CN111382949A - Power resource allocation method, system and equipment - Google Patents

Power resource allocation method, system and equipment Download PDF

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CN111382949A
CN111382949A CN202010191055.0A CN202010191055A CN111382949A CN 111382949 A CN111382949 A CN 111382949A CN 202010191055 A CN202010191055 A CN 202010191055A CN 111382949 A CN111382949 A CN 111382949A
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路韬
黄友朋
党三磊
张捷
赵闻
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Metrology Center of Guangdong Power Grid Co Ltd
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Abstract

The invention discloses a power resource allocation method, a system and equipment, comprising the following steps: acquiring data related to power consumption behaviors, wherein the data related to the power consumption behaviors comprises: forming a four-dimensional tensor data set by using power consumption data, weather data, social and economic index data and geographic and topographic data, reducing dimensions by using a dimension reduction method of nonnegative tensor decomposition and local linear preservation, obtaining core tensors, clustering, carrying out visual analysis on the core tensors of different categories, obtaining an analysis result of the power consumption behavior of a user, and distributing power resources. The invention combines the geographical position of the user, the regional climate and the local economic index with the original electricity consumption data, thereby comprehensively considering the influence of different factors on the electricity consumption behavior of the user, and reducing the complexity of operation by performing dimension reduction treatment on the combined data; the power utilization rate is improved by analyzing the power utilization behavior of the user so as to allocate the power resources.

Description

Power resource allocation method, system and equipment
Technical Field
The present invention relates to the field of power technologies, and in particular, to a method, a system, and a device for allocating power resources.
Background
At present, the power industry is taken as the national pillar industry, and the management of electric energy is related to the healthy and stable development of national economy and the daily life of people. Along with the continuous development of the Chinese social economy and the continuous acceleration of the urbanization process, the number of the electric power users in China is increased rapidly, and the data volume generated by the smart grid is also increased rapidly. In a traditional power system, the production and consumption of power resources belong to two different departments to manage, and are not well coordinated. If the power resources and the power consumption data and management can be well coordinated, the utilization rate of the resources can be greatly improved, and the problem can be exactly solved by the user power utilization behavior analysis technology. The user electricity utilization behavior analysis means that the user electricity utilization data and the user electricity utilization mode are combined by utilizing a big data correlation technology, so that potential characteristics are mined, and the utilization rate of power resources is improved.
At present, a common user electricity consumption behavior analysis algorithm mainly comprises a classic machine learning algorithm (such as random forest, K-means clustering, neural network and the like). The algorithm uses the power consumption of the user as data to perform feature extraction, and inputs the data into a model to perform training so as to obtain the behavior features of the user. However, currently, a common user behavior analysis algorithm only performs simple feature extraction, data cleaning and other data preprocessing on original data; the influence of the electricity consumption on the user behavior is only considered, the geographical position, the local climate, the local economic index and the like are not integrated into the electricity consumption for analysis, the electricity consumption behavior characteristics of the user cannot be objectively and comprehensively analyzed, and the electricity resources cannot be reasonably distributed.
In summary, the prior art has a technical problem that power resources cannot be reasonably allocated because the power consumption behavior characteristics of the user cannot be objectively and comprehensively analyzed.
Disclosure of Invention
The invention provides a power resource allocation method, a system and equipment, which are used for solving the technical problem that power resources cannot be allocated reasonably due to the fact that the power consumption behavior characteristics of users cannot be analyzed objectively and comprehensively in the prior art.
The invention provides a power resource allocation method, which comprises the following steps:
acquiring data related to power consumption behaviors, wherein the data related to the power consumption behaviors comprises: forming a four-dimensional tensor data set based on the electricity consumption behavior related data by using electricity consumption data, weather data, social and economic index data and geographic and topographic data;
reducing the dimensions of the four-dimensional tensor data set by adopting a dimension reduction method of nonnegative tensor decomposition and local linear preservation to obtain a core tensor;
clustering the core tensors to obtain different types of core tensors, and performing visual analysis on the different types of core tensors to obtain an analysis result of the power utilization behavior of the user;
and allocating the power resources according to the analysis result of the power utilization behavior of the user.
Preferably, the power usage data includes voltage, current, power, and electrical energy.
Preferably, the weather data includes temperature, humidity, wind direction and wind speed.
Preferably, the socioeconomic indicator data includes the human-averaged GDP, the kini index, and the engel coefficient.
Preferably, the geographic terrain data includes mountainous regions, plains, and coastal regions.
Preferably, in the dimension reduction, the core tensor constructed in the non-negative tensor decomposition uses a manifold regularization term.
Preferably, the core tensor is clustered by adopting a K-means algorithm.
Preferably, the process of visualizing the different classes of core tensors is: and respectively drawing the data of each power consumption index of each type of user for the core tensors of different types.
A power resource distribution system comprises a data acquisition module, a four-dimensional tensor module, a dimension reduction module, a clustering module, a visual analysis module and a power resource distribution module;
the data acquisition module is used for acquiring the electricity consumption behavior related data, and the electricity consumption behavior related data comprises: electricity consumption data, weather data, socioeconomic index data, and geographic and topographic data,
the four-dimensional tensor module is used for forming a four-dimensional tensor data set based on the electricity consumption behavior related data;
the dimensionality reduction module is used for carrying out dimensionality reduction on the four-dimensional tensor data set by adopting a dimensionality reduction method of nonnegative tensor decomposition and local linear maintenance to obtain a core tensor;
the clustering module is used for clustering the core tensors to obtain different types of core tensors;
the visualization analysis module is used for carrying out visualization analysis on the core tensors of different types to obtain an analysis result of the power utilization behavior of the user;
the power resource allocation module is used for allocating power resources according to the analysis result of the power utilization behavior of the user.
A power resource allocation apparatus comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute one of the above power resource allocation methods according to instructions in the program code.
According to the technical scheme, the embodiment of the invention has the following advantages:
the embodiment of the invention combines the geographical position of the user, the regional climate and the local economic index with the original power consumption data, thereby comprehensively considering the influence of different factors on the power consumption behavior of the user, and in the embodiment, the combined data is subjected to dimension reduction processing, the correlation characteristics are extracted, and the complexity of operation is reduced for subsequent analysis; the power utilization behavior of the user is analyzed, so that the power resources are reasonably distributed, the utilization rate of the power resources is improved, and the method has guiding significance in practical application.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.
Fig. 1 is a flowchart of a method, a system and a device for allocating power resources according to an embodiment of the present invention.
Fig. 2 is a system structure diagram of a power resource allocation method, system and device according to an embodiment of the present invention.
Fig. 3 is a device framework diagram of a power resource allocation method, system and device according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a power resource allocation method, a system and equipment, which are used for solving the technical problem that power resources cannot be allocated reasonably due to the fact that the power consumption behavior characteristics of users cannot be analyzed objectively and comprehensively in the prior art.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method, a system and a device for allocating power resources according to an embodiment of the present invention.
The embodiment of the invention provides a power resource allocation method, which comprises the following steps:
acquiring data related to power consumption behaviors, wherein the data related to the power consumption behaviors comprises: forming a four-dimensional tensor data set based on the electricity consumption behavior related data by using electricity consumption data, weather data, social and economic index data and geographic and topographic data; and (3) integrating the geographic position, the local climate, the local economic index and other influencing factors into the electricity consumption data to form multidimensional data in a tensor form for realizing a subsequent algorithm.
It is further noted that the electricity consumption data includes voltage, current, power and electric energy; weather data includes temperature, humidity, wind direction, and wind speed; the socioeconomic index data comprise the average human GDP, the Gini index and the Enger coefficient; geographic terrain data includes mountains, plains, and coastal.
Reducing the dimensions of the four-dimensional tensor data set by adopting a dimension reduction method of nonnegative tensor decomposition and local linear preservation to obtain a core tensor; a set of core tensors with smaller size is obtained by finding a set of bases of tensor objects in a low-dimensional space, and in order to retain associated geometric information in the tensors, a manifold regularization term is adopted for the core tensors constructed in the non-negative tensor decomposition, and the specific process is as follows:
the four-dimensional tensor data set is modeled as follows:
Figure BDA0002415928130000041
Figure BDA0002415928130000042
wherein the content of the first and second substances,
Figure BDA0002415928130000043
is a four-dimensional tensor data set, and
Figure BDA0002415928130000044
is a core tensor, and
Figure BDA0002415928130000045
Anis a projection matrix and N is 1, …, N-1; tr (-) is the trace of the matrix;
Figure BDA0002415928130000046
an N-mode expansion for the four-dimensional tensor data set;
Figure BDA0002415928130000047
an N-mode expansion for the core tensor; i isnN-1 is 1, …, N-1 is the size of the four-dimensional tensor data set; j. the design is a squarenN-1 is 1, …, N-1 is the size of the core tensor; m is the number of data set samples, and N is the order of the original tensor X;
where Π is the local linear retention matrix, whose solution is as follows:
1) local linearity
Is provided with
Figure BDA0002415928130000051
Is that
Figure BDA0002415928130000052
K neighborhoods of where ikIs an integer, and 1. ltoreq. ik≤M,k＝1,…,K，
Wherein the content of the first and second substances,
Figure BDA0002415928130000053
is x(N)In the ith row, K is the number of neighbors,
Figure BDA0002415928130000054
is composed of
Figure BDA0002415928130000055
K neighborhood points.
Then it is required to make
Figure BDA0002415928130000056
Minimum size
Figure BDA0002415928130000057
Wherein |2Is a 2-norm of the matrix and,
Figure BDA0002415928130000058
locally linear embedding coefficients.
The solution method is as follows:
Figure BDA0002415928130000059
wherein
Figure BDA00024159281300000510
Thereby to obtain
Figure BDA00024159281300000511
Get it solved
Figure BDA00024159281300000512
Wherein the content of the first and second substances,
Figure BDA00024159281300000513
2) local linear hold
Due to omegaiIs about each local data
Figure BDA00024159281300000514
The weight coefficient is expanded to the whole four-dimensional tensor data set representation for convenient calculation, namely, zetaij，ζijIs a matrix of M × M, which is calculated as follows:
Figure BDA00024159281300000515
Figure BDA0002415928130000061
wherein the content of the first and second substances,
Figure BDA0002415928130000062
is composed of
Figure BDA0002415928130000063
K neighborhood points;
Figure BDA0002415928130000064
and obtaining the core tensor of the four-dimensional tensor data set through the model solution of the four-dimensional tensor data set.
Clustering the core tensors to obtain different types of core tensors, and performing visual analysis on the different types of core tensors to obtain an analysis result of the power utilization behavior of the user;
and after the analysis result of the user electricity utilization behavior is obtained, allocating the power resources according to the analysis result of the user electricity utilization behavior.
As a preferred embodiment, the K-means algorithm is adopted to perform clustering processing on the core tensor. The K-means algorithm is a clustering analysis algorithm for iterative solution, and the steps are that data are divided into K groups in advance, K objects are randomly selected to serve as initial clustering centers, then the distance between each object and each seed clustering center is calculated, and each object is allocated to the clustering center closest to the object. 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. This process will be repeated until some termination condition is met. The termination condition may be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers are changed again, and the sum of squared errors is locally minimal.
As a preferred embodiment, the process of visualizing the core tensors of different classes is: and respectively drawing data of each power consumption index of each type of user for the core tensors of different types, marking the core tensors of different types by using points of different colors, and drawing the data of the power consumption indexes in a map to observe the characteristics of the core tensors for decision making.
As shown in fig. 2, an electric power resource allocation system includes a data acquisition module 201, a four-dimensional tensor module 202, a dimension reduction module 203, a clustering module 204, a visualization analysis module 205, and an electric power resource allocation module 206;
the data obtaining module 201 is configured to obtain data related to power consumption behaviors, where the data related to power consumption behaviors includes: electricity consumption data, weather data, socioeconomic index data, and geographic and topographic data,
the four-dimensional tensor module 202 is configured to form a four-dimensional tensor data set based on the electricity consumption behavior related data;
the dimensionality reduction module 203 is used for performing dimensionality reduction on the four-dimensional tensor data set by adopting a dimensionality reduction method of nonnegative tensor decomposition and local linear preservation to obtain a core tensor;
the clustering module 204 is configured to perform clustering processing on the core tensors to obtain core tensors of different categories;
the visualization analysis module 205 is configured to perform visualization analysis on different types of core tensors to obtain an analysis result of the power consumption behavior of the user;
the power resource allocation module 206 is configured to allocate power resources according to an analysis result of the power consumption behavior of the user.
As shown in fig. 3, a power resource allocation apparatus 30 includes a processor 300 and a memory 301;
the memory 301 is used for storing a program code 302 and transmitting the program code 302 to the processor;
the processor 300 is configured to execute the steps in one of the above embodiments of the power resource allocation method according to the instructions in the program code 302.
Illustratively, the computer program 302 may be partitioned into one or more modules/units that are stored in the memory 301 and executed by the processor 300 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 302 in the terminal device 30.
The terminal device 30 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 300, a memory 301. Those skilled in the art will appreciate that fig. 3 is merely an example of a terminal device 30 and does not constitute a limitation of terminal device 30 and may include more or fewer components than shown, or some components may be combined, or different components, e.g., the terminal device may also include input-output devices, network access devices, buses, etc.
The Processor 300 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 301 may be an internal storage unit of the terminal device 30, such as a hard disk or a memory of the terminal device 30. The memory 301 may also be an external storage device of the terminal device 30, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 30. Further, the memory 301 may also include both an internal storage unit and an external storage device of the terminal device 30. The memory 301 is used for storing the computer program and other programs and data required by the terminal device. The memory 301 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A power resource allocation method, comprising the steps of:
acquiring data related to power consumption behaviors, wherein the data related to the power consumption behaviors comprises: forming a four-dimensional tensor data set based on the electricity consumption behavior related data by using electricity consumption data, weather data, social and economic index data and geographic and topographic data;
reducing the dimensions of the four-dimensional tensor data set by adopting a dimension reduction method of nonnegative tensor decomposition and local linear preservation to obtain a core tensor;
clustering the core tensors to obtain different types of core tensors, and performing visual analysis on the different types of core tensors to obtain an analysis result of the power utilization behavior of the user;
and allocating the power resources according to the analysis result of the power utilization behavior of the user.
2. The power resource allocation method according to claim 1, wherein the power consumption data includes voltage, current, power and electric energy.
3. The power resource allocation method according to claim 2, wherein the weather data includes temperature, humidity, wind direction and wind speed.
4. The power resource allocation method according to claim 3, wherein the socioeconomic index data include GDP, Gini's index, and Enger's coefficient.
5. A power resource allocation method according to claim 4, wherein the geographic terrain data includes mountainous regions, plains and coastal regions.
6. The power resource allocation method according to claim 5, wherein in the dimension reduction, a manifold regularization term is used for a core tensor constructed in the non-negative tensor decomposition.
7. The power resource allocation method according to claim 6, wherein the core tensor is clustered by using a K-means algorithm.
8. The power resource allocation method according to claim 7, wherein the process of visualizing the different classes of core tensors comprises: and respectively drawing the data of each power consumption index of each type of user for the core tensors of different types.
9. A power resource distribution system is characterized by comprising a data acquisition module, a four-dimensional tensor module, a dimension reduction module, a clustering module, a visual analysis module and a power resource distribution module;
the data acquisition module is used for acquiring the electricity consumption behavior related data, and the electricity consumption behavior related data comprises: electricity consumption data, weather data, socioeconomic index data, and geographic and topographic data,
the four-dimensional tensor module is used for forming a four-dimensional tensor data set based on the electricity consumption behavior related data;
the dimensionality reduction module is used for carrying out dimensionality reduction on the four-dimensional tensor data set by adopting a dimensionality reduction method of nonnegative tensor decomposition and local linear maintenance to obtain a core tensor;
the clustering module is used for clustering the core tensors to obtain different types of core tensors;
the visualization analysis module is used for carrying out visualization analysis on the core tensors of different types to obtain an analysis result of the power utilization behavior of the user;
the power resource allocation module is used for allocating power resources according to the analysis result of the power utilization behavior of the user.
10. An electric power resource allocation apparatus comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute a power resource allocation method according to any one of claims 1 to 8 according to instructions in the program code.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140052503A1 (en) * 2011-04-21 2014-02-20 E3 Corporation System, technology, and method for a universal energy efficiency optimization platform for energy consuming devices, appliances and systems at residential, commercial, and industrial facilities
CN104915737A (en) * 2015-06-30 2015-09-16 国网山东省电力公司济南供电公司 Coordinating economic dispatching method for power system containing large-scale wind power based on risk control
CN105761109A (en) * 2016-02-19 2016-07-13 刘隽琦 Smart management system for energy management and electricity transaction in virtual power plant and method for optimizing operation thereof
CN110532444A (en) * 2019-07-22 2019-12-03 中国电力科学研究院有限公司 A kind of higher-dimension electric power big data method for visualizing and system
CN110543889A (en) * 2019-07-18 2019-12-06 广州供电局有限公司 power load hierarchical clustering method and device, computer equipment and storage medium

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140052503A1 (en) * 2011-04-21 2014-02-20 E3 Corporation System, technology, and method for a universal energy efficiency optimization platform for energy consuming devices, appliances and systems at residential, commercial, and industrial facilities
CN104915737A (en) * 2015-06-30 2015-09-16 国网山东省电力公司济南供电公司 Coordinating economic dispatching method for power system containing large-scale wind power based on risk control
CN105761109A (en) * 2016-02-19 2016-07-13 刘隽琦 Smart management system for energy management and electricity transaction in virtual power plant and method for optimizing operation thereof
CN110543889A (en) * 2019-07-18 2019-12-06 广州供电局有限公司 power load hierarchical clustering method and device, computer equipment and storage medium
CN110532444A (en) * 2019-07-22 2019-12-03 中国电力科学研究院有限公司 A kind of higher-dimension electric power big data method for visualizing and system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
侯雪等: "用电行为数据可视分析系统", 《计算机应用》 *
熊李艳等: "张量分解算法研究与应用综述", 《华东交通大学学报》 *
蒋子规: "面向电力大数据的用户用电行为挖掘方法", 《中国博士学位论文全文数据库》 *

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