CN111553413B - User electricity behavior clustering result evaluation method based on rough set - Google Patents
User electricity behavior clustering result evaluation method based on rough set Download PDFInfo
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Abstract
The invention provides a rough set-based clustering result evaluation method for user electricity behaviors, which introduces the idea of uncertainty in the rough set, can objectively evaluate the accuracy of clustering results of the electricity behaviors possibly belonging to a plurality of classes at the same time, and overcomes the defect that the conventional method cannot evaluate the clustering results obtained by different clustering methods comprising one user electricity behavior possibly belonging to the plurality of classes.
Description
Technical Field
The invention relates to the field of user electricity behavior clustering result evaluation, in particular to a rough set-based user electricity behavior clustering result evaluation method.
Background
Under the push of the rapid development of the smart grid, the large electric power data gradually become hot spots for people to study and pay attention to, so that the cluster analysis of the electric power consumption behavior data of the users is particularly necessary. How to objectively evaluate the accuracy of the electricity consumption behavior clustering of the user, accurately extract the electricity consumption habit and the electricity consumption rule of the user, and provide personalized service for the user, thereby having important significance in optimizing the power dispatching.
When data mining is performed on intelligent electricity consumption big data, at present, clustering analysis is often used at home and abroad to extract information value, and most of existing evaluation methods assume that one electricity consumption behavior is only classified in one class, so that the situation that the electricity consumption behavior has the characteristics of a plurality of classes at the same time cannot be processed, and the accuracy of clustering results of the electricity consumption behaviors possibly belonging to the classes at the same time is influenced. The invention provides a rough set-based user electricity behavior clustering result evaluation method, which introduces the idea of uncertainty in the rough set and objectively evaluates the accuracy of electricity behavior clustering results which possibly belong to a plurality of classes at the same time.
Disclosure of Invention
The invention provides a rough set-based user electricity behavior clustering result evaluation method, which objectively evaluates the accuracy of electricity behavior clustering results possibly belonging to a plurality of classes at the same time.
In order to achieve the technical effects, the technical scheme of the invention is as follows:
the electricity consumption behavior analysis method based on rough set fuzzy clustering comprises the following steps:
(1) Acquiring a clustering result of the electricity consumption behavior data of the user;
(2) Calculating the clustering accuracy;
(3) And (5) comparing and evaluating the clustering result.
Further, the step (1) is to obtain a clustering result of the user electricity behavior data, and the clustering result is not only a result obtained by a conventional clustering method in which only one electricity behavior is attributed, but also an evaluation of the clustering result of the electricity behavior possibly belonging to a plurality of classes based on a rough set.
Further, the step (2) is based on a rough set idea, and comprises a clustering accuracy formula of an upper approximation set and a lower approximation set:
the clustering accuracy formula is as follows:
wherein, for example, use is made ofTo represent the lower approximation set of class C, meaning that objects in the lower approximation set of class C must belong to class C; use->To represent the lower approximation set of class C, which means that objects in the lower approximation set of class C must belong to class C.
The parameter w is used to connect the upper approximation set and the lower approximation set, and the weights of the upper approximation set and the lower approximation set are represented by the subscript of w using different english letters.
The weight is defined as follows:
(1) Weight usage for lower approximation setTo represent, lower represents the meaning of the lower approximation set;
(2) The weights of the upper approximation set are obtained by usingTo indicate that upper indicates the meaning of the upper approximation set.
(3) The sum of the weights of the upper approximation set and the lower approximation set is 1, namely:
(4) The weight of the lower approximation set is equal to or greater than the upper approximation set. I.e.This is because the lower approximation set is a subset of the upper approximation set, that is, the emphasis of the clustering process is represented by the concept of the lower approximation set of classes, since the meaning of the lower approximation set of classes refers to a set containing objects that are determined to belong to the class
a represents the correct number of partitions of an object into classes.
In particular, as for the clustering result obtained by the conventional clustering method, the only class belongs to the lower approximation set of the class, but the situation that the object may belong to the class does not exist, so that the upper approximation set of all the classes is empty. Therefore, the method is also applicable to various clustering algorithms.
Further, the step (3) can compare the sizes of the clustering accuracy of the clustering results of different methods for the evaluation between different methods including the conventional clustering method and the clustering method based on the rough set, and has the advantages of high clustering accuracy and good clustering effect; otherwise, the clustering accuracy is small and the clustering effect is inferior.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
the invention provides a rough set-based clustering result evaluation method for the electricity consumption behavior of a user, which introduces the idea of uncertainty in the rough set, can objectively evaluate the accuracy of the clustering result of the electricity consumption behavior possibly belonging to a plurality of classes at the same time, and overcomes the defect that the conventional method cannot evaluate the clustering results obtained by different clustering methods comprising the electricity consumption behavior of the user possibly belonging to the plurality of classes.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the present patent;
for the purpose of better illustrating the embodiments, certain elements of the drawings may be omitted, enlarged or reduced and do not represent the actual product dimensions;
it will be appreciated by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1, the electricity behavior analysis method based on rough set fuzzy clustering comprises the following steps:
(1) Acquiring a clustering result of the electricity consumption behavior data of the user;
(2) Calculating the clustering accuracy;
(3) And (5) comparing and evaluating the clustering result.
And (1) acquiring a clustering result of the user electricity behavior data, wherein the clustering result is obtained by a conventional clustering method of only one electricity behavior, and is also suitable for evaluating the clustering result of the electricity behavior possibly belonging to a plurality of classes based on a rough set.
Step (2) is based on a rough set idea, and comprises a clustering accuracy formula of an upper approximation set and a lower approximation set:
the clustering accuracy formula is as follows:
wherein, for example, use is made ofTo represent the lower approximation set of class C, meaning that objects in the lower approximation set of class C must belong to class C; use->To represent the upper approximation set of class C, which means that the objects in the upper approximation set of class C must belong to class C.
The parameter w is used to connect the upper approximation set and the lower approximation set, and the weights of the upper approximation set and the lower approximation set are represented by the subscript of w using different english letters.
The weight is defined as follows:
(1) Weight usage for lower approximation setTo represent, lower represents the meaning of the lower approximation set;
(2) The weights of the upper approximation set are obtained by usingTo indicate that upper indicates the meaning of the upper approximation set.
(3) The sum of the weights of the upper approximation set and the lower approximation set is 1, namely:
(4) The weight of the lower approximation set is equal to or greater than the upper approximation set. I.e.This is because the lower approximation set is a subset of the upper approximation set, that is, the emphasis of the clustering process is represented by the concept of the lower approximation set of classes, since the meaning of the lower approximation set of classes refers to a set containing objects that are determined to belong to the class
a represents the correct number of partitions of an object into classes.
In particular, as for the clustering result obtained by the conventional clustering method, the only class belongs to the lower approximation set of the class, but the situation that the object may belong to the class does not exist, so that the upper approximation set of all the classes is empty. Therefore, the method is also applicable to various clustering algorithms.
The step (3) can compare the clustering accuracy of the clustering results of different methods for the evaluation between different methods including the conventional clustering method and the clustering method based on the rough set, and has good clustering effect if the clustering accuracy is high; otherwise, the clustering accuracy is small and the clustering effect is inferior.
The following describes the steps of the above method in detail in connection with the implementation procedure in one example, and the feasibility of the method is described.
The following table shows 3 different clustering methods for clustering 6 electricity behaviors, and 3 different clustering results are obtained, wherein the result 3 is a clustering method based on a rough set:
the following can be obtained by substituting the formula (1):
the clustering accuracy of result 1 is: 66.7%
The clustering accuracy of result 2 is: 83.3%
The clustering accuracy of result 3 is: 70 percent of
Thus, the clustering result was evaluated: the clustering effect of the clustering result 2 is best, the clustering result is 3 times, and the clustering result 1 is worst.
The same or similar reference numerals correspond to the same or similar components;
the positional relationship depicted in the drawings is for illustrative purposes only and is not to be construed as limiting the present patent;
it is to be understood that the above examples of the present invention are provided by way of illustration only and not by way of limitation of the embodiments of the present invention. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the invention are desired to be protected by the following claims.
Claims (7)
1. The method for evaluating the clustering result of the user electricity consumption behavior based on the rough set is characterized by comprising the following steps of:
s1: acquiring a clustering result of the electricity consumption behavior data of the user; the obtained clustering result of the user electricity behavior data is a result obtained by a conventional clustering method except for one electricity behavior, or a clustering result of one electricity behavior belonging to a plurality of classes based on a rough set;
s2: calculating the clustering accuracy; specifically, based on the rough set idea, the clustering accuracy is calculated through a clustering accuracy formula comprising an upper approximation set and a lower approximation set:
the clustering accuracy formula is as follows:
wherein ,the weights of the lower approximation set are represented,lowerrepresenting the lower approximation set->The weights of the upper approximation set are represented,upperrepresenting the upper approximation set->Representing the division of an object into the firstiThe correct number of classes;
s3: and (5) comparing and evaluating the clustering result.
2. The rough set-based user electricity behavior clustering result evaluation method according to claim 1, wherein the sum of the weight of the upper approximation set and the weight of the lower approximation set is 1, namely:。
3. the method for evaluating the clustering result of the user electricity consumption behavior based on the rough set according to claim 2, wherein the weight of the lower approximation set is greater than or equal to the weight of the upper approximation set, namely。
4. The method for evaluating the clustering result of the user electricity behavior based on the rough set according to claim 3, wherein in the step S3, the clustering accuracy is high, and the clustering effect is good; otherwise, the clustering accuracy is small and the clustering effect is inferior.
5. The method for evaluating the clustering result of the user electricity consumption behavior based on the rough set according to claim 4, wherein in the step S2, if usedTo represent the lower approximation set of class C, which means that objects in the lower approximation set of class C must belong to class C.
6. The method for evaluating the clustering result of the user electricity consumption behavior based on the rough set according to claim 5, wherein in the step S2, if usedTo represent the upper approximation set of class C, which means that the objects in the upper approximation set of class C must belong to class C.
7. The method for evaluating the clustering result of the user electricity behavior based on the rough set according to claim 5, wherein the weight of the lower approximation set is greater than or equal to the upper approximation set because the lower approximation set is a subset of the upper approximation set, and the importance of the clustering process is represented by the concept of the lower approximation set of the class because the meaning of the lower approximation set of the class refers to the set containing the objects determined to belong to the class.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103426123A (en) * | 2013-07-24 | 2013-12-04 | 国家电网公司 | Power grid fault risk evaluation method based on rough set theory |
| CN106529707A (en) * | 2016-11-01 | 2017-03-22 | 华北电力大学（保定） | Load power consumption mode identification method |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103426123A (en) * | 2013-07-24 | 2013-12-04 | 国家电网公司 | Power grid fault risk evaluation method based on rough set theory |
| CN106529707A (en) * | 2016-11-01 | 2017-03-22 | 华北电力大学（保定） | Load power consumption mode identification method |
Non-Patent Citations (1)
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| 一种基于粗糙集的K-means聚类算法;冯征;《计算机工程与应用》;20061231;第141-142、146页 * |
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