CN115759541A - Ordered electricity utilization behavior judgment method based on big data analysis - Google Patents
Ordered electricity utilization behavior judgment method based on big data analysis Download PDFInfo
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Abstract
The invention discloses a big data analysis-based orderly power utilization behavior judgment method, which comprises the following steps: s1: acquiring daily power consumption data of power consumers in the intelligent electric meters in the residential area; s2: transversely comparing the power consumption data of different power consumers in the same date to obtain expected peak-shifting consumers; s3: longitudinally comparing the power consumption data of the expected peak shifting user in different dates to obtain expected peak shifting power; s4: determining a designated day of ordered power utilization according to the power utilization plan of the transformer area, and setting ordered power utilization conditions of the expected peak shifting users on the designated day according to the expected peak shifting power; s5: and judging whether the power consumption data of the expected peak shifting user on the specified day meets the ordered power consumption condition, and if so, considering the ordered power consumption. According to the method, the power utilization power data of the power consumers every day is subjected to big data analysis, the ordered power utilization condition is further set, whether the condition is met is judged, and whether the ordered power utilization behavior of the consumers is executed can be judged.
Description
Technical Field
The invention relates to the field of data processing, in particular to a method for judging ordered power utilization behaviors based on big data analysis.
Background
The orderly power utilization refers to the management work of controlling part of power utilization requirements of users on demand sides through administrative measures, economic means and technical methods under the conditions of insufficient power supply, sudden accidents (events) and the like so as to maintain the order of power supply and utilization on the premise of ensuring the power supply safety of a large power grid.
With the improvement of living standard of people, the load increase demand of a distribution area is faster due to the fact that the active power consumption of single electrical equipment is larger and larger, and due to the fact that the power supply capacity of the distribution area is limited and other unordered power utilization behaviors, the phenomena of tripping, power failure and the like of the distribution area are prone to occurring. Because the existing station capacity increasing speed cannot keep up with the increasing speed of the load demand, the optimal configuration of the existing power distribution station power supply capacity through orderly power utilization monitoring is particularly important. The orderly power utilization refers to that under the condition of insufficient power supply or unbalanced supply and demand, technical means are adopted to manage power utilization so as to realize the work of stable power utilization order and balanced power and electricity quantity.
In a traditional orderly power utilization monitoring method, the power system staff randomly perform switching-off and power limiting according to the running state of a power distribution area so as to ensure the reliability of the whole power supply of the power distribution area. However, the conventional method cannot guarantee the reliability of the overall power supply and simultaneously consider the economic operation of the power distribution area in the process of orderly power utilization monitoring. The problem that the prior art is difficult to solve is how to implement the orderly power utilization under the condition of not influencing the power utilization of users as much as possible.
Disclosure of Invention
The invention provides an ordered power utilization behavior judgment method based on big data analysis, aiming at the problem that the ordered power utilization is difficult to be realized under the condition that the power utilization of a user is not influenced as much as possible in the prior art.
The technical scheme of the invention is as follows.
The ordered power utilization behavior judgment method based on big data analysis comprises the following steps:
s1: acquiring daily power data of power users in intelligent electric meters in residential areas;
s2: transversely comparing power consumption data of different power consumers in the same date to obtain expected peak shifting consumers;
s3: longitudinally comparing the power consumption data of the expected peak staggering users in different dates to obtain expected peak staggering power;
s4: determining a designated day of ordered power utilization according to the power utilization plan of the transformer area, and setting ordered power utilization conditions of expected peak shifting users on the designated day according to expected peak shifting power;
s5: and judging whether the power consumption data of the expected peak shifting user on the specified day meets the ordered power consumption condition, and if so, considering the ordered power consumption.
According to the method, big data analysis is carried out on the daily power consumption data of the power consumers, the power consumption data of a large number of users are synthesized to carry out transverse and longitudinal comparison, the expected peak shifting users needing orderly power consumption and the expected peak shifting power of the expected peak shifting users are sequentially judged, the orderly power consumption condition is further set, whether the condition is met is judged, and whether the orderly power consumption behavior of the users is executed can be judged.
Preferably, in S2, the laterally comparing the power consumption data of different power consumers in the same date to obtain the expected peak-shifting consumer includes:
calling power consumption data of power consumers in the same date, and drawing a change curve graph of the power consumption on a time axis;
judging the fluctuation amplitude of the power consumption of the power consumers in the date, sequencing the power consumers according to the fluctuation amplitude, and selecting the power consumers 30% in front of the fluctuation amplitude as candidate peak staggering users;
and marking the time period of the power consumption peak value of each candidate peak-shifting user, and selecting the candidate peak-shifting user as an expected peak-shifting user if the time period is the peak time period of the power consumption of the distribution area.
Preferably, in S3, the longitudinally comparing the power consumption data of the expected peak shifting users in different dates to obtain the expected peak shifting power includes:
calling power consumption data of expected peak-off users in different dates, and drawing a variation curve graph of the power consumption on a time axis; judging the resident power and the non-resident power of each expected peak shifting user from a change curve chart corresponding to the power consumption of the expected peak shifting users;
the non-stationary power is taken as the expected peak-to-peak power.
Preferably, the resident power determining process includes:
selecting a plurality of sampling moments at fixed time intervals from a change curve graph corresponding to the power consumption of an expected peak-staggering user, reserving the instantaneous power consumption corresponding to the sampling moments, recording as sampling points, and obtaining a sampling set;
selecting sampling moments from the change curve graphs of different dates at the same time interval and different starting moments, reserving instantaneous power consumption corresponding to the sampling moments, recording the instantaneous power consumption as sampling points, and obtaining a plurality of groups of sampling sets, wherein the sampling points at the same moment do not exist among all the sampling sets;
summarizing the groups of sampling sets into a coordinate system, and sequentially connecting all sampling points in the coordinate system according to a time sequence to obtain a composite power curve;
judging stable paragraphs in the composite power curve, judging whether the difference value of the average power values among the stable paragraphs is smaller than a preset proportion, if so, forming a paragraph group, otherwise, discarding the stable paragraphs;
the average power values of the paragraph groups are sorted with the lowest average power value as the resident power.
Preferably, the determining the stable segment in the composite power curve includes:
and judging the slope of the composite power curve, wherein the section with the slope smaller than the preset slope is a stable section.
Preferably, in the change curve graph corresponding to the power consumption of the expected peak-shifting user, after the resident power is removed, the remaining power is the non-resident power.
Preferably, in S4, the setting of the ordered power utilization condition of the expected peak shifting user on the specified day according to the expected peak shifting power includes:
the ordered electricity utilization conditions of the anticipatory peak shifting user on the specified days are: in the preset peak period, the peak-to-peak power is expected to occur at a time not higher than 50%.
The invention also provides electronic equipment which comprises a memory and a processor, wherein the memory stores computer programs, and the processor realizes the steps of the ordered power utilization behavior judgment method based on big data analysis when calling the computer programs in the memory.
The invention also provides a storage medium, wherein the storage medium stores computer executable instructions, and the computer executable instructions are loaded and executed by a processor to realize the steps of the ordered power utilization behavior judgment method based on big data analysis.
The substantial effects of the invention include:
the method comprises the steps of carrying out big data analysis on power consumption power data of power consumers every day, carrying out transverse and longitudinal comparison on the power consumption power data of a large number of users in a comprehensive mode, sequentially judging expected peak shifting users needing orderly power consumption and expected peak shifting power of the expected peak shifting users, further setting an orderly power consumption condition, judging whether the condition is met, and judging whether orderly power consumption behaviors of the users are executed.
In the judgment process of the expected peak shifting users, the large data is analyzed for a large number of power users, in the judgment process of the expected peak shifting power, a large number of sampling points are recombined, the obtained expected peak shifting power can represent the actual situation, and therefore the condition setting of ordered power utilization is more accurate.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to 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.
It should be understood that, in the various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association relationship describing an associated object, meaning that there may be three relationships, for example, and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprising a, B and C", "comprising a, B, C" means that all three of a, B, C are comprised, "comprising a, B or C" means comprising one of a, B, C, "comprising a, B and/or C" means comprising any 1 or any 2 or 3 of a, B, C.
The technical solution of the present invention will be described in detail below with specific examples. Embodiments may be combined with each other and some details of the same or similar concepts or processes may not be repeated in some embodiments.
Example (b):
the orderly power utilization behavior judgment method based on big data analysis comprises the following steps as shown in figure 1:
s1: and acquiring the daily power consumption data of power consumers in the intelligent electric meters in the residential area.
In this embodiment, the power consumption data of each day includes data relating power consumption to time.
S2: transversely comparing the power consumption data of different power consumers in the same date to obtain expected peak-shifting consumers, comprising the following steps:
calling power consumption data of power consumers in the same date, and drawing a change curve graph of the power consumption on a time axis;
judging the fluctuation amplitude of the power consumption of the power consumers in the date, sequencing the power consumers according to the fluctuation amplitude, and selecting the power consumers 30% in front of the fluctuation amplitude as candidate peak-shifting consumers;
and marking the time period of the power consumption peak value of each candidate peak-shifting user, and selecting the candidate peak-shifting user as an expected peak-shifting user if the time period is the peak time period of the power consumption of the distribution area.
S3: the method for longitudinally comparing the power consumption data of the expected peak shifting users in different dates to obtain the expected peak shifting power comprises the following steps:
calling power consumption data of expected peak-off users in different dates, and drawing a variation curve graph of the power consumption on a time axis; judging the resident power and the non-resident power of each expected peak staggering user from a change curve chart corresponding to the power consumption of the expected peak staggering users;
the non-stationary power is taken as the expected peak-to-peak power.
Wherein the resident power determining process comprises:
selecting a plurality of sampling moments at fixed time intervals from a change curve graph corresponding to the power consumption of an expected peak shifting user, reserving the instantaneous power consumption corresponding to the sampling moments, recording the instantaneous power consumption as sampling points, and obtaining a sampling set;
selecting sampling moments from the change curve graphs of different dates at the same time interval and different starting moments, reserving instantaneous power consumption corresponding to the sampling moments, recording the instantaneous power consumption as sampling points, and obtaining a plurality of groups of sampling sets, wherein the sampling points at the same moment do not exist among all the sampling sets;
summarizing the groups of sampling sets into a coordinate system, and sequentially connecting all sampling points in the coordinate system according to a time sequence to obtain a composite power curve;
judging stable paragraphs in the composite power curve, judging whether the difference value of the average power values among the stable paragraphs is smaller than a preset proportion, if so, forming a paragraph group, otherwise, discarding the stable paragraphs;
the average power values of the paragraph groups are sorted, with the lowest average power value being the resident power.
Wherein, judging the stable paragraph in the composite power curve includes: and judging the slope of the composite power curve, wherein the section with the slope smaller than the preset slope is a stable section.
In the variation curve diagram corresponding to the power consumption of the expected peak-shifting user, after the resident power is removed, the remaining power is the non-resident power.
S4: determining the appointed day of the ordered power utilization according to the power utilization plan of the transformer area, and setting the ordered power utilization condition of the expected peak shifting user on the appointed day according to the expected peak shifting power, wherein the ordered power utilization condition comprises the following steps:
the ordered electricity utilization conditions of the anticipating peak shifting user on a given day are as follows: in the preset peak period, the time of occurrence of peak-to-peak power is expected to be not higher than 50%.
S5: and judging whether the power consumption data of the expected peak shifting user on the specified day meets the ordered power consumption condition, and if so, considering the ordered power consumption.
This embodiment is through carrying out big data analysis to the power consumption power data of power consumer every day, synthesizes a large amount of users 'power consumption power data and carries out horizontal and fore-and-aft contrast, judges in proper order that the anticipated peak shifting user that needs carry out orderly power consumption and the anticipated peak shifting power of anticipated peak shifting user, and then sets up the orderly power consumption condition, judges whether to satisfy this condition, can judge whether user's orderly power consumption action is executed.
The invention also provides electronic equipment which comprises a memory and a processor, wherein the memory stores computer programs, and the processor realizes the steps of the ordered power utilization behavior judgment method based on big data analysis when calling the computer programs in the memory.
The invention also provides a storage medium, wherein the storage medium stores computer executable instructions, and the computer executable instructions are loaded and executed by a processor to realize the steps of the ordered power utilization behavior judgment method based on big data analysis.
The substantial effects of the present embodiment include:
the method comprises the steps of carrying out big data analysis on power utilization power data of power consumers every day, carrying out transverse and longitudinal comparison on the power utilization power data of a large number of users in a comprehensive mode, sequentially judging expected peak shifting users needing orderly power utilization and expected peak shifting power of the expected peak shifting users, further setting an orderly power utilization condition, judging whether the condition is met, and judging whether orderly power utilization behaviors of the users are executed.
In the judgment process of the expected peak shifting users, the large data is analyzed for a large number of power users, in the judgment process of the expected peak shifting power, a mode of recombining a large number of sampling points is adopted, the obtained expected peak shifting power can represent the actual situation, and therefore the condition setting of ordered power utilization is more accurate.
Through the description of the above embodiments, those skilled in the art will understand that, for convenience and simplicity of description, only the division of the above functional modules is used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of a specific device is divided into different functional modules to complete all or part of the above described functions.
In the embodiments provided in the present application, it should be understood that the disclosed structures and methods may be implemented in other ways. For example, the above-described embodiments with respect to structures are merely illustrative, and for example, a module or a unit may be divided into only one logic function, and may have another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another structure, 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, structures or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. 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 application 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 may be implemented in the form of hardware, or may also be implemented in the 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 readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a variety of media that can store program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The ordered power utilization behavior judgment method based on big data analysis is characterized by comprising the following steps of:
s1: acquiring daily power data of power users in intelligent electric meters in residential areas;
s2: transversely comparing power consumption data of different power consumers in the same date to obtain expected peak shifting consumers;
s3: longitudinally comparing the power consumption data of the expected peak shifting user in different dates to obtain expected peak shifting power;
s4: determining a designated day of ordered power utilization according to the power utilization plan of the transformer area, and setting ordered power utilization conditions of expected peak shifting users on the designated day according to expected peak shifting power;
s5: and judging whether the power consumption data of the expected peak shifting user on the specified day meets the ordered power consumption condition, and if so, considering the ordered power consumption.
2. The ordered power consumption behavior judgment method based on big data analysis according to claim 1, wherein in S2, power consumption power data of different power consumers in the same date are transversely compared to obtain expected peak shifting consumers, and the method includes: calling power consumption data of power consumers in the same date, and drawing a change curve graph of the power consumption on a time axis;
judging the fluctuation amplitude of the power consumption of the power consumers in the date, sequencing the power consumers according to the fluctuation amplitude, and selecting the power consumers 30% in front of the fluctuation amplitude as candidate peak-shifting consumers;
and marking the time period of the power consumption peak value of each candidate peak-shifting user, and selecting the candidate peak-shifting user as an expected peak-shifting user if the time period is the peak time period of the power consumption of the distribution area.
3. The ordered power consumption behavior judgment method based on big data analysis according to claim 1, wherein in S3, the longitudinal comparison of the power consumption data of the expected peak shifting users in different dates is performed to obtain the expected peak shifting power, and the method comprises: calling power consumption data of expected peak-off users in different dates, and drawing a variation curve graph of the power consumption on a time axis;
judging the resident power and the non-resident power of each expected peak staggering user from a change curve chart corresponding to the power consumption of the expected peak staggering users;
the non-stationary power is taken as the expected peak-to-peak power.
4. The ordered power consumption behavior judgment method based on big data analysis as claimed in claim 3, wherein the resident power judgment process comprises:
selecting a plurality of sampling moments at fixed time intervals from a change curve graph corresponding to the power consumption of an expected peak-staggering user, reserving the instantaneous power consumption corresponding to the sampling moments, recording as sampling points, and obtaining a sampling set;
selecting sampling moments from the change curve graphs of different dates at the same time interval and different starting moments, reserving instantaneous power consumption corresponding to the sampling moments, recording the instantaneous power consumption as sampling points, and obtaining a plurality of groups of sampling sets, wherein the sampling points at the same moment do not exist among all the sampling sets;
summarizing the groups of sampling sets into a coordinate system, and sequentially connecting all sampling points in the coordinate system according to a time sequence to obtain a composite power curve;
judging stable paragraphs in the composite power curve, judging whether the difference value of the average power values among the stable paragraphs is smaller than a preset proportion, if so, forming a paragraph group, otherwise, discarding the stable paragraphs;
the average power values of the paragraph groups are sorted, with the lowest average power value being the resident power.
5. The ordered power consumption behavior judgment method based on big data analysis according to claim 4, wherein the judging of the stable segment in the composite power curve comprises:
and judging the slope of the composite power curve, wherein the section with the slope smaller than the preset slope is a stable section.
6. The ordered power consumption behavior judgment method based on big data analysis according to claim 4, wherein in a change curve graph corresponding to the power consumption of the expected peak-staggering user, after the resident power is removed, the remaining power is the non-resident power.
7. The ordered power utilization behavior judgment method based on big data analysis according to claim 4, wherein in S4, setting the ordered power utilization condition of the expected peak shifting user on the specified day according to the expected peak shifting power comprises:
the ordered electricity utilization conditions of the anticipating peak shifting user on a given day are as follows: in the preset peak period, the time of occurrence of peak-to-peak power is expected to be not higher than 50%.
8. An electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the ordered power consumption behavior judgment method based on big data analysis according to any one of claims 1 to 7 when calling the computer program in the memory.
9. A storage medium, wherein computer-executable instructions are stored in the storage medium, and when being loaded and executed by a processor, the storage medium implements the steps of the ordered power consumption behavior judgment method based on big data analysis according to any one of claims 1 to 7.
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