Method for judging orderly power consumption behavior based on big data analysis

Abstract

The application discloses an ordered power consumption behavior judgment method based on big data analysis, which comprises the following steps: S1, collecting the daily power consumption data of power users in intelligent electric meters in residential areas; S2, horizontally comparing the power consumption data of different power users in the same day to obtain expected peak-shaving users; S3, longitudinally comparing the power consumption data of the expected peak-shaving users in different days to obtain expected peak-shaving power; S4, determining the designated day of ordered power consumption according to the power consumption planning of a transformer area, and setting the ordered power consumption conditions of the expected peak-shaving users in the designated day according to the expected peak-shaving power; and S5, judging whether the power consumption data of the expected peak-shaving users in the designated day meets the ordered power consumption conditions, and considering that the ordered power consumption is performed if the ordered power consumption conditions are met. The daily power consumption data of power users is analyzed by the application, and then the ordered power consumption conditions are set, and whether the ordered power consumption behavior of the users is performed can be judged by judging whether the conditions are met.

Description

Technical Field

[0001] This invention relates to the field of data processing, and in particular to a method for judging orderly electricity consumption behavior based on big data analysis. Background Technology

[0002] Orderly electricity use refers to the management work of controlling some of the electricity demand of users on the demand side through administrative measures, economic means, and technical methods in the event of insufficient power supply or sudden accidents (events), so as to ensure the safety of the power supply of the large power grid and maintain the stable and orderly order of power supply and use.

[0003] With the improvement of people's living standards, the active power consumption of individual electrical appliances is increasing, leading to a rapid increase in the load demand of distribution substations. Due to the limited power supply capacity of distribution substations and other disorderly electricity consumption behaviors, power outages and other phenomena are prone to occur frequently. Because the current rate of capacity expansion of distribution substations cannot keep up with the rate of load demand growth, optimizing the power supply capacity of existing distribution substations through orderly electricity consumption monitoring is particularly important. Orderly electricity consumption refers to the use of technical means to manage electricity consumption in situations of insufficient power supply or imbalance between supply and demand, in order to achieve stable electricity consumption and a balance between power generation and consumption.

[0004] Traditional methods of monitoring orderly electricity consumption involve power system personnel randomly implementing power rationing based on the operational status of distribution substations to ensure the overall reliability of power supply to those substations. However, these traditional methods cannot simultaneously guarantee the reliability of the overall power supply and ensure the economical operation of the distribution substations. How to implement orderly electricity consumption with minimal disruption to user power usage remains a problem that current technology struggles to solve. Summary of the Invention

[0005] To address the problem that existing technologies struggle to implement orderly electricity use with minimal disruption to users' electricity consumption, this invention provides a method for judging orderly electricity use behavior based on big data analysis. By performing big data analysis on the daily power consumption data of electricity users and comparing the power consumption data of a large number of users, the method identifies users expected to stagger their peak hours and the corresponding orderly electricity use conditions, thereby judging orderly electricity use behavior and facilitating the implementation of orderly electricity use.

[0006] The following is the technical solution of the present invention.

[0007] A method for judging orderly electricity consumption behavior based on big data analysis includes the following steps: S1: Collect daily power consumption data of electricity users in smart meters within residential areas; S2: Compare the power consumption data of different electricity users on the same day to identify the expected off-peak users; S3: Perform a longitudinal comparison of the power consumption data of users expected to stagger peak hours on different days to obtain the expected staggered peak power; S4: Determine the designated days for orderly electricity use based on the power consumption plan of the distribution area, and set the orderly electricity use conditions for users expected to stagger peak power on the designated days based on the expected peak power. S5: Determine whether the power consumption data of the expected off-peak users on the specified day meets the conditions for orderly power consumption. If it does, it is considered to be orderly power consumption.

[0008] This invention performs big data analysis on the daily power consumption data of electricity users, compares the power consumption data of a large number of users horizontally and vertically, and determines the expected peak-shifting power of users who need to use electricity in an orderly manner. Then, it sets the conditions for orderly electricity use and judges whether the conditions are met, thereby determining whether the user's orderly electricity use behavior is executed.

[0009] Preferably, in step S2, a horizontal comparison is performed on the power consumption data of different electricity users on the same day to obtain the expected peak-shifting users, including: Retrieve power consumption data of electricity users within the same date and plot the power consumption change curve on the time axis; Determine the fluctuation range of electricity consumption of power users within the specified date and rank the power users according to the magnitude of the fluctuation range. Select the power users with the largest fluctuation range as candidate peak-shifting users. The peak power consumption period of each candidate staggered peak user is marked. If the peak power consumption period is during the peak power consumption period of the distribution area, the candidate staggered peak user is selected as the expected staggered peak user.

[0010] Preferably, in step S3, the expected peak-shifting power is obtained by longitudinally comparing the power consumption data of the expected peak-shifting users on different dates, including: Retrieve electricity consumption data of users expected to stagger peak hours on different dates and plot the change curve of electricity consumption on the time axis; from the change curve of electricity consumption of users expected to stagger peak hours, determine the permanent and non-permanent power of each user expected to stagger peak hours. Non-stationary power is used as the expected peak-shifting power.

[0011] Preferably, the process for determining the resident power includes: From the power consumption change curve of expected off-peak users, select a number of sampling moments at fixed time intervals, retain the instantaneous power consumption corresponding to the sampling moment, record it as the sampling point, and obtain the sampling set; Using the same time interval but different start times, the sampling times are selected for the change curves of different dates. The instantaneous power consumption corresponding to the sampling time is retained and recorded as the sampling point, resulting in several sampling sets. There are no sampling points at the same time among all the sampling sets. The aforementioned sets of samples are aggregated into a coordinate system, and all sampling points in the coordinate system are connected sequentially according to time order to obtain the composite power curve; Identify stable segments in the composite power curve and determine whether the difference in average power values ​​between stable segments is less than a preset ratio. If it is less, form a segment group; otherwise, discard the stable segment. The average power values ​​of the paragraph groups are sorted, and the lowest average power value is taken as the resident power.

[0012] Preferably, the determination of the stable segment in the composite power curve includes: Determine the slope of the composite power curve; segments with a slope less than the preset slope are considered stable segments.

[0013] Preferably, in the power consumption variation curve of the expected off-peak users, the remaining power after removing the resident power is the non-resident power.

[0014] Preferably, in step S4, setting the orderly electricity consumption conditions for expected peak-shifting users on a specified day based on the expected peak-shifting power includes: The expected orderly electricity consumption conditions for staggered peak users on a designated day are: during the preset peak period, the expected staggered peak power consumption time is no more than 50%.

[0015] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-described method for judging orderly electricity consumption behavior based on big data analysis when it calls the computer program in the memory.

[0016] The present invention also provides a storage medium storing computer-executable instructions, which, when loaded and executed by a processor, implement the steps of the above-described method for judging orderly electricity consumption behavior based on big data analysis.

[0017] The substantial effects of this invention include: By conducting big data analysis on the daily power consumption data of electricity users, and comparing the power consumption data of a large number of users horizontally and vertically, it is possible to determine the users who need to implement orderly power consumption and the expected peak-shaving power of these users. Then, orderly power consumption conditions are set, and it is determined whether the conditions are met, thus determining whether the user's orderly power consumption behavior is executed.

[0018] The determination of expected peak-shifting users is obtained through big data analysis of a large number of electricity users. The determination of expected peak-shifting power adopts a combination of a large number of sampling points, and the obtained expected peak-shifting power is more representative of the actual situation. Therefore, the conditions for orderly electricity use are set more accurately. Attached Figure Description

[0019] Figure 1 This is a flowchart of an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0022] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0023] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, "and / or B" can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0024] The technical solution of the present invention will be described in detail below with reference to specific embodiments. Embodiments may be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0025] Example: Methods for judging orderly electricity consumption behavior based on big data analysis, such as Figure 1 As shown, it includes the following steps: S1: Collect daily power consumption data of electricity users in smart meters within residential areas.

[0026] In this embodiment, the daily power consumption data includes the correlation data between power consumption and time.

[0027] S2: By comparing the power consumption data of different electricity users on the same day, the expected peak-shifting users are identified, including: Retrieve power consumption data of electricity users within the same date and plot the power consumption change curve on the time axis; Determine the fluctuation range of electricity consumption of power users within the specified date and rank the power users according to the magnitude of the fluctuation range. Select the power users with the largest fluctuation range as candidate peak-shifting users. The peak power consumption period of each candidate staggered peak user is marked. If the peak power consumption period is during the peak power consumption period of the distribution area, the candidate staggered peak user is selected as the expected staggered peak user.

[0028] S3: Perform a longitudinal comparison of the power consumption data of users expected to stagger their peak consumption on different dates to obtain the expected peak-shaving power, including: Retrieve electricity consumption data of users expected to stagger peak hours on different dates and plot the change curve of electricity consumption on the time axis; from the change curve of electricity consumption of users expected to stagger peak hours, determine the permanent and non-permanent power of each user expected to stagger peak hours. Non-stationary power is used as the expected peak-shifting power.

[0029] The process for determining the resident power includes: From the power consumption change curve of expected off-peak users, select a number of sampling moments at fixed time intervals, retain the instantaneous power consumption corresponding to the sampling moment, record it as the sampling point, and obtain the sampling set; Using the same time interval but different start times, the sampling times are selected for the change curves of different dates. The instantaneous power consumption corresponding to the sampling time is retained and recorded as the sampling point, resulting in several sampling sets. There are no sampling points at the same time among all the sampling sets. The aforementioned sets of samples are aggregated into a coordinate system, and all sampling points in the coordinate system are connected sequentially according to time order to obtain the composite power curve; Identify stable segments in the composite power curve and determine whether the difference in average power values ​​between stable segments is less than a preset ratio. If it is less, form a segment group; otherwise, discard the stable segment. The average power values ​​of the paragraph groups are sorted, and the lowest average power value is taken as the resident power.

[0030] The determination of stable segments in the composite power curve includes: determining the slope of the composite power curve; segments with a slope less than a preset slope are considered stable segments.

[0031] In the expected peak-shifting power consumption curve, the remaining power after removing the resident power is the non-resident power.

[0032] S4: Determine the designated date for orderly electricity consumption based on the power consumption plan for the distribution area, and set the orderly electricity consumption conditions for expected peak-shaving users on the designated date based on the expected peak-shaving power, including: The expected orderly electricity consumption conditions for staggered peak users on a designated day are: during the preset peak period, the expected staggered peak power consumption time is no more than 50%.

[0033] S5: Determine whether the power consumption data of the expected off-peak users on the specified day meets the conditions for orderly power consumption. If it does, it is considered to be orderly power consumption.

[0034] This embodiment performs big data analysis on the daily power consumption data of electricity users, compares the power consumption data of a large number of users horizontally and vertically, and determines the expected peak-shifting users who need to use electricity in an orderly manner and the expected peak-shifting power of the expected peak-shifting users. Then, it sets the conditions for orderly electricity use and determines whether the conditions are met, so as to determine whether the user's orderly electricity use behavior is executed.

[0035] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-described method for judging orderly electricity consumption behavior based on big data analysis when it calls the computer program in the memory.

[0036] The present invention also provides a storage medium storing computer-executable instructions, which, when loaded and executed by a processor, implement the steps of the above-described method for judging orderly electricity consumption behavior based on big data analysis.

[0037] The substantial effects of this embodiment include: By conducting big data analysis on the daily power consumption data of electricity users, and comparing the power consumption data of a large number of users horizontally and vertically, it is possible to determine the users who need to implement orderly power consumption and the expected peak-shaving power of these users. Then, orderly power consumption conditions are set, and it is determined whether the conditions are met, thus determining whether the user's orderly power consumption behavior is executed.

[0038] The determination of expected peak-shifting users is obtained through big data analysis of a large number of electricity users. The determination of expected peak-shifting power adopts a combination of a large number of sampling points, and the obtained expected peak-shifting power is more representative of the actual situation. Therefore, the conditions for orderly electricity use are set more accurately.

[0039] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0040] In the embodiments provided in this application, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another structure, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between structures or units, and may be electrical, mechanical, or other forms.

[0041] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0042] Furthermore, in the embodiments of this application, the functional units can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0043] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0044] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for judging orderly electricity consumption behavior based on big data analysis, characterized in that, Includes the following steps: S1: Collect daily power consumption data of electricity users in smart meters within residential areas; S2: Compare the power consumption data of different electricity users on the same day to identify the expected off-peak users; S3: Perform a longitudinal comparison of the power consumption data of users expected to stagger peak hours on different days to obtain the expected staggered peak power; S3 includes: retrieving electricity consumption data of expected off-peak users on different dates and plotting a curve of electricity consumption change on a time axis; determining the resident power and non-resident power of each expected off-peak user from the curve of electricity consumption change; using the non-resident power as the expected off-peak power; wherein, in the curve of electricity consumption change of expected off-peak users, the remaining power after removing the resident power is the non-resident power; The process for determining the resident power includes: selecting several sampling times at fixed time intervals from the power consumption change curves corresponding to the expected off-peak users, retaining the instantaneous power consumption corresponding to each sampling time, and recording it as a sampling point to obtain a sampling set; selecting sampling times from the change curves of different dates at the same time interval but different start times, retaining the instantaneous power consumption corresponding to each sampling time, and recording it as a sampling point to obtain several sets of sampling sets, where there are no sampling points at the same time among all sampling sets; summarizing the several sets of sampling sets into a coordinate system, and connecting all sampling points in the coordinate system in chronological order to obtain a composite power curve; determining the stable segments in the composite power curve, and determining whether the difference in average power values ​​between stable segments is less than a preset ratio, if less, forming a segment group, otherwise discarding the stable segment; sorting the average power values ​​of the segment groups, with the lowest average power value as the resident power; S4: Determine the designated days for orderly electricity use based on the power consumption plan of the distribution area, and set the orderly electricity use conditions for users expected to stagger peak power on the designated days based on the expected peak power. S5: Determine whether the power consumption data of the expected off-peak users on the specified day meets the conditions for orderly power consumption. If it does, it is considered to be orderly power consumption.

2. The method for judging orderly electricity consumption behavior based on big data analysis according to claim 1, characterized in that, In step S2, a horizontal comparison is performed on the power consumption data of different electricity users on the same day to obtain the expected peak-shifting users, including: Retrieve power consumption data of electricity users within the same date and plot the power consumption change curve on the time axis; Determine the fluctuation range of electricity consumption of power users within the specified date and rank the power users according to the magnitude of the fluctuation range. Select the power users with the largest fluctuation range as candidate peak-shifting users. The peak power consumption period of each candidate staggered peak user is marked. If the peak power consumption period is during the peak power consumption period of the distribution area, the candidate staggered peak user is selected as the expected staggered peak user.

3. The method for judging orderly electricity consumption behavior based on big data analysis according to claim 1, characterized in that, The determination of the stable segment in the composite power curve includes: Determine the slope of the composite power curve; segments with a slope less than the preset slope are considered stable segments.

4. The method for judging orderly electricity consumption behavior based on big data analysis according to claim 1, characterized in that, In step S4, setting the orderly electricity consumption conditions for expected peak-shifting users on a specified day based on the expected peak-shifting power includes: The expected orderly electricity consumption conditions for staggered peak users on a designated day are: during the preset peak period, the expected staggered peak power consumption time is no more than 50%.

5. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the steps of the method for judging orderly electricity consumption behavior based on big data analysis as described in any one of claims 1 to 4.

6. A storage medium, characterized in that, The storage medium stores computer-executable instructions, which, when loaded and executed by a processor, implement the steps of the method for judging orderly electricity consumption behavior based on big data analysis as described in any one of claims 1 to 4.

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