CN110781332A - Electric power resident user daily load curve clustering method based on composite clustering algorithm - Google Patents
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Abstract
The method comprises the steps that a daily load curve clustering method of power residents based on a composite clustering algorithm is used for obtaining daily load data of the power residents, wherein the data comprises P samples, and each sample is provided with a data set matrix with Q time point attributes; preprocessing daily load data of power resident users to obtain an initial cluster; performing dimensionality reduction on the initial cluster to obtain a dimensionality reduction cluster; carrying out primary clustering on the dimensionality reduction cluster by adopting a clustering algorithm 1 to obtain an initial clustering center; clustering the initial clustering centers obtained by the clustering algorithm 1 by adopting a clustering algorithm 2, and evaluating clustering results by using a clustering effectiveness index to finally obtain M clustering centers; and clustering data by using the obtained M clustering centers as initial clustering centers of a clustering algorithm 2 to obtain user groups with similar behaviors. The invention clusters huge and scattered daily load data into similar user groups. The power enterprise manager analyzes the clustered user groups, can better predict power consumption peak and valley, and provides a more reliable method for power business management.
Description
Technical Field
The invention relates to the technical field of electricity consumption of power residents, in particular to a power resident daily load curve clustering method based on a composite clustering algorithm.
Background
With the rapid development of the power industry and the popularization of intelligent electric meters, the power utilization condition of electric power resident users is more convenient to acquire, and meanwhile, the electric power public can acquire more huge and detailed power utilization data of the users.
In the face of huge power consumption data, the conventional data mining and analyzing technology is utilized to perform rule analysis and feature extraction on daily load data of power consumers, so that a power company can provide higher-quality power supply service for the consumers according to the power price policy conveniently. The subdivision of the electricity consumption of the residential users is an important aspect of providing high-quality service for the power company, and in the face of increasing proportion of electricity consumption load of the residential users, the subdivision of the electricity consumption of the residential users is reasonable to use, and a high-efficiency data clustering algorithm is used for analyzing the users, so that the power company can be helped to provide a more reasonable and personalized power supply scheme according to the characteristics of the users, and the users can obtain better experience.
However, the single original clustering algorithm has low clustering efficiency and poor clustering effect, for example, the K-means algorithm is easy to make the clustering result into local optimum for a data set with large sample data size because the selection of the initial clustering center is random. The optimal clustering number cannot be determined, and researchers need to test one by one, so that the clustering efficiency is low. Therefore, the potential rules and the electricity utilization characteristics in the electricity utilization data of the users cannot be well reflected, and good support cannot be provided for the power company in the aspect of resident user clustering.
Disclosure of Invention
The invention provides a method for clustering daily load curves of power resident users based on a composite clustering algorithm.
The technical scheme adopted by the invention is as follows:
the electric power resident user daily load curve clustering method based on the composite clustering algorithm comprises the following steps:
step 1: acquiring daily load data of power residents, wherein the data comprises P samples, and each sample has Q data set matrixes of time point attributes;
step 2: preprocessing daily load data of power resident users to obtain an initial cluster;
and step 3: performing dimensionality reduction on the initial cluster to obtain a dimensionality reduction cluster;
and 4, step 4: carrying out primary clustering on the dimensionality reduction cluster by adopting a clustering algorithm 1 to obtain an initial clustering center;
and 5: clustering the initial clustering centers obtained by the clustering algorithm 1 by adopting a clustering algorithm 2, and evaluating clustering results by using a clustering effectiveness index to finally obtain M clustering centers;
step 6: and 5, clustering the data by using the M clustering centers obtained in the step 5 as initial clustering centers of the clustering algorithm 2 to obtain user groups with similar behaviors, and performing behavior characteristic analysis on the obtained user groups with similar behaviors.
In step 1, for P samples, each sample has Q time point attributes of the daily load data sets of the electric power residents, which specifically includes:
the P samples are resident user samples, the resident life is mainly influenced by factors such as seasonal changes, air temperature changes, people receiving level, air conditioning and electric cooking ownership, and different daily load curves can be caused by different factors; q is the power consumption at each time point every day collected by the intelligent electric meter at the time point, and the value of Q is determined according to the time interval of data collection of the intelligent electric meter.
In the step 2, the preprocessing comprises missing value processing, data standardization and data regularization processing;
missing value processing, namely deleting data with more missing values and completing data with less missing values;
data standardization, namely converting a method of raw data linearization into a range of [0, 1 ];
and (4) carrying out data regularization treatment, namely subtracting the mean value corresponding to each attribute from each attribute, and then dividing the mean value by the variance corresponding to the attribute.
In the step 3, pca (principal Component analysis), namely a principal Component analysis method, is adopted for the dimension reduction; the obtained dimensionality reduction cluster is a data set matrix with p samples and q attributes in each sample.
In the step 4, the dimensionality reduction cluster is subjected to preliminary clustering by adopting a clustering algorithm 1 to obtain a user group with similar behaviors, and the method specifically comprises the step of clustering p samples in a data set into N types by adopting a Mean-shift algorithm, wherein N is a positive integer.
In the step 5, clustering is performed on the clustering centers obtained by the clustering algorithm 1 by using the clustering algorithm 2, and clustering results are evaluated by using a clustering validity index, which specifically comprises: clustering N clustering centers obtained by the Mean-shift algorithm by adopting a K-means algorithm, respectively clustering [2 and N ] within a clustering number N range, wherein N is a positive integer, evaluating a clustering result by using a Calinski-Harabasz (CH) index, selecting a result with the largest CH value, and finally obtaining M clustering centers, wherein M is the positive integer in [2 and N ].
In the step 6, the obtained M clustering centers are used as initial clustering centers of the K-means algorithm, each sample in the data set, namely each user or each record, is clustered, and finally M classes of users are obtained.
The invention discloses a method for clustering daily load curves of power residents based on a composite clustering algorithm. The large and scattered daily load data is clustered into similar user groups. The power enterprise manager analyzes the clustered user groups, can better predict power consumption peak and valley, provides a more reliable method for power business management, and provides better service for power customers.
Drawings
FIG. 1 is a flow chart of the method of the present invention in example 1.
FIG. 2 is a flow chart of method embodiment 2 of the present invention.
Detailed Description
Example 1:
the electric power resident user daily load curve clustering method based on the composite clustering algorithm comprises the following steps:
step 1: acquiring daily load data of power residents, wherein the data comprises P samples, and each sample has Q data set matrixes of time point attributes;
step 2: preprocessing daily load data of power resident users to obtain an initial cluster;
and step 3: performing dimensionality reduction on the initial cluster to obtain a dimensionality reduction cluster;
and 4, step 4: carrying out primary clustering on the dimensionality reduction clusters by adopting a clustering algorithm 1 to obtain N initial clustering centers;
and 5: clustering the initial clustering centers obtained by the clustering algorithm 1 by adopting a clustering algorithm 2, and evaluating clustering results by using a clustering effectiveness index to finally obtain M clustering centers;
step 6: and 5, clustering the data by using the M clustering centers obtained in the step 5 as initial clustering centers of the clustering algorithm 2 to obtain user groups with similar behaviors, and performing behavior characteristic analysis on the obtained user groups with similar behaviors.
Example 2:
the electric power resident user daily load curve clustering method based on the composite clustering algorithm comprises the following steps:
firstly, daily load data of electric power residents is obtained, and the data comprises P samples and a data set matrix with Q time point attributes in each sample.
Generally, a data set passed by a marketing system of a power grid company comprises tens of thousands or more samples, each sample is a power resident user, and with the popularization of the smart meters, it becomes very easy to count daily load data of the resident users of each user.
Then, preprocessing the acquired daily load data of the electric power residents to obtain an initial cluster, wherein in the embodiment, the preprocessing process includes missing value processing, data standardization, data regularization and data dimension reduction on the daily load power consumption data of the electric power residents, and after the processing, the obtained initial cluster is a data set matrix with p samples and q attributes in each sample.
Specifically, the missing value processing includes deleting samples with few valid values and completing the missing values of samples with many valid values. Of course, when deleting an attribute having a small effective value, redundant attributes may be deleted together.
In the process of deleting samples, if n samples are deleted, P samples remain, where P is P-n. In addition, there are various ways to supplement the missing value, and in the present application, the average of the existing validity is taken as the filling value of the missing value. The skilled person can choose other complementary methods, the inhomogeneities of which do not affect the subsequent analysis process.
The data normalization is realized by converting a method of raw data linearization into a range of [0, 1], and the calculation formula of maximum-minimum normalization is
The method realizes the equal scaling of the original data, wherein, X
normIs normalized data, X is raw data, X
max、X
minRespectively, the maximum and minimum values of the original data set.
Specifically, the data regularization is to subtract the mean value corresponding to each attribute, and then divide by the variance corresponding to the attribute. After normalization and regularization, data of each attribute are gathered near 0, and the variance is 1, that is, the obtained sample data has zero mean and unit variance.
The data dimensionality reduction is specifically to perform dimensionality reduction on the data set by using Principal Component Analysis (PCA), namely a principal Component analysis method, so as to obtain a processed dimensionality reduction cluster R.
In the dimension reduction process, if the dimension reduction number is q, the dimension reduction cluster R after dimension reduction is a data set matrix with p samples and q attributes in each sample.
Then, clustering the data in the data set R by adopting a clustering algorithm to obtain a user group with similar electricity consumption behaviors, specifically, firstly, clustering p samples in the data set into N types by adopting a Mean-shift algorithm, wherein N is a positive integer, then clustering N clustering centers obtained by the Mean-shift algorithm by adopting a K-means algorithm, respectively clustering [2, N ] in the range of the clustering number N, wherein N is a positive integer, evaluating the clustering result by using Calinski-Harabasz (CH) index, selecting the result with the largest CH value to finally obtain M clustering centers, wherein M is a positive integer in [2, N ], and finally, clustering each sample in the data set, namely each user or each record by adopting the obtained M clustering centers as the initial clustering center of the K-means algorithm, and finally obtaining M classes of users.
In this embodiment, the specific process of the Mean-shift algorithm includes:
firstly, finding any sample i from a data set, calculating a mean shift vector of a sample point, and changing the position of a current central point; then, translating the window and recalculating the probability density; eventually converging to a probability density maximum, Mean-shift processes the next object in the data set R.
The more similar or equal the data attribute values in a class, the greater the sample density in that class. Each user group with similar behaviors is called a class, a plurality of similar classes are finally obtained, each class has a central sample point, and the user groups are named as class 1, class 2 … and class N in sequence.
In this embodiment, the specific process of the K-means algorithm includes:
first, the N center sample points are denoted as X ═ X
1,x
2,...,x
NFinding k points Y ═ Y arbitrarily from the cluster X
1,y
2,...,y
kAs a cluster center, where k belongs to [2, N ]]；
Secondly, calculating the distance from each point in the cluster X to k cluster center points in the cluster Y, and dividing the distance into classes corresponding to the cluster center points with the minimum distance;
thirdly, recalculating each clustering center;
fourthly, repeating the second step and the third step until the position of the clustering center is not changed;
fifthly, calculating a Calinski-Harabasz (CH) index corresponding to the k value;
and sixthly, repeating the first step to the fifth step for each K value from 2 to N, selecting the K value corresponding to the maximum value of Calinski-Harabasz (CH) index as K, and recording the corresponding clustering center as Z ═ Z { (Z)
1,Z
2,...,Z
K}；
Seventhly, calculating the distance from each point in the data set R to K clustering center points in the Z, and dividing the distance into classes corresponding to the clustering center points with the minimum distance;
eighthly, repeating the seventh step and the third step until the position of the clustering center is not changed;
and ninthly, outputting a clustering result.
In this embodiment, a specific calculation process of the Calinski-harabasz (ch) index is as follows:
where g denotes the number of clusters, h denotes the current class, trB (h) denotes the trace of the inter-class dispersion matrix, and trW (h) denotes the trace of the intra-class dispersion matrix. The larger CH represents the closer the class is, the more dispersed the class is, i.e. the better clustering result.
Claims (7)
1. The electric power resident user daily load curve clustering method based on the composite clustering algorithm is characterized by comprising the following steps of:
step 1: acquiring daily load data of power residents, wherein the data comprises P samples, and each sample has Q data set matrixes of time point attributes;
step 2: preprocessing daily load data of power resident users to obtain an initial cluster;
and step 3: performing dimensionality reduction on the initial cluster to obtain a dimensionality reduction cluster;
and 4, step 4: carrying out primary clustering on the dimensionality reduction cluster by adopting a clustering algorithm 1 to obtain an initial clustering center;
and 5: clustering the initial clustering centers obtained by the clustering algorithm 1 by adopting a clustering algorithm 2, and evaluating clustering results by using a clustering effectiveness index to finally obtain M clustering centers;
step 6: and 5, clustering the data by using the M clustering centers obtained in the step 5 as initial clustering centers of the clustering algorithm 2 to obtain user groups with similar behaviors.
2. The electric power resident user daily load curve clustering method based on the composite clustering algorithm as claimed in claim 1, wherein: in step 1, for P samples, each sample has Q time point attributes of the daily load data sets of the electric power residents, including: the P samples are resident user samples, the resident life is mainly influenced by factors such as seasonal changes, air temperature changes, people receiving level, air conditioning and electric cooking ownership, and different daily load curves can be caused by different factors; q is the power consumption at each time point every day collected by the intelligent electric meter at the time point, and the value of Q is determined according to the time interval of data collection of the intelligent electric meter.
3. The electric power resident user daily load curve clustering method based on the composite clustering algorithm as claimed in claim 1, wherein: in the step 2, the preprocessing comprises missing value processing, data standardization and data regularization processing;
missing value processing, namely deleting data with more missing values and completing data with less missing values;
data standardization, namely converting a method of raw data linearization into a range of [0, 1 ];
and (4) carrying out data regularization treatment, namely subtracting the mean value corresponding to each attribute from each attribute, and then dividing the mean value by the variance corresponding to the attribute.
4. The electric power resident user daily load curve clustering method based on the composite clustering algorithm as claimed in claim 1, wherein: in the step 3, pca (principal Component analysis), namely a principal Component analysis method, is adopted for the dimension reduction; the obtained dimensionality reduction cluster is a data set matrix with p samples and q attributes in each sample.
5. The electric power resident user daily load curve clustering method based on the composite clustering algorithm as claimed in claim 1, wherein: in the step 4, the dimensionality reduction cluster is subjected to preliminary clustering by adopting a clustering algorithm 1 to obtain a user group with similar behaviors, and the method specifically comprises the step of clustering p samples in a data set into N types by adopting a Mean-shift algorithm, wherein N is a positive integer.
6. The electric power resident user daily load curve clustering method based on the composite clustering algorithm as claimed in claim 1, wherein: in the step 5, clustering is performed on the clustering centers obtained by the clustering algorithm 1 by using the clustering algorithm 2, and clustering results are evaluated by using a clustering validity index, which specifically comprises: clustering N clustering centers obtained by the Mean-shift algorithm by adopting a K-means algorithm, respectively clustering [2 and N ] within a clustering number N range, wherein N is a positive integer, evaluating a clustering result by using a Calinski-Harabasz (CH) index, selecting a result with the largest CH value, and finally obtaining M clustering centers, wherein M is the positive integer in [2 and N ].
7. The electric power resident user daily load curve clustering method based on the composite clustering algorithm as claimed in claim 1, wherein: in the step 6, the obtained M clustering centers are used as initial clustering centers of the K-means algorithm, each sample in the data set, namely each user or each record, is clustered, and finally M classes of users are obtained.
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