CN110543889A - power load hierarchical clustering method and device, computer equipment and storage medium - Google Patents
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Abstract
The invention relates to a hierarchical clustering method, a hierarchical clustering device, computer equipment and a storage medium for power loads, wherein the method comprises the following steps: acquiring power load data of a user; performing clustering analysis processing on the power load data to obtain initial clustering data; updating the initial clustering data based on a self-adaptive K mean value algorithm to obtain updated clustering data; and carrying out visual analysis processing on the updated cluster data to obtain visual data of the power load, classifying the power load data, and carrying out visual analysis processing on the updated data to accurately acquire the power load type so that a power system can make corresponding response regulation and control strategies for different power load types.
Description
Technical Field
the invention relates to the technical field of power systems, in particular to a method and a device for hierarchical clustering of power loads, computer equipment and a storage medium.
Background
With the continuous development of communication technology and automation technology, the control of the power load on the demand side can be realized by finely controlling the load parameter setting instead of simply switching off and limiting the power, so that the load form is changed. The load setting parameters are effectively and timely adjusted, the load form is changed, investment waste caused by coping with power consumption peaks can be avoided, certain standby support can be provided for massive access of the distributed new energy power supply, and the stability of a power grid is maintained.
demand Response (Demand Response) has been widely studied and applied as a means of rapidly adjusting load. Direct Load Control (Direct Load Control) is also becoming an important Load Control means. With the support of advanced communication and control technologies, more and more control methods and control strategies are applied in demand-side response and direct load control. However, because the number of the residential users is huge and the situations are different, the load types cannot be screened in a targeted manner so as to make a response regulation strategy.
disclosure of Invention
in view of the above, it is desirable to provide a power load hierarchical clustering method, a device, a computer device, and a storage medium capable of discriminating a power load type.
A method for hierarchical clustering of power loads is provided, the method comprising: acquiring power load data of a user; performing clustering analysis processing on the power load data to obtain initial clustering data; updating the initial clustering data based on a self-adaptive K mean value algorithm to obtain updated clustering data; and carrying out visual analysis processing on the updated clustering data to obtain visual data of the power load.
In one embodiment, the step of performing cluster analysis on the power load data to obtain initial cluster data includes: performing characterization processing on the power load data to obtain characteristic data; and carrying out clustering analysis processing on the characteristic data to obtain initial clustering data.
In one embodiment, the step of performing characterization processing on the power load data to obtain characteristic data includes: and carrying out normalization processing on the power load data to obtain the characteristic data.
in one embodiment, the step of performing cluster analysis on the power load data to obtain initial cluster data includes: and performing clustering analysis processing on the initial clustering data based on a K-means algorithm to obtain initial clustering data.
in one embodiment, the step of updating the initial clustering data based on a self-adaptive K-means algorithm to obtain updated clustering data includes: performing self-adaptive K-means operation on the power load data to obtain processed power load data; judging whether the minimum square error of the processed power load data is larger than a preset threshold value or not; when the minimum square error of the processed power load data is larger than the preset threshold value; and updating the initial clustering data according to the processed power load data to obtain updated clustering data.
In one embodiment, the step of performing a visual analysis process on the updated cluster data to obtain visual data of the power load includes: and carrying out visual analysis processing on the updated clustering data based on an entropy analysis method to obtain visual data of the power load.
In one embodiment, the step of performing a visual analysis process on the updated cluster data to obtain visual data of the power load includes: performing hierarchical clustering coordination processing on the updated clustering data to obtain clustering data subjected to coordination processing; and performing visual analysis processing on the cluster data subjected to the coordination processing to obtain visual data of the power load.
In one embodiment, a power load hierarchical clustering apparatus is provided, including: the device comprises an acquisition module, a clustering module, an updating module and a visualization module.
The acquisition module is used for acquiring the power load data of a user; the clustering module is used for carrying out clustering analysis processing on the power load data to obtain initial clustering data; the updating module is used for updating the initial clustering data based on a self-adaptive K mean algorithm to obtain updated clustering data; and the visualization module is used for performing visualization analysis processing on the updated clustering data to obtain visualization data of the power load.
A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor when executing the computer program implements the steps of the method of any of the embodiments described above.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any of the above embodiments.
According to the power load hierarchical clustering method, the power load hierarchical clustering device, the computer equipment and the storage medium, the power load data are classified through clustering analysis processing on the power load data, then the power load data are updated through a self-adaptive K-means algorithm, and the updated data are subjected to visual analysis processing, so that the power load type is accurately obtained, and a power system can conveniently make corresponding response regulation strategies for different power load types.
drawings
Fig. 1 is a schematic flow chart of a hierarchical clustering method for power loads according to an embodiment of the present invention;
Fig. 2 is a schematic structural diagram of a power load hierarchical clustering apparatus according to an embodiment of the present invention;
Fig. 3 is an internal structural diagram of a computer device in one embodiment of the present invention.
Detailed Description
To facilitate an understanding of the invention, the invention will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present invention are shown in the drawings. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
for example, there is provided a method of hierarchical clustering of electrical loads, the method comprising: acquiring power load data of a user; performing clustering analysis processing on the power load data to obtain initial clustering data; updating the initial clustering data based on a self-adaptive K mean value algorithm to obtain updated clustering data; and carrying out visual analysis processing on the updated clustering data to obtain visual data of the power load.
According to the power load hierarchical clustering method, the power load data are classified through clustering analysis processing of the power load data, then the power load data are updated through a self-adaptive K-means algorithm, and the updated data are subjected to visual analysis processing, so that the power load type is accurately obtained, and a power system can conveniently make corresponding response regulation and control strategies for different power load types.
In one embodiment, referring to fig. 1, a method for hierarchical clustering of power loads is provided, the method comprising:
Step 110, obtaining the power load data of the user.
specifically, the power load data of the user, that is, the average daily power load data of the user, may be acquired by the smart meter or the micro synchronous vector device, for example, the power load data of the user may be obtained by reading a reading and a time sequence of the smart meter.
And 120, performing clustering analysis processing on the power load data to obtain initial clustering data.
specifically, cluster analysis refers to an analysis process that groups a set of physical or abstract objects into a plurality of classes composed of similar objects, i.e., classifying data into different classes or overcharging clusters, so that objects in the same cluster have great similarity, and objects in different clusters have great difference. The cluster analysis can automatically classify from the sample data. Therefore, the power load data can be classified by carrying out cluster analysis processing on the power load data to obtain initial cluster data.
and step 130, updating the initial clustering data based on a self-adaptive K-means algorithm to obtain updated clustering data.
Specifically, the adaptive K-means algorithm calculates a minimum tree of the sample set, and then adaptively calculates a reasonable threshold through a side length distribution characteristic of the minimum tree, so as to cut all side lengths exceeding the threshold, and uses the number of subtrees generated thereby as the optimal clustering number of the K-means algorithm, and simultaneously uses a vertex set center of each subtree as an initial clustering center of the K-means algorithm, that is, the adaptive K-means algorithm can automatically calculate the optimal classification number, that is, the optimal type number of the power load can be automatically calculated based on the adaptive K-means algorithm, and the clustering data is updated.
It should be understood that, in the cluster analysis, the number of cluster clusters needs to be preset, and the number of cluster clusters determines the number of power load types, that is, when the cluster analysis processing is performed on the power load data, an initial cluster needs to be set first, and the power load data is classified according to the set initial cluster.
And 140, performing visual analysis processing on the updated clustering data to obtain visual data of the power load.
Specifically, the visualization data is a visualization graph, for example, an entropy value of the clustering data is calculated according to the updated clustering data, the entropy value is used as an abscissa and an average electric quantity is used as an ordinate, and the visualization graph of the updated clustering data is established so as to be convenient for a user to view. Therefore, corresponding response regulation strategies can be specified according to different power load types.
According to the power load hierarchical clustering method, the power load data are classified through clustering analysis processing of the power load data, then the power load data are updated through a self-adaptive K-means algorithm, and the updated data are subjected to visual analysis processing, so that the power load type is accurately obtained, and a power system can conveniently make corresponding response regulation and control strategies for different power load types.
In order to better perform cluster analysis processing on the power load data, in one embodiment, the step of performing cluster analysis processing on the power load data to obtain initial cluster data includes: performing characterization processing on the power load data to obtain characteristic data; and carrying out clustering analysis processing on the characteristic data to obtain initial clustering data.
specifically, the power load data is characterized, that is, the power load data is characterized and extracted, and the characteristics of the power load, for example, the maximum value, the minimum value, the average value, and the like of the power load, are counted to obtain the characteristic data. Therefore, before the clustering analysis processing is carried out on the power load data, the characteristic processing is carried out firstly to avoid the interference of individual abnormal power load data, so that the clustering analysis processing is better carried out on the power load data.
in order to better normalize the power load data, in one embodiment, the step of characterizing the power load data to obtain the characteristic data includes: and carrying out normalization processing on the power load data to obtain the characteristic data. Specifically, the normalization processing is a process of performing linear transformation on the power load data and transforming the power load data into a [0, 1] interval, and the power load data can be better normalized by performing the normalization processing on the power load data, so that a property leading that one or more elements cannot be ignored in calculating the distance is avoided.
in order to better perform cluster analysis on the power load data, in one embodiment, the step of performing cluster analysis processing on the power load data to obtain initial cluster data includes: and performing clustering analysis processing on the initial clustering data based on a K-means algorithm to obtain initial clustering data.
Specifically, the K-means algorithm, i.e., a K-means clustering algorithm (K-means clustering algorithm), is a clustering analysis algorithm for iterative solution, and the K-means clustering algorithm includes the steps of randomly selecting K objects as initial clustering centers, then calculating the distance between each object and each seed clustering center, and assigning each object to the nearest clustering center. The cluster centers and the objects assigned to them represent a cluster. The cluster center of a cluster is recalculated for each sample assigned based on the objects existing in the cluster. Therefore, the power load data can be better subjected to cluster analysis processing through the K-means algorithm.
In order to obtain the power load clustering result better. In one embodiment, the step of updating the initial clustering data based on a self-adaptive K-means algorithm to obtain updated clustering data includes: performing self-adaptive K-means operation on the power load data to obtain processed power load data; judging whether the minimum square error of the processed power load data is larger than a preset threshold value or not; when the minimum square error of the processed power load data is larger than the preset threshold value; and updating the initial clustering data according to the processed power load data to obtain updated clustering data.
Specifically, when the power load data is divided and clustered, a standard K-means algorithm is firstly adopted to set an initial cluster K-K0, then an adaptive K-means algorithm is utilized to calculate the power load data to obtain processed power load data, and when a least square error corresponding to any power load data s (t) is greater than a preset threshold, new clusters are added, so that the cluster data is updated. The expression for updating the initial clustering data is as follows:
Wherein Ci (t) is K representative load curves in the dictionary library; s (t) is the load curve that has been normalized; i(s) is the distribution center; and theta is an adaptive parameter.
therefore, the clustering data are updated through the steps, and the power load clustering result is obtained better.
In order for the power system to adopt corresponding regulation and control strategies for the power loads of different users, in one embodiment, the step of performing visual analysis processing on the updated cluster data to obtain visual data of the power loads includes: and carrying out visual analysis processing on the updated clustering data based on an entropy analysis method to obtain visual data of the power load.
In particular, entropy analysis, i.e. in information theory, entropy is a measure of uncertainty. The larger the information quantity is, the smaller the uncertainty is, and the smaller the entropy is; the smaller the amount of information, the greater the uncertainty and the greater the entropy. According to the characteristics of entropy, the randomness and the disorder degree of an event can be judged by calculating the entropy, and the dispersion degree of an index can also be judged by using the entropy, wherein the larger the dispersion degree of the index is, the larger the influence of the index on comprehensive evaluation is. Therefore, the updated clustering data is analyzed by adopting an entropy analysis method, the entropy of the clustering data is calculated, the entropy is used as an abscissa, the average electric quantity is used as an ordinate, and a visual graph of the updated clustering data is established so as to be convenient for a user to check. For example, a demand response regulation strategy may be adopted for users whose entropy values are lower than a first preset threshold, and an energy storage peak regulation control strategy may be adopted for users whose entropy values are higher than a second preset threshold, where the first preset threshold is smaller than the second preset threshold.
In one embodiment, the step of performing a visual analysis process on the updated cluster data to obtain visual data of the power load includes: and carrying out visual analysis processing on the updated clustering data based on a region analysis method to obtain visual data of the power load.
In one embodiment, the step of performing a visual analysis process on the updated cluster data to obtain visual data of the power load includes: and carrying out visual analysis processing on the updated clustering data based on a multi-dimensional segmentation analysis method to obtain visual data of the power load.
in one embodiment, the power load stratification method further comprises: and acquiring a regulation and control strategy of the power load according to the visual data of the power load.
in order to better acquire the visualized data, in one embodiment, the performing visualization analysis processing on the updated clustered data to obtain the visualized data of the power load includes: performing hierarchical clustering coordination processing on the updated clustering data to obtain clustering data subjected to coordination processing; and performing visual analysis processing on the cluster data subjected to the coordination processing to obtain visual data of the power load.
For example, the step of performing hierarchical clustering coordination processing on the updated clustering data to obtain the clustering data after coordination processing includes locating a clustering center of the clustering data according to the updated clustering data; identifying the frequency of the clustering data according to the clustering center of the clustering data; and obtaining the clustering data after coordination processing according to the frequency of the clustering data and the clustering center of the clustering data.
specifically, the hierarchical clustering coordination processing comprises the following steps: firstly, quickly positioning a daily average load curve clustering center Ci; then identifying the frequency pn (Ci) of any daily average load curve; finally, entropy values corresponding to the users are calculated, and based on entropy value analysis, the user load clustering categories are expanded to be as follows:
Therefore, by carrying out hierarchical clustering coordination processing on the updated clustering data, the clustering center and frequency of the power load can be calculated, so that the entropy value can be calculated subsequently, and the visualized data of the power load can be obtained better.
The following is a complete embodiment, and provides a method for hierarchical clustering of power loads, including: acquiring power load data of a user, and carrying out normalization processing on the power load data to obtain characteristic data; performing clustering analysis processing on the characteristic data to obtain initial clustering data; based on a K mean algorithm, carrying out clustering analysis processing on the initial clustering data to obtain initial clustering data; performing self-adaptive K-means operation on the power load data to obtain processed power load data; judging whether the minimum square error of the processed power load data is larger than a preset threshold value or not; when the minimum square error of the processed power load data is larger than the preset threshold value; updating the initial clustering data according to the processed power load data to obtain updated clustering data; performing hierarchical clustering coordination processing on the updated clustering data to obtain clustering data subjected to coordination processing; and performing visual analysis processing on the clustering data after the coordination processing based on an entropy analysis method to obtain visual data of the power load.
In one embodiment, referring to fig. 2, a power load hierarchical clustering apparatus is provided, which includes: an acquisition module 210, a clustering module 220, an update module 230, and a visualization module 240.
the obtaining module 210 is configured to obtain power load data of a user; the clustering module 220 is configured to perform clustering analysis on the power load data to obtain initial clustering data; the updating module 230 is configured to update the initial clustering data based on a self-adaptive K-means algorithm to obtain updated clustering data; the visualization module 240 is configured to perform visualization analysis processing on the updated clustering data to obtain visualization data of the power load. In one embodiment, the power load hierarchical clustering device comprises corresponding modules for realizing the steps of the power load hierarchical clustering method. In one embodiment, the power load hierarchical clustering device is implemented by using the power load hierarchical clustering method in any one of the embodiments.
in one embodiment, the clustering module comprises a characterization unit and a clustering unit.
The characterization unit is used for performing characterization processing on the power load data to obtain characteristic data; and the clustering unit is used for carrying out clustering analysis processing on the characteristic data to obtain initial clustering data.
In one embodiment, the characterization module is further configured to perform normalization processing on the power load data to obtain the characteristic data.
In one embodiment, the clustering module is further configured to perform clustering analysis processing on the initial clustering data based on a K-means algorithm to obtain initial clustering data.
In one embodiment, the updating module is further configured to perform adaptive K-means operation on the power load data to obtain processed power load data; judging whether the minimum square error of the processed power load data is larger than a preset threshold value or not; when the minimum square error of the processed power load data is larger than the preset threshold value; and updating the initial clustering data according to the processed power load data to obtain updated clustering data.
In one embodiment, the visualization module is further configured to perform visualization analysis processing on the updated clustering data based on an entropy analysis method to obtain visualization data of the power load.
In one embodiment, the visualization module includes a coordination unit and a visualization unit, and the coordination unit is configured to perform hierarchical clustering coordination processing on the updated clustering data to obtain clustering data after coordination processing; and the visualization unit is used for performing visualization analysis processing on the coordinated clustering data to obtain visualization data of the power load.
In one embodiment, a computer device is provided, the internal structure of which may be as shown in FIG. 3. The computer device includes a processor, a memory, a network interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method of hierarchical clustering of power loads. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, an external keyboard, a touch pad or a mouse and the like.
Those skilled in the art will appreciate that the architecture shown in fig. 3 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
in one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
Acquiring power load data of a user; performing clustering analysis processing on the power load data to obtain initial clustering data; updating the initial clustering data based on a self-adaptive K mean value algorithm to obtain updated clustering data; and carrying out visual analysis processing on the updated clustering data to obtain visual data of the power load.
in one embodiment, the processor, when executing the computer program, performs the steps of:
Performing characterization processing on the power load data to obtain characteristic data; and carrying out clustering analysis processing on the characteristic data to obtain initial clustering data.
In one embodiment, the processor, when executing the computer program, performs the steps of:
And carrying out normalization processing on the power load data to obtain the characteristic data.
In one embodiment, the processor, when executing the computer program, performs the steps of:
And performing clustering analysis processing on the initial clustering data based on a K-means algorithm to obtain initial clustering data.
In one embodiment, the processor, when executing the computer program, performs the steps of:
Performing self-adaptive K-means operation on the power load data to obtain processed power load data; judging whether the minimum square error of the processed power load data is larger than a preset threshold value or not; when the minimum square error of the processed power load data is larger than the preset threshold value; and updating the initial clustering data according to the processed power load data to obtain updated clustering data.
in one embodiment, the processor, when executing the computer program, performs the steps of:
And carrying out visual analysis processing on the updated clustering data based on an entropy analysis method to obtain visual data of the power load.
In one embodiment, the processor, when executing the computer program, performs the steps of:
Performing hierarchical clustering coordination processing on the updated clustering data to obtain clustering data subjected to coordination processing; and performing visual analysis processing on the cluster data subjected to the coordination processing to obtain visual data of the power load.
In one embodiment, a computer-readable storage medium is provided, having stored thereon a computer program which, when executed by a processor, performs the steps of:
acquiring power load data of a user; performing clustering analysis processing on the power load data to obtain initial clustering data; updating the initial clustering data based on a self-adaptive K mean value algorithm to obtain updated clustering data; and carrying out visual analysis processing on the updated clustering data to obtain visual data of the power load.
In one embodiment, the computer program when executed by a processor implements the steps of:
Performing characterization processing on the power load data to obtain characteristic data; and carrying out clustering analysis processing on the characteristic data to obtain initial clustering data.
In one embodiment, the computer program when executed by a processor implements the steps of: and carrying out normalization processing on the power load data to obtain the characteristic data.
in one embodiment, the computer program when executed by a processor implements the steps of: and performing clustering analysis processing on the initial clustering data based on a K-means algorithm to obtain initial clustering data.
in one embodiment, the computer program when executed by a processor implements the steps of: performing self-adaptive K-means operation on the power load data to obtain processed power load data; judging whether the minimum square error of the processed power load data is larger than a preset threshold value or not; when the minimum square error of the processed power load data is larger than the preset threshold value; and updating the initial clustering data according to the processed power load data to obtain updated clustering data.
In one embodiment, the computer program when executed by a processor implements the steps of: and carrying out visual analysis processing on the updated clustering data based on an entropy analysis method to obtain visual data of the power load.
In one embodiment, the computer program when executed by a processor implements the steps of: performing hierarchical clustering coordination processing on the updated clustering data to obtain clustering data subjected to coordination processing; and performing visual analysis processing on the cluster data subjected to the coordination processing to obtain visual data of the power load.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A method for hierarchical clustering of power loads, the method comprising:
acquiring power load data of a user;
Performing clustering analysis processing on the power load data to obtain initial clustering data;
Updating the initial clustering data based on a self-adaptive K mean value algorithm to obtain updated clustering data;
And carrying out visual analysis processing on the updated clustering data to obtain visual data of the power load.
2. The method according to claim 1, wherein the step of performing cluster analysis on the power load data to obtain initial cluster data includes:
Performing characterization processing on the power load data to obtain characteristic data;
And carrying out clustering analysis processing on the characteristic data to obtain initial clustering data.
3. the method according to claim 2, wherein the step of performing the characterization process on the power load data to obtain the characteristic data includes:
And carrying out normalization processing on the power load data to obtain the characteristic data.
4. The method according to claim 1, wherein the step of performing cluster analysis on the power load data to obtain initial cluster data includes:
And performing clustering analysis processing on the initial clustering data based on a K-means algorithm to obtain initial clustering data.
5. The method according to claim 1, wherein the step of updating the initial clustering data based on the adaptive K-means algorithm to obtain updated clustering data comprises:
Performing self-adaptive K-means operation on the power load data to obtain processed power load data;
Judging whether the minimum square error of the processed power load data is larger than a preset threshold value or not;
When the minimum square error of the processed power load data is larger than the preset threshold value;
And updating the initial clustering data according to the processed power load data to obtain updated clustering data.
6. the method for hierarchical clustering of power loads according to claim 1, wherein the step of performing visual analysis processing on the updated cluster data to obtain visual data of power loads comprises:
And carrying out visual analysis processing on the updated clustering data based on an entropy analysis method to obtain visual data of the power load.
7. The method for hierarchical clustering of power loads according to claim 1, wherein the step of performing visual analysis processing on the updated cluster data to obtain visual data of power loads comprises:
Performing hierarchical clustering coordination processing on the updated clustering data to obtain clustering data subjected to coordination processing;
And performing visual analysis processing on the cluster data subjected to the coordination processing to obtain visual data of the power load.
8. An apparatus for hierarchical clustering of power loads, comprising:
The acquisition module is used for acquiring the power load data of a user;
The clustering module is used for carrying out clustering analysis processing on the power load data to obtain initial clustering data;
The updating module is used for updating the initial clustering data based on a self-adaptive K mean algorithm to obtain updated clustering data;
And the visualization module is used for performing visualization analysis processing on the updated clustering data to obtain the visualization data of the power load.
9. a computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 7.
10. a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111382949A (en) * | 2020-03-18 | 2020-07-07 | 广东电网有限责任公司计量中心 | Power resource allocation method, system and equipment |
| CN112085076A (en) * | 2020-08-27 | 2020-12-15 | 河北智达光电科技股份有限公司 | Decision-making method and device based on smart street lamp big data and terminal |
| CN112464059A (en) * | 2020-12-08 | 2021-03-09 | 深圳供电局有限公司 | Power distribution network user classification method and device, computer equipment and storage medium |
| CN113591588A (en) * | 2021-07-02 | 2021-11-02 | 四川大学 | Video content key frame extraction method based on bidirectional space-time slice clustering |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447082A (en) * | 2015-11-04 | 2016-03-30 | 广东电网有限责任公司电力科学研究院 | Distributed clustering method for mass load curves |
| CN107423769A (en) * | 2017-08-03 | 2017-12-01 | 四川大学 | Electric load curve adaptive clustering scheme based on morphological feature |
| CN108875801A (en) * | 2018-05-29 | 2018-11-23 | 深圳万智联合科技有限公司 | A kind of Classification of Load Curves system based on smart grid |
| CN109409702A (en) * | 2018-10-10 | 2019-03-01 | 武汉华飞智能电气科技有限公司 | A kind of operation of power networks index analysing system |
| CN109657891A (en) * | 2018-09-18 | 2019-04-19 | 深圳供电局有限公司 | A kind of Load Characteristic Analysis method based on adaptive k-means++ algorithm |
| CN109787979A (en) * | 2019-01-22 | 2019-05-21 | 电子科技大学 | A kind of detection method of electric power networks event and invasion |
-
2019
- 2019-07-18 CN CN201910650185.3A patent/CN110543889A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105447082A (en) * | 2015-11-04 | 2016-03-30 | 广东电网有限责任公司电力科学研究院 | Distributed clustering method for mass load curves |
| CN107423769A (en) * | 2017-08-03 | 2017-12-01 | 四川大学 | Electric load curve adaptive clustering scheme based on morphological feature |
| CN108875801A (en) * | 2018-05-29 | 2018-11-23 | 深圳万智联合科技有限公司 | A kind of Classification of Load Curves system based on smart grid |
| CN109657891A (en) * | 2018-09-18 | 2019-04-19 | 深圳供电局有限公司 | A kind of Load Characteristic Analysis method based on adaptive k-means++ algorithm |
| CN109409702A (en) * | 2018-10-10 | 2019-03-01 | 武汉华飞智能电气科技有限公司 | A kind of operation of power networks index analysing system |
| CN109787979A (en) * | 2019-01-22 | 2019-05-21 | 电子科技大学 | A kind of detection method of electric power networks event and invasion |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111382949A (en) * | 2020-03-18 | 2020-07-07 | 广东电网有限责任公司计量中心 | Power resource allocation method, system and equipment |
| CN112085076A (en) * | 2020-08-27 | 2020-12-15 | 河北智达光电科技股份有限公司 | Decision-making method and device based on smart street lamp big data and terminal |
| CN112464059A (en) * | 2020-12-08 | 2021-03-09 | 深圳供电局有限公司 | Power distribution network user classification method and device, computer equipment and storage medium |
| CN112464059B (en) * | 2020-12-08 | 2024-03-22 | 深圳供电局有限公司 | Distribution network user classification method, device, computer equipment and storage medium |
| CN113591588A (en) * | 2021-07-02 | 2021-11-02 | 四川大学 | Video content key frame extraction method based on bidirectional space-time slice clustering |
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