CN116579884A - Power user behavior analysis method and system - Google Patents

Power user behavior analysis method and system Download PDF

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CN116579884A
CN116579884A CN202310848427.6A CN202310848427A CN116579884A CN 116579884 A CN116579884 A CN 116579884A CN 202310848427 A CN202310848427 A CN 202310848427A CN 116579884 A CN116579884 A CN 116579884A
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park
users
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CN116579884B (en
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叶剑锋
龚艳玲
龚廷
彭军
程航宁
苏高扬
陈俊学
付文蕊
陈鑫
杜克
张鹏超
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Yicheng Power Supply Co Of State Grid Hubei Electric Power Co ltd
Wuhan Zhenming Technology Development Co ltd
Xiangyang Power Supply Co of State Grid Hubei Electric Power Co Ltd
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Yicheng Power Supply Co Of State Grid Hubei Electric Power Co ltd
Wuhan Zhenming Technology Development Co ltd
Xiangyang Power Supply Co of State Grid Hubei Electric Power Co Ltd
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Abstract

The application belongs to the technical field of power control, and relates to a power user behavior analysis method and a system, wherein the method comprises the following steps: calculating an associated user set corresponding to the target user according to the association degree; calculating the electricity consumption of the target user in the target time period according to the associated user set; and distributing power to the target user according to the power consumption of the target user in the target time period. According to the application, the electricity consumption of the target user in a specific time period can be predicted more accurately by analyzing the electricity consumption behavior of the user set with high correlation with the target user; according to the predicted electricity consumption, accurate power distribution to the target user can be realized, so that waste of park power resources is avoided, and through accurate prediction and power distribution, the operation pressure of a park power system can be effectively reduced, and the operation efficiency of the park power system is improved.

Description

Power user behavior analysis method and system
Technical Field
The application relates to the technical field of power control, in particular to a power consumer behavior analysis method and system.
Background
The park power system mainly refers to power supply services provided for enterprises or institutions in a park, such as an industrial park, a science and technology park, a university city and the like; distribution of campus power typically requires consideration of the power demand and consumption behavior of individual users within the campus to achieve efficient utilization of power resources.
In a traditional power distribution system, the prediction and distribution of the power demand of a user are usually based on historical data and empirical rules, and the method can meet the operation demand of the power system to a certain extent, but when processing large-scale user data, the method often needs a large amount of computing resources and time, and is difficult to process the mutual influence and dependency relationship among users, the operation state of the power system and the power demand of the user cannot be reflected in real time to cope with the abrupt change of the power demand, so that the operation efficiency of the power system is reduced, and the unfair distribution of power resources is possibly caused.
Therefore, how to accurately predict the power demand of users in a campus and realize accurate power distribution according to the prediction result is a problem to be solved in the power control of the campus.
Disclosure of Invention
In order to solve the problems in the prior art: the application provides a power user behavior analysis method and a system, which can effectively solve the problems in the background technology.
In order to solve the technical problems, the technical scheme provided by the application is as follows:
in a first aspect, a method for analyzing power consumer behavior includes the steps of:
acquiring electricity utilization data corresponding to users in a park, and constructing a three-dimensional matrix corresponding to the park according to the electricity utilization data;
respectively calculating the association degree of the target user and the user in the park according to the three-dimensional matrix;
calculating an associated user set corresponding to the target user according to the association degree;
calculating the electricity consumption of the target user in the target time period according to the associated user set;
and distributing power to the target user according to the power consumption of the target user in the target time period.
In any of the above schemes, preferably, the method for obtaining the electricity utilization data respectively corresponding to the users in the park, and constructing the three-dimensional matrix corresponding to the park according to the electricity utilization data includes:
setting a time separation period, wherein the time separation period comprises at least one time separation interval;
acquiring user electricity data corresponding to at least one time separation interval in the current time separation period;
and sequencing and grouping the user electricity data according to the unique identifiers respectively corresponding to the user and the time separation interval to obtain a three-dimensional matrix corresponding to the park in the current time separation period.
In any of the above schemes, preferably, calculating the association degree of the target user and the user in the campus according to the three-dimensional matrix includes:
calculating first data chains respectively corresponding to the target users and the users in the park according to the three-dimensional matrix corresponding to the park in the current time separation period;
obtaining at least one first sub-chain of the first data chain according to at least one time separation interval in the current time separation period;
setting a matching threshold, calculating the matching degree between the first sub-chains corresponding to the target user and the users in the park, and if the matching degree is smaller than the matching threshold, judging that the first sub-chain corresponding to the target user is matched with the first sub-chain corresponding to the users in the park;
extracting matched first sub-chains in first data chains respectively corresponding to the target user and the users in the park to generate a second data chain;
traversing a time separation interval corresponding to a first sub-chain in the second data chain, and connecting the first sub-chains corresponding to adjacent time separation intervals to obtain a second sub-chain;
and extracting the second sub-chain corresponding to the maximum value of the time length to obtain at least one third data chain.
In any of the above schemes, preferably, calculating the first data chains respectively corresponding to the target user and the users in the campus according to the three-dimensional matrix corresponding to the campus in the current time separation period includes:
obtaining characteristic points according to the three-dimensional matrix corresponding to the park in the previous time separation periodWherein->For the category of the user, < >>For user->In the time interval->Is (are) the electricity consumption of the car>For the ith time interval, +.>For user->In the time interval->Is used up of the time stamp of electricity;
and sequencing the characteristic points according to the time stamps to respectively obtain first data chains corresponding to the target users and the users in the campus.
In any of the above solutions, preferably, calculating the matching degree between the first sub-chains corresponding to the target user and the users in the campus respectively includes:
by the formula:calculating the matching degree between the first sub-chains respectively corresponding to the target user and the users in the campus>Wherein->Is the object ofUser u uses the amount of electricity at time stamp t, < >>For the power consumption of the users w in the campus at the time stamp t +.>And->Respectively corresponding to the minimum value and the maximum value of the electricity consumption of the first sub-chain for the target user u, +.>And->And respectively obtaining the minimum value and the maximum value of the electricity consumption of the first sub-chain corresponding to the user w in the park.
In any of the above schemes, preferably, calculating the associated user set corresponding to the target user according to the association degree includes:
sequentially calculating the degree of assimilation between the target user and different users in the park according to the lengths of the first sub-chain and the third data chain of the second data chain;
carrying out layered distribution on different users in a park according to the degree of assimilation to obtain a layered distribution diagram;
and calculating a selected area, and extracting park users corresponding to the selected area by the layered distribution map to obtain an associated user set.
In any of the above schemes, it is preferable that the calculating the degree of identity between the target user and different users in the campus sequentially according to the lengths of the first sub-chain and the third data chain of the second data chain includes:
by the formula:calculating the degree of identity between the target user and the different users in the campus +.>Wherein->Pearson coefficients for target user u and campus user w>Is a dynamic weight.
In any of the above schemes, preferably, calculating the electricity consumption of the target user in the target time period according to the associated user set includes:
the target time period corresponds to the time separation interval in the last time separation period, and a target historical time separation interval is obtained;
acquiring the electricity consumption of each element in the associated user set in the target historical time separation interval;
calculating average power consumption of each element in the target user and the associated user set for the current segmentation period;
and determining the electricity consumption of the target user in the target time period according to the electricity consumption of each element in the associated user set in the target historical time separation interval and the average electricity consumption of each element in the target user and the associated user set for the current segmentation period.
In any of the above solutions, preferably, determining the power consumption of the target user in the target period according to the power consumption of each element in the set of associated users in the target historical time interval and the average power consumption of each element in the set of target users and the set of associated users for the current segmentation period includes:
by the formula:calculating the power consumption of the target user in the target time period, wherein +.>For the power consumption of the target user u in the time division section of the target time period corresponding to the current time division period,/>For the average power consumption of the target user in the time division interval corresponding to the current time division period, +.>For the average power consumption of the user v in the time division section corresponding to the current time division period in the associated user set,/>For the associated user set the power consumption of user v in said target historical time interval, +.>The degree of assimilation between the user v is concentrated for the target user and the associated user.
In a second aspect, a power consumer behavior analysis system, the system comprising:
the construction module is used for acquiring the power utilization data respectively corresponding to the users in the park and constructing a three-dimensional matrix corresponding to the park according to the power utilization data;
the computing module is used for respectively computing the association degree of the target user and the user in the park according to the three-dimensional matrix;
the association module is used for calculating an association user set corresponding to the target user according to the association degree;
the prediction module is used for calculating the electricity consumption of the target user in the target time period according to the associated user set;
and the distribution module is used for distributing power to the target user according to the power consumption of the target user in the target time period.
Compared with the prior art, the application has the beneficial effects that:
according to the application, the electricity consumption of the target user in a specific time period can be predicted more accurately by analyzing the electricity consumption behavior of the user set with high correlation with the target user; according to the predicted electricity consumption, accurate power distribution to the target user can be realized, so that waste of park power resources is avoided, and through accurate prediction and power distribution, the operation pressure of a park power system can be effectively reduced, and the operation efficiency of the park power system is improved.
Drawings
The accompanying drawings are included to provide a further understanding of the application, and are incorporated in and constitute a part of this specification.
FIG. 1 is a flow chart of a method for analyzing power consumer behavior according to the present application;
FIG. 2 is a schematic block diagram of the power consumer behavior analysis system of the present application.
The reference numerals in the figures illustrate:
10. constructing a module; 20. a computing module; 30. an association module; 40. a prediction module; 50. and a distribution module.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In order to better understand the above technical scheme, the following detailed description of the technical scheme of the present application will be given with reference to the accompanying drawings of the specification and the specific embodiments.
As shown in fig. 1, a method for analyzing the behavior of a power consumer includes the following steps:
step 1, obtaining power utilization data respectively corresponding to users in a park, and constructing a three-dimensional matrix corresponding to the park according to the power utilization data;
step 2, respectively calculating the association degree of the target user and the user in the park according to the three-dimensional matrix;
step 3, calculating an associated user set corresponding to the target user according to the association degree;
step 4, calculating the electricity consumption of the target user in the target time period according to the associated user set;
and 5, distributing power to the target user according to the power consumption of the target user in the target time period.
It should be noted that the above steps are only preferred embodiments, and in the specific implementation process, part of the steps may be exchanged without affecting the overall implementation effect, so as to more clearly illustrate the technical solution of the present application, and the following description will explain the present application in a preferred manner.
In another optional embodiment of the present application, the step 1 further includes:
step 11, setting a time separation period, wherein the time separation period comprises at least one time separation interval;
step 12, obtaining user electricity data corresponding to at least one time separation interval in the current time separation period;
and 13, sorting and grouping the user electricity data according to the unique identifications respectively corresponding to the user and the time separation interval to obtain a three-dimensional matrix corresponding to the park in the current time separation period.
In this embodiment, for the time separation period involved in the determination in step 11 described above, the time separation period may be divided by hour, by day, by week, or by month, or the like. For example, according to the day as the time separation period, each hour in the day forms at least one time separation interval, so as to better analyze and predict the electricity consumption condition of the user in different time periods; for the step 13, after the user electricity data is obtained, the user electricity data needs to be ordered and grouped, the basis of the ordering and grouping is the unique identification of the user and the time separation interval, such as the user ID and the date, and after the ordering, the electricity consumption condition of the user in the campus in each time interval can be directly obtained; after grouping, the electricity consumption condition of each user in the park in the same time interval can be seen so as to be convenient for comparison and analysis; and finally converting the ordered and grouped data into a three-dimensional matrix, wherein one dimension represents a user, one dimension represents a time separation interval, and the other dimension represents electricity consumption.
In another optional embodiment of the present application, the step 2 further includes:
step 21, calculating first data chains respectively corresponding to the target users and the users in the park according to the three-dimensional matrix corresponding to the park in the current time separation period;
step 22, obtaining at least one first sub-chain of the first data chain according to at least one time separation interval in the current time separation period;
step 23, setting a matching threshold, calculating the matching degree between the first sub-chains corresponding to the target user and the user in the park, and if the matching degree is smaller than the matching threshold, judging that the first sub-chain corresponding to the target user is matched with the first sub-chain corresponding to the user in the park;
step 24, extracting the first sub-chains matched in the first data chains respectively corresponding to the target user and the users in the campus to generate a second data chain;
step 25, traversing the time separation interval corresponding to the first sub-chain in the second data chain, and connecting the first sub-chains corresponding to the adjacent time separation interval to obtain a second sub-chain;
and step 26, extracting the second sub-chain corresponding to the maximum value of the time length to obtain at least one third data chain.
In this embodiment, a first data chain may be calculated for each user (including the target user and other users in the campus) by separating the corresponding three-dimensional matrix of the campus according to the current time; the first data chain is a power consumption behavior sequence of a user under a certain time separation period; for the first data chain, a plurality of first sub-chains may be partitioned according to a time-separated period. For example, if the data chain is in terms of electricity usage recorded per hour, 24 hours per day may be considered as one sub-chain; and then a matching threshold is set, and matching first sub-chains are found by calculating the matching degree of the first sub-chains in the first data chains of the target user and other users in the park, and the matching first sub-chains are extracted from the respective first data chains to form a second data chain only comprising the same electricity utilization behaviors of the target user and other users in the park, so that a third data chain with the longest time line consistency is obtained through the processing, and the electricity utilization behavior similarity between the two users can be more conveniently judged.
Optionally, in the step 21, calculating the first data chains respectively corresponding to the target user and the users in the campus according to the three-dimensional matrix corresponding to the campus in the current time separation period includes:
obtaining characteristic points according to the three-dimensional matrix corresponding to the park in the previous time separation periodWherein->For the category of the user, < >>For user->In the time interval->Is (are) the electricity consumption of the car>For the ith time interval, +.>For user->In the time interval->Is used up of the time stamp of electricity;
and sequencing the characteristic points according to the time stamps to respectively obtain first data chains corresponding to the target users and the users in the campus.
In this embodiment, by the above, the original three-dimensional matrix data can be converted into a data chain which is more convenient to process and analyze, and the efficiency of data processing is improved.
Optionally, in step 23, calculating the matching degree between the first sub-chains corresponding to the target user and the users in the campus respectively includes:
by the formula:calculating the matching degree between the first sub-chains respectively corresponding to the target user and the users in the campus>Wherein->For the power consumption of the target user u at the time stamp t +.>For the power consumption of the users w in the campus at the time stamp t +.>And->Respectively corresponding to the minimum value and the maximum value of the electricity consumption of the first sub-chain for the target user u, +.>And->And respectively obtaining the minimum value and the maximum value of the electricity consumption of the first sub-chain corresponding to the user w in the park.
In this embodiment, because there may be a large difference in the electricity consumption of different users, the direct comparison may not reflect the actual matching degree, and the electricity consumption of the target user and the users in the campus may be normalized by the formula, so that the electricity consumption of different users under the same timestamp may be compared under the same standard; and the similarity of the electricity utilization modes of the target user and other users in the park in the same time period can be calculated, so that the users with similar electricity utilization modes can be identified.
In another alternative embodiment of the present application, the step 3 may include:
step 31, calculating the degree of assimilation between the target user and different users in the campus in sequence according to the lengths of the first sub-chain and the third data chain of the second data chain;
step 32, carrying out layered distribution on different users in a park according to the degree of homology to obtain a layered distribution diagram;
and step 33, calculating a selected area, and extracting park users corresponding to the selected area by the layered distribution map to obtain an associated user set.
Optionally, in step 31, the calculating the degree of identity between the target user and different users in the campus according to the lengths of the first sub-chain and the third data chain of the second data chain sequentially includes:
by the formula:calculating the degree of identity between the target user and the different users in the campus +.>Wherein->Pearson coefficients for target user u and campus user w>Is a dynamic weight.
In this embodiment, the formula is given by:
calculate dynamic weight->Wherein->Is constant (I)>For the number of first sub-chains in the second data chain of target user u and on-campus user w,/o>For the number of first sub-chains in the first data chain of target user u +.>For the number of first sub-chains in the first data chain of on-campus user w +.>For the length of the third data chain of the target user u and the on-campus user w +.>Length of the first data chain for target user u, < >>The length of the first data chain for the target user w; in this embodiment, the greater the number of the first sub-chains in the second data chain, the higher the similarity between the target user and the user in the campus is reflected, and the longer the length of the third data chain, the higher the similarity between the target user and the user in the campus is reflected, so that the features of the second data chain and the third data chain can be fused according to the above formula, thereby obtaining a more accurate calculation result compared with a general similarity algorithm, and further obtaining a final assimilation calculation result by integrating the fused similarity calculation result into a pearson coefficient, and the identity of the target user and the user in the campus can be sufficiently and accurately calculated.
Optionally, in step 32, the step of distributing different users in the campus in a layered manner according to the degree of homology to obtain a layered distribution map includes:
setting an assimilation degree threshold value, and obtaining the number k of users in a park meeting the assimilation degree threshold value;
taking the square root value of k, and rounding the square root value of k to the nearest integer m;
whether k/m is an integer or not is judged, if not, the k/m is rounded to the nearest integer Y, m is set to be the number of layers distributed in a layered mode, and Y is the number of users distributed in each layer;
sequencing users from high to low according to the user degree of assimilation, and obtaining the user degree of assimilation range of each layer according to the number Y of the users of each layer;
and distributing the users to corresponding levels according to the user uniformity range of each layer to obtain a layered distribution map.
Optionally, in the step 33, a selection area is calculated, and the campus users corresponding to the selection area are extracted from the layered distribution map to obtain an associated user set, which includes:
obtaining a maximum layer number U of which the minimum value of the user assimilation degree range in the layered distribution diagram meets the assimilation degree threshold;
by the formula:calculating the area ST of the selected area, wherein +.>In order to be a layer width,is the interlayer spacing;
and extracting park users of the layered distribution map corresponding to the area ST to obtain an associated user set.
In this embodiment, by the above, it is possible to more efficiently process large-scale user data in the case where the number of campus users is very large; by performing layered distribution according to the user degrees of assimilation, the user degrees of assimilation of each layer have a definite range, so that the difference between users can be reflected more accurately, a user group more similar to a target user can be found more quickly, and the computing resources and time are saved.
In another optional embodiment of the present application, the step 4 includes:
step 41, corresponding the target time period to the time separation interval in the previous time separation period to obtain a target historical time separation interval;
step 42, obtaining the electricity consumption of each element in the associated user set in the target historical time separation interval;
step 43, calculating average electricity consumption of each element in the target user and the associated user set for the current segmentation period;
and step 44, determining the electricity consumption of the target user in the target time period according to the electricity consumption of each element in the associated user set in the target historical time separation interval and the average electricity consumption of each element in the target user and the associated user set for the current segmentation period.
Further, step 44, determining the power consumption of the target user in the target time period according to the power consumption of each element in the set of associated users in the target historical time separation interval and the average power consumption of each element in the set of target users and the set of associated users for the current segmentation period, includes:
by the formula:calculating the power consumption of the target user in the target time period, wherein +.>For the power consumption of the target user u in the time division section of the target time period corresponding to the current time division period,/>For the average power consumption of the target user in the time division interval corresponding to the current time division period, +.>For the average power consumption of the user v in the time division section corresponding to the current time division period in the associated user set,/>For the associated user set the power consumption of user v in said target historical time interval, +.>The degree of assimilation between the user v is concentrated for the target user and the associated user.
In this embodiment, the historical electricity consumption of the target user and the assimilation degree and electricity consumption of the related user set can be collected through the formula, so that the electricity consumption prediction accuracy of the target user in the target time period can be improved, and reasonable electricity can be distributed to the target user in the target time period according to the step 5; the dynamic adjustment can be carried out according to the actual application scene, and the application scene of the whole algorithm is enlarged.
As shown in fig. 2, the present application further provides a system for analyzing the behavior of a power consumer, the system comprising:
the construction module 10 is used for acquiring power utilization data respectively corresponding to users in the park and constructing a three-dimensional matrix corresponding to the park according to the power utilization data;
the calculating module 20 is configured to calculate association degrees corresponding to the target users and the users in the campus according to the three-dimensional matrix respectively;
the association module 30 is configured to calculate an associated user set corresponding to the target user according to the association degree;
a prediction module 40, configured to calculate, according to the set of associated users, an amount of electricity used by the target user in the target time period;
and the distribution module 50 is used for distributing power to the target users according to the power consumption of the target users in the target time period.
The above is only a preferred embodiment of the present application, and the present application is not limited thereto, but it is to be understood that the present application is described in detail with reference to the foregoing embodiments, and modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims (8)

1. A power user behavior analysis method is characterized in that: the method comprises the following steps:
the method for obtaining the electricity utilization data respectively corresponding to the users in the park and constructing the three-dimensional matrix corresponding to the park according to the electricity utilization data comprises the following steps: setting a time separation period, wherein the time separation period comprises at least one time separation interval; acquiring user electricity data corresponding to at least one time separation interval in the current time separation period; sorting and grouping the user electricity data according to unique identifiers respectively corresponding to the user and the time separation interval to obtain a three-dimensional matrix corresponding to the park in the current time separation period;
and respectively calculating the association degree of the target user and the user in the park according to the three-dimensional matrix, wherein the association degree comprises the following steps: calculating first data chains respectively corresponding to the target users and the users in the park according to the three-dimensional matrix corresponding to the park in the current time separation period; obtaining at least one first sub-chain of the first data chain according to at least one time separation interval in the current time separation period; setting a matching threshold, calculating the matching degree between the first sub-chains corresponding to the target user and the users in the park, and if the matching degree is smaller than the matching threshold, judging that the first sub-chain corresponding to the target user is matched with the first sub-chain corresponding to the users in the park; extracting matched first sub-chains in first data chains respectively corresponding to the target user and the users in the park to generate a second data chain; traversing a time separation interval corresponding to a first sub-chain in the second data chain, and connecting the first sub-chains corresponding to adjacent time separation intervals to obtain a second sub-chain; extracting the second sub-chain corresponding to the maximum value of the time length to obtain at least one third data chain;
calculating an associated user set corresponding to the target user according to the association degree;
calculating the electricity consumption of the target user in the target time period according to the associated user set;
and distributing power to the target user according to the power consumption of the target user in the target time period.
2. The power consumer behavior analysis method of claim 1, wherein: calculating a first data chain respectively corresponding to the target user and the user in the park according to the three-dimensional matrix corresponding to the park in the current time separation period, wherein the first data chain comprises the following components:
obtaining characteristic points according to the three-dimensional matrix corresponding to the park in the previous time separation periodWherein->For the category of the user, < >>For user->In the time interval->Is (are) the electricity consumption of the car>For the ith time interval, +.>For user->In the time interval->Is used up of the time stamp of electricity;
and sequencing the characteristic points according to the time stamps to respectively obtain first data chains corresponding to the target users and the users in the campus.
3. The power consumer behavior analysis method of claim 2, wherein: calculating the matching degree between the first sub-chains respectively corresponding to the target user and the users in the campus comprises the following steps:
by the formula:calculating the matching degree between the first sub-chains respectively corresponding to the target user and the users in the campus>Wherein->For the power consumption of the target user u at the time stamp t +.>For the power consumption of the users w in the campus at the time stamp t +.>And->Respectively corresponding to the minimum value and the maximum value of the electricity consumption of the first sub-chain for the target user u, +.>And->And respectively obtaining the minimum value and the maximum value of the electricity consumption of the first sub-chain corresponding to the user w in the park.
4. A method of power consumer behavior analysis according to claim 3, wherein: calculating an associated user set corresponding to the target user according to the association degree, wherein the method comprises the following steps:
sequentially calculating the degree of assimilation between the target user and different users in the park according to the lengths of the first sub-chain and the third data chain of the second data chain;
carrying out layered distribution on different users in a park according to the degree of assimilation to obtain a layered distribution diagram;
and calculating a selected area, and extracting park users corresponding to the selected area by the layered distribution map to obtain an associated user set.
5. The power consumer behavior analysis method of claim 4, wherein: and sequentially calculating the degree of identity between the target user and different users in the campus according to the lengths of the first sub-chain and the third data chain of the second data chain, wherein the method comprises the following steps:
by the formula:calculating the degree of identity between the target user and the different users in the campus +.>Wherein->Pearson coefficients for target user u and campus user w>Is a dynamic weight.
6. The power consumer behavior analysis method of claim 5, wherein: calculating the electricity consumption of the target user in the target time period according to the associated user set, wherein the electricity consumption comprises the following steps:
the target time period corresponds to the time separation interval in the last time separation period, and a target historical time separation interval is obtained;
acquiring the electricity consumption of each element in the associated user set in the target historical time separation interval;
calculating average power consumption of each element in the target user and the associated user set for the current segmentation period;
and determining the electricity consumption of the target user in the target time period according to the electricity consumption of each element in the associated user set in the target historical time separation interval and the average electricity consumption of each element in the target user and the associated user set for the current segmentation period.
7. The power consumer behavior analysis method of claim 6, wherein: according to the electricity consumption of each element in the associated user set in the target historical time separation interval and the average electricity consumption of each element in the associated user set for the current segmentation period, determining the electricity consumption of the target user in the target time period comprises the following steps:
by the formula:calculating the power consumption of the target user in the target time period, wherein +.>For the power consumption of the target user u in the time division section of the target time period corresponding to the current time division period,/>To average the power consumption of the target user for the time-divided interval corresponding to the current time-divided period,for the average power consumption of the user v in the time division section corresponding to the current time division period in the associated user set,/>For the associated user set the power consumption of user v in said target historical time interval, +.>The degree of assimilation between the user v is concentrated for the target user and the associated user.
8. An electric power user behavior analysis system, characterized in that: the system comprises:
the construction module (10) is used for acquiring the electricity utilization data corresponding to the users in the park and constructing a three-dimensional matrix corresponding to the park according to the electricity utilization data, and comprises the following steps: setting a time separation period, wherein the time separation period comprises at least one time separation interval; acquiring user electricity data corresponding to at least one time separation interval in the current time separation period; sorting and grouping the user electricity data according to unique identifiers respectively corresponding to the user and the time separation interval to obtain a three-dimensional matrix corresponding to the park in the current time separation period;
the calculating module (20) is used for calculating the association degree of the target user and the user in the park according to the three-dimensional matrix, and comprises the following steps: calculating first data chains respectively corresponding to the target users and the users in the park according to the three-dimensional matrix corresponding to the park in the current time separation period; obtaining at least one first sub-chain of the first data chain according to at least one time separation interval in the current time separation period; setting a matching threshold, calculating the matching degree between the first sub-chains corresponding to the target user and the users in the park, and if the matching degree is smaller than the matching threshold, judging that the first sub-chain corresponding to the target user is matched with the first sub-chain corresponding to the users in the park; extracting matched first sub-chains in first data chains respectively corresponding to the target user and the users in the park to generate a second data chain; traversing a time separation interval corresponding to a first sub-chain in the second data chain, and connecting the first sub-chains corresponding to adjacent time separation intervals to obtain a second sub-chain; extracting the second sub-chain corresponding to the maximum value of the time length to obtain at least one third data chain;
the association module (30) is used for calculating an association user set corresponding to the target user according to the association degree;
a prediction module (40) for calculating the electricity consumption of the target user in the target time period according to the associated user set;
and the distribution module (50) is used for distributing power to the target users according to the power consumption of the target users in the target time period.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117114368A (en) * 2023-10-23 2023-11-24 西安航空学院 Industrial big data safety monitoring system and method based on artificial intelligence

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005275819A (en) * 2004-03-25 2005-10-06 Hitachi Ltd Method for adjusting gain and loss between dispersed power source user and power user, and mediating device thereof
CN112288467A (en) * 2020-10-23 2021-01-29 广州创晨能源技术有限公司 User electricity utilization analysis and management method and device based on block chain technology
CN112446519A (en) * 2019-08-28 2021-03-05 国网福建省电力有限公司经济技术研究院 Power demand prediction method and system for incremental power distribution park
CN113988723A (en) * 2021-12-28 2022-01-28 广东电网有限责任公司佛山供电局 User behavior locking method and system based on power consumption data anomaly analysis
CN114091783A (en) * 2021-11-30 2022-02-25 广东电网有限责任公司 Enterprise electricity utilization early warning method and device, computer equipment and storage medium
KR20220061713A (en) * 2020-11-06 2022-05-13 고려대학교 산학협력단 Method of replacing missing value in smart meter and control system of smart meter using the same
CN115099502A (en) * 2022-06-29 2022-09-23 四川大学 Short-term power load prediction method based on inter-user power consumption behavior similarity
CN115660225A (en) * 2022-12-13 2023-01-31 浙江万胜智能科技股份有限公司 Electricity load prediction management method and system based on ammeter communication module

Patent Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005275819A (en) * 2004-03-25 2005-10-06 Hitachi Ltd Method for adjusting gain and loss between dispersed power source user and power user, and mediating device thereof
CN112446519A (en) * 2019-08-28 2021-03-05 国网福建省电力有限公司经济技术研究院 Power demand prediction method and system for incremental power distribution park
CN112288467A (en) * 2020-10-23 2021-01-29 广州创晨能源技术有限公司 User electricity utilization analysis and management method and device based on block chain technology
KR20220061713A (en) * 2020-11-06 2022-05-13 고려대학교 산학협력단 Method of replacing missing value in smart meter and control system of smart meter using the same
CN114091783A (en) * 2021-11-30 2022-02-25 广东电网有限责任公司 Enterprise electricity utilization early warning method and device, computer equipment and storage medium
CN113988723A (en) * 2021-12-28 2022-01-28 广东电网有限责任公司佛山供电局 User behavior locking method and system based on power consumption data anomaly analysis
CN115099502A (en) * 2022-06-29 2022-09-23 四川大学 Short-term power load prediction method based on inter-user power consumption behavior similarity
CN115660225A (en) * 2022-12-13 2023-01-31 浙江万胜智能科技股份有限公司 Electricity load prediction management method and system based on ammeter communication module

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
BARIS AKSANLI等: "User Behavior Modeling for Estimating Residential Energy Consumption", 《SMART CITY 360》 *
曹梦;刘宝成;何金;张春晖;胡泉伟;: "基于前趋势相似度的细粒度用户用电负荷预测", 计算机应用与软件, no. 07 *
李志敏: "基于数据挖掘的工业用户用电行为分析及辨识研究", 《 中国优秀硕士论文 工程科技Ⅱ辑》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117114368A (en) * 2023-10-23 2023-11-24 西安航空学院 Industrial big data safety monitoring system and method based on artificial intelligence
CN117114368B (en) * 2023-10-23 2024-01-23 西安航空学院 Industrial big data safety monitoring system and method based on artificial intelligence

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