CN113822702B - Inter-industry electricity consumption demand correlation analysis system and method under emergency - Google Patents
Inter-industry electricity consumption demand correlation analysis system and method under emergency Download PDFInfo
- Publication number
- CN113822702B CN113822702B CN202111008294.9A CN202111008294A CN113822702B CN 113822702 B CN113822702 B CN 113822702B CN 202111008294 A CN202111008294 A CN 202111008294A CN 113822702 B CN113822702 B CN 113822702B
- Authority
- CN
- China
- Prior art keywords
- matrix
- candidate
- data
- vector
- module
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000010219 correlation analysis Methods 0.000 title claims abstract description 13
- 230000005611 electricity Effects 0.000 title claims description 19
- 239000011159 matrix material Substances 0.000 claims abstract description 142
- 239000013598 vector Substances 0.000 claims abstract description 106
- 238000013139 quantization Methods 0.000 claims abstract description 63
- 238000012216 screening Methods 0.000 claims abstract description 21
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000004364 calculation method Methods 0.000 claims description 6
- 238000012098 association analyses Methods 0.000 claims description 3
- 238000007418 data mining Methods 0.000 abstract description 5
- 238000005065 mining Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000007781 pre-processing Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- 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
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2216/00—Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F16/00 and subgroups
- G06F2216/03—Data mining
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Strategic Management (AREA)
- Finance (AREA)
- Accounting & Taxation (AREA)
- Development Economics (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Entrepreneurship & Innovation (AREA)
- Economics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Game Theory and Decision Science (AREA)
- General Engineering & Computer Science (AREA)
- Probability & Statistics with Applications (AREA)
- Software Systems (AREA)
- Computational Linguistics (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Human Resources & Organizations (AREA)
- Primary Health Care (AREA)
- Tourism & Hospitality (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a system and a method for correlation analysis of power consumption requirements among industries under emergency events, and relates to the technical field of data mining and big data. Firstly, collecting the power consumption information of each enterprise, the urban population trip intensity, the medical data and other data to form a matrix to form an original data set, and screening and processing the data in the original data set through a data quantization processing module to form a quantization matrix and a weight vector. And forming a candidate matrix by the quantization matrix through the data candidate module, generating an association matrix by the candidate matrix and the weight vector through the support degree generation and screening module, forming a candidate matrix by the association matrix through the data candidate module, and the like until a required association rule is generated.
Description
Technical Field
The invention relates to the technical field of data mining and big data, in particular to a system and a method for correlation analysis of power consumption requirements among industries under emergency.
Background
The association rule mining technology has gradually become one of the most important mining technologies in the field of data mining, and the most direct method for reflecting the association rule between two events is to use the association mining technology. However, the association rule algorithm in the existing research still has large time consumption, and has not been analyzed and applied to the inter-industry electricity consumption requirement under the emergency condition. Along with the development of modernization and informatization of a power system, a large amount of data is generated in the aspects of power consumption, power factor and the like of the power system, the correlation between power consumption requirements among industries is analyzed, and the planning and management of the power system are helped to play a key role.
However, the current association rule algorithm has the following problems: 1. no correlation analysis algorithm specific to the power consumption requirement of the power system exists; 2. the power system has huge data, a large number of candidate item sets can be generated in calculation, and a large amount of time is required to be consumed; 3. multiple scans of the original dataset are required, resulting in frequent I/O usage and increased costs.
Disclosure of Invention
Aiming at the problems existing in the prior art, the invention provides a system and a method for correlation analysis of power consumption requirements among industries under emergency. The association rule mining can be carried out on huge data of the power system through a reasonable algorithm and preprocessing of the data, and the problem of large calculation amount caused by huge data amount in the traditional data mining algorithm is solved.
In order to solve the technical problems, the invention adopts the following technical scheme:
on the one hand, the inter-industry electricity consumption demand association analysis system under the emergency event comprises a data quantization processing module, a data candidate module, a weight vector module, a support degree generation and screening module and an association rule generation module;
the data quantization processing module processes the data of the acquired original data set by adopting an interval weight method to generate a quantization matrix and transmits the quantization matrix to the data candidate module and the weight vector module respectively;
the data candidate module generates a candidate matrix by adopting a correlation combination method, and the weight vector module reduces the complexity of the calculation of the correlation matrix by scanning the quantization matrix and outputs the complexity to the support degree generation and screening module;
the support degree generation and screening module is used for screening vectors meeting the minimum support degree in the candidate matrix, combining the vectors into an association matrix and outputting the association matrix to the association rule generation module;
the association rule generation module outputs inter-industry electricity consumption requirement association under emergency;
on the other hand, the inter-industry electricity consumption demand correlation analysis method under the emergency is realized by the inter-industry electricity consumption demand correlation analysis system under the emergency, and comprises the following steps:
step 1: acquiring power consumption information of enterprises in various industries, urban population trip intensity and medical data to form a matrix to form an original data set; wherein the medical data comprises newly increased suspected cases, confirmed cases, healed people, dead people, accumulated suspected cases, confirmed cases, healed people and dead people;
step 2: scanning an original data set, and carrying out quantization processing on enterprise electricity consumption data and urban population trip intensity data in each industry through a data quantization processing module, namely converting the original data set into a quantization matrix by taking the growth rate of the data as a standard;
step 2.1, set D ij For the original dataset D t Calculating the data growth rate M:
wherein the original data set D t The method comprises the following steps:
step 2.2: let d pn For quantizing the elements in the matrix, d is calculated by interval weighting pn The method comprises the following steps:
step 2.3: by d pn Generating vector I in quantization matrix p ：
I p ＝(d p1 … d pn )
Step 2.4: from vector I p Generating quantization matrix D quantify ：
Step 3, scanning the quantization matrix to generate a weight vector W, and if repeated vectors are found in the process of scanning the quantization matrix, adding one to the corresponding weight and deleting the corresponding vector in the matrix; initial weight vector W 0 Is defined as
W 0 ＝{1,1,…,1}
Step 4: and combining the vectors in the quantization matrix into a candidate matrix according to a correlation degree combination method, wherein the combination method combines the vectors pairwise according to the correlation degree between the vectors.
Step 4.1, set A i (i＝1,2,3…)，B i (i=1, 2,3 …) is the vector I in the quantization matrix u Sum vector I v The element in (2) can calculate the vector I u Sum vector I v The method comprises the following steps:
I u ＝(A 1 A 2 … A n )
I v ＝(B 1 B 2 … B n )
step 4.2: vector I in the quantization matrix u Sum vector I v Corresponding element A in (3) i (i＝1,2,3…)，B i (i=1, 2,3, …) are combined according to a correlation combination method, and the element C in the candidate matrix is calculated n ：
Step 4.3: element C in the candidate matrix to be calculated i (i=1, 2,3 …) are combined into vectors and named I uv Represented by vector I in the quantization matrix u Sum vector I v Is combined.
I uv ＝(C 1 C 2 … C n )
Step 4.4: and combining the vectors in the candidate matrix into the candidate matrix. The candidate matrix is:
D candidate ＝(I 12 I 23 … I uv ) T
step 5: and converting the candidate matrix into an incidence matrix by a support generating and screening module.
Step 5.1; setting the minimum support degree in a support degree generation and screening module;
step 5.2, calculating the support degree of each item in the candidate matrix, wherein the support degree represents the association degree of two items in the candidate matrix, is the product of the weight vector W and the number of corresponding item vectors in the candidate matrix, and is expressed as follows:
sup_d(I uv )＝WI j
wherein sup_d (I uv ) Is vector I uv Is a support of (1).
Step 5.3; deleting the items with the minimum support degree, and forming an association matrix by the rest items;
step 6: the association matrix D is obtained through a data candidate module con-k Generating candidate matrix D candidate-k+1 Connecting every two vectors in the incidence matrix by using a correlation combination method, and removing the redundant items;
step 6.1: set up the association matrix D con-k And vector I therein uv ，I vw The following form:
D con-k ＝[I 12 I 13 … I uv I vw ] T
step 6.2: will associate matrix D con-k The vectors in (2) are combined two by adopting a correlation degree combination method, and the remainder is removed, wherein the remainder is a vector formed by combining K+2 or more quantization matrix vectors.
Step 6.3: will remain vector I uvw The combined matrix is defined as candidate matrix D candidate-k+1 。
D candidate-k+1 ＝(I 123 I 124 … I uvw ) T
Step 7, repeating the step 5 and the step 6 to generate an association matrix and a candidate matrix D candidate-k+1 And obtaining the association rule of the power consumption requirement among industries until all the association matrixes are generated.
The beneficial effects of the invention are as follows:
the invention provides a system and a method for correlation analysis of power consumption requirements among industries under emergency, which have the following beneficial effects:
1. the association rule mining can be carried out on huge data of the power system through a reasonable algorithm and preprocessing of the data, and the problem of large calculation amount caused by huge data amount in the traditional data mining algorithm is solved.
2. The original data set is only required to be scanned once, so that the use times of the I/O are reduced, and the cost is saved.
Drawings
FIG. 1 is a system general flow diagram of the present invention;
FIG. 2 is a program flow diagram of a data quantization processing module of the present invention;
FIG. 3 is a flow chart of a process for generating candidate matrices by a data candidate module for quantization matrices according to the present invention;
FIG. 4 is a flow chart of a process of the support generation and screening module of the present invention;
FIG. 5 is a flow chart of a process for generating candidate matrices from an association matrix by a data candidate module in accordance with the present invention.
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
On the one hand, as shown in fig. 1, the inter-industry electricity consumption demand association analysis system under the emergency event comprises a data quantization processing module, a data candidate module, a weight vector module, a support degree generation and screening module and an association rule generation module;
the data quantization processing module processes the data of the acquired original data set by adopting an interval weight method to generate a quantization matrix and transmits the quantization matrix to the data candidate module and the weight vector module respectively;
the interval weight method is that the original data set is scanned, the data increase rate is 0-10% compared with the previous day, and the corresponding element of the quantization matrix is set to be 1; the data has an increase rate of 10% -50% compared with the previous day, and the corresponding element of the quantization matrix is set to be 2; the data has a growth rate of greater than 50% for the previous day, and the quantization matrix corresponding element is set to 3. The data is-10% -0 compared with the previous day, the corresponding element of the quantization matrix is-1, the data is-50% -10% compared with the previous day, the corresponding element of the quantization matrix is-2, the data is less than-50% compared with the previous day, and the corresponding element of the quantization matrix is-3.
The data candidate module generates a candidate matrix by adopting a correlation combination method, and the weight vector module reduces the complexity of the calculation of the correlation matrix by scanning the quantization matrix and outputs the complexity to the support degree generation and screening module;
the support degree generation and screening module is used for screening vectors meeting the minimum support degree in the candidate matrix, combining the vectors into an association matrix and outputting the association matrix to the association rule generation module; the vector in the candidate matrix meeting the minimum support number indicates that the association between the inter-industry electricity consumption amounts meets the minimum association requirement.
The association rule generation module outputs inter-industry electricity consumption requirement association under emergency;
on the other hand, the inter-industry electricity consumption demand correlation analysis method under the emergency is realized by the inter-industry electricity consumption demand correlation analysis system under the emergency, and comprises the following steps:
step 1: acquiring power consumption information of enterprises in various industries, urban population trip intensity and medical data to form a matrix to form an original data set; wherein the medical data comprises newly increased suspected cases, confirmed cases, healed people, dead people, accumulated suspected cases, confirmed cases, healed people and dead people;
the original data set format in this embodiment is shown in table 1;
table 1: an original dataset matrix table;
step 2: scanning an original data set, and carrying out quantization processing on enterprise electricity consumption data and urban population trip intensity data in each industry through a data quantization processing module, namely converting the original data set into a quantization matrix by taking the growth rate of the data as a standard; as shown in fig. 2;
step 2.1, set D ij For the original dataset D t Calculating the data growth rate M:
wherein the original data set D t The method comprises the following steps:
step 2.2: let d pn For quantizing the elements in the matrix, d is calculated by interval weighting pn The method comprises the following steps:
step 2.3: by d pn Generating vector I in quantization matrix p ：
I p ＝(d p1 … d pn )
Step 2.4: from vector I p Generating quantization matrix D quantify ：
Step 3, scanning the quantization matrix to generate a weight vector W, and if repeated vectors are found in the process of scanning the quantization matrix, adding one to the corresponding weight and deleting the corresponding vector in the matrix; initial weight vector W 0 Is defined as
W 0 ＝{1,1,…,1}
In the present embodiment, if the matrix D is quantized quantify The weight vector W is as follows:
wherein matrix D quantify The first column and the third column vector are equal, the third column of the quantization matrix and the weight vector thereof is deleted, and the corresponding weight in the weight vector is added by one, and the result is that:
step 4: the vectors in the quantization matrix are combined into candidate matrices according to a correlation combining method, as shown in fig. 3, and the combining method combines the vectors two by two according to the correlation degree between the vectors.
Step 4.1, set A i (i＝1,2,3…)，B i (i=1, 2,3 …) is the vector I in the quantization matrix u Sum vector I v The element in (2) can calculate the vector I u Sum vector I v The method comprises the following steps:
I u ＝(A 1 A 2 … A n )
I v ＝(B 1 B 2 … B n )
step 4.2: vector I in the quantization matrix u Sum vector I v Corresponding element A in (3) i (i＝1,2,3…)，B i (i=1, 2,3, …) are combined according to a correlation combination method, and the element C in the candidate matrix is calculated n ：
Step 4.3: element C in the candidate matrix to be calculated i (i=1, 2,3 …) are combined into vectors and named I uv Represented by vector I in the quantization matrix u Sum vector I v Is combined.
I uv ＝(C 1 C 2 … C n )
Step 4.4: and combining the vectors in the candidate matrix into the candidate matrix. The candidate matrix is:
D candidate ＝(I 12 I 23 … I uv ) T
step 5: the candidate matrix is converted to an associated matrix by the support generation and screening module, as shown in fig. 4.
Step 5.1; setting the minimum support degree in a support degree generation and screening module;
step 5.2, calculating the support degree of each item in the candidate matrix, wherein the support degree represents the association degree of two items in the candidate matrix, is the product of the weight vector W and the number of corresponding item vectors in the candidate matrix, and is expressed as follows:
sup_d(I uv )＝WI j
wherein sup_d (I uv ) Is vector I uv Is a support of (1).
Step 5.3; deleting the items with the minimum support degree, and forming an association matrix by the rest items;
step 6: the association matrix D is obtained through a data candidate module con-k Generating candidate matrix D candidate-k+1 As shown in fig. 5, each two vectors in the association matrix are connected by using a correlation combination method, and the rest items are removed;
step 6.1: set up the association matrix D con-k And vector I therein uv ，I vw The following form:
D con-k ＝[I 12 I 13 … I uv I vw ] T
step 6.2: will associate matrix D con-k The vectors in (2) are combined two by adopting a correlation degree combination method, and the remainder is removed, wherein the remainder is a vector formed by combining K+2 or more quantization matrix vectors.
Step 6.3: will remain vector I uvw The combined matrix is defined as candidate matrix D candidate-k+1 。
D candidate-k+1 ＝(I 123 I 124 … I uvw ) T
Step 7, repeating the step 5 and the step 6 to generate an association matrix and a candidate matrix D candidate-k+1 And obtaining the association rule of the power consumption requirement among industries until all the association matrixes are generated.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced with equivalents; such modifications and substitutions do not depart from the spirit of the corresponding technical solutions, which are defined by the scope of the appended claims.
Claims (1)
1. The inter-industry electricity consumption demand association analysis system under the emergency is characterized by comprising a data quantization processing module, a data candidate module, a weight vector module, a support degree generation and screening module and an association rule generation module;
the data quantization processing module processes the data of the acquired original data set by adopting an interval weight method to generate a quantization matrix and transmits the quantization matrix to the data candidate module and the weight vector module respectively;
the data candidate module generates a candidate matrix by adopting a correlation combination method, and the weight vector module reduces the complexity of the calculation of the correlation matrix by scanning the quantization matrix and outputs the complexity to the support degree generation and screening module;
the support degree generation and screening module is used for screening vectors meeting the minimum support degree in the candidate matrix, combining the vectors into an association matrix and outputting the association matrix to the association rule generation module;
the association rule generation module outputs inter-industry electricity consumption requirement association under emergency;
the inter-industry electricity consumption demand correlation analysis system under the emergency is used for realizing an inter-industry electricity consumption demand correlation analysis method under the emergency, and comprises the following steps:
step 1: acquiring power consumption information of enterprises in various industries, urban population trip intensity and medical data to form a matrix to form an original data set; wherein the medical data comprises newly increased suspected cases, confirmed cases, healed people, dead people, accumulated suspected cases, confirmed cases, healed people and dead people;
step 2: scanning an original data set, and carrying out quantization processing on enterprise electricity consumption data and urban population trip intensity data in each industry through a data quantization processing module, namely converting the original data set into a quantization matrix by taking the growth rate of the data as a standard;
step 2.1, set D ij For the original dataset D t In the presence of an element of the group,calculating a data growth rate M:
wherein the original data set D t The method comprises the following steps:
step 2.2: let d pn For quantizing the elements in the matrix, d is calculated by interval weighting pn The method comprises the following steps:
step 2.3: by d pn Generating vector I in quantization matrix p ：
I p ＝(d p1 … d pn )
Step 2.4: from vector I p Generating quantization matrix D quantify ：
Step 3, scanning the quantization matrix to generate a weight vector W, and if repeated vectors are found in the process of scanning the quantization matrix, adding one to the corresponding weight and deleting the corresponding vector in the matrix; initial weight vector W 0 Is defined as
W 0 ＝{1,1,…,1}
Step 4: combining vectors in the quantization matrix into a candidate matrix according to a correlation degree combination method, wherein the combination method combines the vectors pairwise according to the correlation degree between the vectors;
step 4.1, set A i (i＝1,2,3…)，B i (i=1, 2,3 …) is the vector I in the quantization matrix u Sum vector I v In (a) elementsElement, vector I can be calculated u Sum vector I v The method comprises the following steps:
I u ＝(A 1 A 2 … A n )
I v ＝(B 1 B 2 … B n )
step 4.2: vector I in the quantization matrix u Sum vector I v Corresponding element A in (3) i (i＝1,2,3…)，B i (i=1, 2,3, …) are combined according to a correlation combination method, and the element C in the candidate matrix is calculated n ：
Step 4.3: element C in the candidate matrix to be calculated i (i=1, 2,3 …) are combined into vectors and named I uv Represented by vector I in the quantization matrix u Sum vector I v Is combined into a whole;
I uv ＝(C 1 C 2 … C n )
step 4.4: combining vectors in the candidate matrixes into candidate matrixes, wherein the candidate matrixes are as follows:
D candidate ＝(I 12 I 23 … I uv ) T
step 5: converting the candidate matrix into an associated matrix by a support degree generation and screening module;
step 5.1; setting the minimum support degree in a support degree generation and screening module;
step 5.2, calculating the support degree of each item in the candidate matrix, wherein the support degree represents the association degree of two items in the candidate matrix, is the product of the weight vector W and the number of corresponding item vectors in the candidate matrix, and is expressed as follows:
sup_d(I uv )＝WI j
wherein sup_d (I uv ) Is vector I uv Is a support degree of (2);
step 5.3; deleting the items with the minimum support degree, and forming an association matrix by the rest items;
step 6: the association matrix D is obtained through a data candidate module con-k Generating candidate matrix D candidate-k+1 Connecting every two vectors in the incidence matrix by using a correlation combination method, and removing the redundant items;
step 6.1: set up the association matrix D con-k And vector I therein uv ，I vw The following form:
D con-k ＝[I 12 I 13 … I uv I vw ] Τ
step 6.2: will associate matrix D con-k The vectors in (a) are combined two by adopting a correlation degree combination method, and the rest is removed, wherein the rest is a vector formed by combining K+2 or more quantization matrix vectors;
step 6.3: will remain vector I uvw The combined matrix is defined as candidate matrix D candidate-k+1 ；
D candidate-k+1 ＝(I 123 I 124 … I uvw ) Τ
Step 7: repeating the step 5 and the step 6 to generate an association matrix and a candidate matrix D candidate-k+1 And obtaining the association rule of the power consumption requirement among industries until all the association matrixes are generated.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111008294.9A CN113822702B (en) | 2021-08-30 | 2021-08-30 | Inter-industry electricity consumption demand correlation analysis system and method under emergency |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111008294.9A CN113822702B (en) | 2021-08-30 | 2021-08-30 | Inter-industry electricity consumption demand correlation analysis system and method under emergency |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113822702A CN113822702A (en) | 2021-12-21 |
| CN113822702B true CN113822702B (en) | 2023-10-20 |
Family
ID=78913793
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111008294.9A Active CN113822702B (en) | 2021-08-30 | 2021-08-30 | Inter-industry electricity consumption demand correlation analysis system and method under emergency |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113822702B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001092841A (en) * | 1999-09-24 | 2001-04-06 | Nippon Telegr & Teleph Corp <Ntt> | Cluster analyzing and processing method and recording medium having cluster analyzing program recorded thereon |
| CN104123600A (en) * | 2014-08-14 | 2014-10-29 | 国家电网公司 | Electrical manager's index forecasting method for typical industry big data |
| CN105335785A (en) * | 2015-10-30 | 2016-02-17 | 西华大学 | Association rule mining method based on vector operation |
| CN107463665A (en) * | 2017-08-01 | 2017-12-12 | 广东云下汇金科技有限公司 | A kind of data correlation rule mining algorithms |
| CN108701462A (en) * | 2016-03-21 | 2018-10-23 | 华为技术有限公司 | The adaptive quantizing of weighting matrix coefficient |
| CN109685277A (en) * | 2018-12-28 | 2019-04-26 | 国网冀北电力有限公司经济技术研究院 | Electricity demand forecasting method and device |
| JP2019159990A (en) * | 2018-03-15 | 2019-09-19 | 東京電力ホールディングス株式会社 | Power demand computing device and program |
| CN113282646A (en) * | 2021-04-30 | 2021-08-20 | 国网四川省电力公司经济技术研究院 | Daily load curve acquisition method based on power consumption behavior influence factors |
-
2021
- 2021-08-30 CN CN202111008294.9A patent/CN113822702B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001092841A (en) * | 1999-09-24 | 2001-04-06 | Nippon Telegr & Teleph Corp <Ntt> | Cluster analyzing and processing method and recording medium having cluster analyzing program recorded thereon |
| CN104123600A (en) * | 2014-08-14 | 2014-10-29 | 国家电网公司 | Electrical manager's index forecasting method for typical industry big data |
| CN105335785A (en) * | 2015-10-30 | 2016-02-17 | 西华大学 | Association rule mining method based on vector operation |
| CN108701462A (en) * | 2016-03-21 | 2018-10-23 | 华为技术有限公司 | The adaptive quantizing of weighting matrix coefficient |
| CN107463665A (en) * | 2017-08-01 | 2017-12-12 | 广东云下汇金科技有限公司 | A kind of data correlation rule mining algorithms |
| JP2019159990A (en) * | 2018-03-15 | 2019-09-19 | 東京電力ホールディングス株式会社 | Power demand computing device and program |
| CN109685277A (en) * | 2018-12-28 | 2019-04-26 | 国网冀北电力有限公司经济技术研究院 | Electricity demand forecasting method and device |
| CN113282646A (en) * | 2021-04-30 | 2021-08-20 | 国网四川省电力公司经济技术研究院 | Daily load curve acquisition method based on power consumption behavior influence factors |
Non-Patent Citations (2)
| Title |
|---|
| 基于大数据的用户用电行为分析研究在新能源电网中的应用;迟俊琳;中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑(第3期);第2-63页 * |
| 应用向量误差修正模型的行业电力需求 关联分析与负荷预测方法;王鹏，陈启鑫，夏清，王宣元;中国电机工程学报;第32卷(第4期);第1-9页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113822702A (en) | 2021-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111199016B (en) | Daily load curve clustering method for improving K-means based on DTW | |
| Nelson-Sathi et al. | Acquisition of 1,000 eubacterial genes physiologically transformed a methanogen at the origin of Haloarchaea | |
| Wang et al. | Stochastic unit commitment with sub-hourly dispatch constraints | |
| Durica et al. | Decision tree based model of business failure prediction for Polish companies | |
| WO2022142001A1 (en) | Target object evaluation method based on multi-score card fusion, and related device therefor | |
| CN110046801B (en) | Typical scene generation method of power distribution network power system | |
| CN113822702B (en) | Inter-industry electricity consumption demand correlation analysis system and method under emergency | |
| Jin et al. | A novel memory-efficient deep learning training framework via error-bounded lossy compression | |
| CN114328898A (en) | Text abstract generating method and device, equipment, medium and product thereof | |
| CN113157853B (en) | Problem mining method, device, electronic equipment and storage medium | |
| CN114556367A (en) | Interpretable table data learning using sequence sparse attention | |
| CN110188025B (en) | Efficient association method for alarm logs | |
| Garg et al. | Powernet: A novel method for wind power predictive analytics using Powernet deep learning model | |
| Shrestha et al. | High-performance classification of phishing URLs using a multi-modal approach with MapReduce | |
| Orito et al. | Search space reduction model with trigonometric function for linear equality constraint-handling: the case of portfolio replication problem | |
| CN110457650B (en) | Method, device, medium and electronic equipment for generating livability design | |
| CN113360652B (en) | Enterprise-level power user intelligent classification method and device | |
| Bing et al. | An optimized unbiased GM (1, 1) power model for forecasting MRO spare parts inventory | |
| Varmazyar et al. | Factors affecting the level of poverty and inequity of urban and rural households in Iran | |
| Choudhury et al. | Analysis of a two phases batch arrival queueing model with Bernoulli vacation schedule | |
| Mangal et al. | The contribution to economic growth by industry: The Case of Saint Lucia | |
| CN113837585B (en) | Collaborative integrated alarm receiving and processing comprehensive training system for urban frequent emergency | |
| Zhao et al. | Ultra-short-term power forecasting of wind farm cluster based on spatio-temporal graph neural network pattern prediction | |
| CN107562800B (en) | SFp-Link-based semi-structured data frequent pattern mining method | |
| Sun et al. | A Multi-scale Hybrid Wind Power Prediction Model Based on LSSVR-RELM-Multi-Head Attention-BiGRU and Data Decomposition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |