and method of anaylzing water-energy-food nexus | |
| CN103745417A (en) | Power grid load characteristic curve analysis method | |
| CN102609259A (en) | Architecture design and evaluation method for basic software platform | |
| Wang et al. | Three-dimensional maturity model of regional power users against the background of the ubiquitous power internet of things | |
| Ji | Analysis of a modified logistic model for describing the growth of durable customer goods in China | |
| Ullah et al. | Industrial Energy Management System: Design of a Conceptual Framework Using IoT and Big Data | |
| CN106451438B (en) | A kind of load setting prediction technique considering intelligent power behavior | |
| CN112926777A (en) | Day-ahead load prediction method and system considering commercial building energy consumption behaviors | |
| CN108154281A (en) | A kind of Gas Purification Factory energy consumption evaluation method and device | |
| CN108108864B (en) | Laboratory quality control data management method | |
| CN104239725A (en) | Dynamic optimal managing method for multisource sensor | |
| Nykamp et al. | Forecast the grid oriented battery operation to enable a multi-use approach and discussion of the regulatory framework | |
| Jiang et al. | Research on multi-type demand response user profile based on improved k-means clustering algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20161214 |