CN113935410A - Electric power customer portrait method based on cross-correlation density clustering - Google Patents

Electric power customer portrait method based on cross-correlation density clustering Download PDF

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CN113935410A
CN113935410A CN202111192403.7A CN202111192403A CN113935410A CN 113935410 A CN113935410 A CN 113935410A CN 202111192403 A CN202111192403 A CN 202111192403A CN 113935410 A CN113935410 A CN 113935410A
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张小敏
何清素
靳丹
张兴松
关志军
白爱东
刘晓光
张自强
李方军
杨仕博
韩庆之
丁芳玉
孙健鹏
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Jiayuguan Power Supply Company State Grid Gansu Electric Power Corp
State Grid Gansu Electric Power Co Ltd
Gansu Tongxing Intelligent Technology Development Co Ltd
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Jiayuguan Power Supply Company State Grid Gansu Electric Power Corp
State Grid Gansu Electric Power Co Ltd
Gansu Tongxing Intelligent Technology Development Co Ltd
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Abstract

The invention relates to the technical field of customer management in the power industry, in particular to a power customer portrait method based on intersection specific density clustering. Collecting a sample set, and carrying out missing value processing, consistency check, noise detection and normalization processing on the sample set by using a sample; constructing a power consumption behavior tag of a power customer; assigning the finished labels and then clustering the electricity utilization behaviors: calculating the mean value of all samples in each cluster according to the primary clustering result; and calculating the similarity among different clusters by using the obtained mean value to obtain a cluster similarity matrix, and then merging the potential similar clusters according to the similarity until the final cluster number is obtained. The invention relates to an electric power customer portrait method based on intersection specific density clustering, which can provide differentiated services for different power consumption groups.

Description

Electric power customer portrait method based on cross-correlation density clustering
Technical Field
The invention relates to the technical field of customer management in the power industry, in particular to a power customer portrait method based on intersection specific density clustering.
Background
At present, due to factors such as the improvement of the electrification level, the popularization of social informatization and the like, power supply enterprises begin to show a plurality of defects, and the contradiction between electric power marketing and high-quality service is increasingly excited. Customers are beginning to have increased demands on power quality, and even though power supply enterprises are always improving customer satisfaction, the number of complaints is still large every year. Furthermore, power marketing systems have stocked large amounts of power data, but these data are not being utilized efficiently. Nowadays, big data technology has become a trend, and many industries have established marketing systems related to big data, established enterprise client images, and helped enterprises to perform accurate marketing and information recommendation, and have been applied to industries such as finance, telecommunication, traffic, medical education and the like. Many industries at home and abroad carry out customer portrait in a marketing system, realize accurate marketing and personalized recommendation and the like, and promote multi-channel development. With the deepening of power system innovation, the power marketing department also sets forth power customer portraits, and the aim is to prepare in advance after the power selling side is released in terms of humanistic care and customer satisfaction, so that the application research of the power marketing informationized customer portraits has profound significance. The power grid enterprise has mass power data for many years, and the data resource is sufficiently reserved. The power data is fused by means of big data technologies such as multi-data fusion, data analysis mining, visualization, data storage and processing, and the like, so that the customer portrait of the power consumer becomes possible in the technical level. The application research of the electric power marketing information-based customer portrait has execution conditions in the aspects of requirements, resources and technologies, and is a guarantee for better serving users and improving the satisfaction degree of electric power users.
In the existing density clustering algorithm, a cut-off distance needs to be defined according to the requirement of the data set scale, however, an objective measurement standard is not provided to measure whether the data set is large-scale or small-scale, and different density measurement criteria and cut-off distances can cause large influence on a clustering result. Meanwhile, the allocation strategies of different categories are prone to generate a continuous error of category allocation, that is, once a certain sample is allocated incorrectly, a subsequent series of sample allocation errors can be caused.
Therefore, a power customer portrait method based on cross-comparison density clustering is provided.
Disclosure of Invention
The invention aims to provide a power customer portrait method based on cross-comparison density clustering, which aims to solve the problem that the conventional density clustering algorithm cannot define a truncation distance according to the size of a data set, does not have an objective measurement standard to measure whether the data set is large-scale or small-scale, and the distribution strategies of different categories are easy to generate category distribution errors.
A power customer portrait method based on cross-comparison density clustering comprises the following steps:
s1: collecting a sample set, and carrying out missing value processing, consistency check, noise detection and normalization processing on the sample set by using the sample;
s2: constructing a power consumption behavior tag of a power customer;
s3: assigning values to the labels completed in step S2 and then clustering the electricity usage behavior:
s4: calculating the mean value of all samples in each cluster according to the primary clustering result obtained in the step S3;
s5: and calculating the similarity among different clusters by using the average value obtained in the step S5 to obtain a cluster similarity matrix, and then merging the potential similar clusters according to the similarity until the final cluster number is obtained.
Further, in step S1, the sample set is X ═ X (X)1，x2，...，xm) Where m is the number of samples.
Further, in step S2, the electricity consumption behavior labels of the electricity consumers include more than capacity, electricity type, seasonal characteristics, temperature sensitivity, load stability, capacity utilization, holiday electricity characteristics, peak characteristics, and electricity consumption increase and decrease.
Further, in step S3, the electrical behavior clustering step includes:
s3.1: calculate any two samples xiAnd xjA distance d betweenij，
Figure BDA0003301692980000021
Obtaining a distance matrix D;
s3.2: computing arbitrary samples x from the distance matrixiK number of neighboring samples Ni(k)；
S3.3: calculating a sample xiOf neighboring samples Ni(k) And each sample remainedxpOf neighboring samples Np(k) The cross-over-cross-over ratio of (c),
Figure BDA0003301692980000031
s3.4: clustering intersection ratio IOU;
wherein d isijIs xiAnd xjDistance between, Ni(k) Is a neighbor sample, Np(k) For each sample x remainingpThe IOU is the cross-over ratio.
Further, in step S3.4, the clustering method of the intersection ratio IOU includes:
s3.4.1: when IOU is greater than threshold, then xiAnd its neighbor samples Ni (k) and xpAnd its neighbor samples np (k) are grouped into a cluster and these samples are removed from X and no longer participate in the calculation of IOU;
s3.4.2: when x ispE Ni (k) and IOU is less than the threshold, then xpAnd the samples in Np (k) that intersect with Ni (k) still belong to xiOne class, np (k) other samples are other classes and still participate in IOU calculations;
s3.4.3: when in use
Figure BDA0003301692980000032
And IOU is less than the threshold, the samples in Np (k) intersected with Ni (k) still belong to xiClass I, xpAnd np (k) where the other samples are of other classes and still participate in the IOU computation.
Further, the calculation formula of the cluster similarity matrix is as follows:
Figure BDA0003301692980000033
in summary, due to the adoption of the technical scheme, the beneficial technical effects of the invention are as follows:
the power customer portrait method based on cross-comparison density clustering can intuitively and quickly master power consumption behavior characteristics of different power customer groups, provides support for power companies to carry out power customer load prediction, and meanwhile, the power companies can also provide power consumption suggestions for power customers, specify good power price policies and provide differentiated services for different power customer groups.
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FIG. one is a flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
A power customer portrait method based on cross-comparison density clustering comprises the following steps:
s1: collecting a sample set, and carrying out missing value processing, consistency check, noise detection and normalization processing on the sample set by using the sample;
s2: constructing a power consumption behavior tag of a power customer;
s3: assigning values to the labels completed in step S2 and then clustering the electricity usage behavior:
s4: calculating the mean value of all samples in each cluster according to the primary clustering result obtained in the step S3;
s5: and calculating the similarity among different clusters by using the average value obtained in the step S5 to obtain a cluster similarity matrix, and then merging the potential similar clusters according to the similarity until the final cluster number is obtained.
In step S1, the sample set is X ═ X (X)1，x2，...，xm) Where m is the number of samples.
In step S2, the electricity consumption behavior labels of the electricity consumers include not only capacity, electricity consumption type, seasonal characteristics, temperature sensitivity, load stability, capacity utilization rate, holiday electricity characteristics, peak characteristics, and electricity consumption increase and decrease conditions.
In step S3, the electrical behavior clustering step includes:
s3.1: calculate any two samples xiAnd xjA distance d betweenij，
Figure BDA0003301692980000041
Obtaining a distance matrix D;
s3.2: computing arbitrary samples x from the distance matrixiK number of neighboring samples Ni(k)；
S3.3: calculating a sample xiOf neighboring samples Ni(k) And each sample x remainspOf neighboring samples Np(k) The cross-over-cross-over ratio of (c),
Figure BDA0003301692980000042
s3.4: clustering intersection ratio IOU;
wherein d isijIs xiAnd xjDistance between, Ni(k) Is a neighbor sample, Np(k) For each sample x remainingpThe IOU is the cross-over ratio.
In step S3.4, the clustering method of the intersection-to-parallel ratio IOU includes:
s3.4.1: when IOU is greater than threshold, then xiAnd its neighbor samples Ni (k) and xpAnd its neighbor samples np (k) are grouped into a cluster and these samples are removed from X and no longer participate in the calculation of IOU;
s3.4.2: when x ispE Ni (k) and IOU is less than the threshold, then xpAnd the samples in Np (k) that intersect with Ni (k) still belong to xiOne class, np (k) other samples are other classes and still participate in IOU calculations;
s3.4.3: when in use
Figure BDA0003301692980000051
And IOU is less than the threshold, the samples in Np (k) intersected with Ni (k) still belong to xiClass I, xpAnd np (k) where the other samples are of other classes and still participate in the IOU computation.
The calculation formula of the cluster similarity matrix is as follows:
Figure BDA0003301692980000052
the above description is not intended to limit the present invention, but rather, the present invention is intended to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present invention.

Claims (6)

1. A power customer portrait method based on cross-comparison density clustering comprises the following steps:
s1, collecting a sample set, and carrying out missing value processing, consistency check, noise detection and normalization processing on the sample set by the sample;
s2: constructing a power consumption behavior tag of a power customer;
s3: assigning values to the labels completed in step S2 and then clustering the electricity usage behavior:
s4: calculating the mean value of all samples in each cluster according to the primary clustering result obtained in the step S3;
s5: and calculating the similarity among different clusters by using the average value obtained in the step S5 to obtain a cluster similarity matrix, and then merging the potential similar clusters according to the similarity until the final cluster number is obtained.
2. The method as claimed in claim 1, wherein in step S1, the sample set is X ═ X (X ═ X)1，x2，...，xm) Where m is the number of samples.
3. The method as claimed in claim 1, wherein in step S2, the electricity consumption behavior labels of the electricity consumers include more than capacity, electricity type, seasonal characteristics, temperature sensitivity, load stability, capacity utilization rate, holiday electricity characteristics, peak characteristics, electricity consumption increase and decrease.
4. The electric power customer imaging method based on cross-comparison density clustering according to claim 1, characterized in that: in step S3, the electrical behavior clustering step includes:
s3.1: calculate any two samples xiAnd xjA distance d betweenij,
Figure FDA0003301692970000011
Obtaining a distance matrix D;
s3.2: computing arbitrary samples x from the distance matrixiK number of neighboring samples Ni(k)；
S3.3: calculating a sample xiOf neighboring samples Ni(k) And each sample x remainspOf neighboring samples Np(k) The cross-over-cross-over ratio of (c),
Figure FDA0003301692970000012
s3.4: clustering the cross-over-parallel ratio IOU;
wherein d isijIs xiAnd xjDistance between, Ni(k) Is a neighbor sample, Np(k) For each sample x remainingpThe IOU is the cross-over ratio.
5. The method for clustering intersection-to-parallel ratios IOUs according to claim 4, comprising the steps of:
s3.4.1: when IOU is greater than threshold, then xiAnd its neighbor samples Ni (k) and xpAnd its neighbor samples np (k) are grouped into a cluster and these samples are removed from X and no longer participate in the calculation of IOU;
s3.4.2: when x ispE Ni (k) and IOU is less than the threshold, then xpAnd the samples in Np (k) that intersect with Ni (k) still belong to xiOne class, np (k) other samples are other classes and still participate in IOU calculations;
s3.4.3: when in use
Figure FDA0003301692970000022
And IOU is less than the threshold, samples in Np (k) that intersect with Ni (k)Originally belonging to xiClass I, xpAnd np (k) where the other samples are of other classes and still participate in the IOU computation.
6. The electric power customer imaging method based on cross-comparison density clustering according to claim 1, characterized in that: the calculation formula of the cluster similarity matrix is as follows:
Figure FDA0003301692970000021
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Patent Citations (5)

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CN107358368A (en) * 2017-07-21 2017-11-17 国网四川省电力公司眉山供电公司 A kind of robust k means clustering methods towards power consumer subdivision
CN112241958A (en) * 2019-07-18 2021-01-19 普天信息技术有限公司 Target segmentation modeling method and device for three-dimensional point cloud data
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CN111177208A (en) * 2019-10-18 2020-05-19 姚长征 Power consumption abnormity detection method based on big data analysis
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