CN115459270A - Method and device for configuring urban peak electricity consumption, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses a method and a device for configuring urban peak power utilization, computer equipment and a storage medium, which are used for improving the power distribution accuracy and the power utilization rate during peak power utilization. The main technical scheme is as follows: determining a peak time period of the urban electricity consumption based on historical data of the urban electricity consumption; acquiring a first power utilization area in which the power consumption of the user exceeds a first preset numerical value, a second power utilization area in which the power consumption of the user is lower than a second preset numerical value and a third power utilization area in which the power consumption of the user is between the first preset numerical value and the second preset numerical value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value; inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into a self-encoder electricity quantity prediction model to obtain the predicted electricity utilization quantity of each electricity utilization user; and configuring the electricity consumption quantity for the corresponding electricity consumption user according to the predicted electricity consumption quantity.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a method and a device for configuring urban peak power utilization, computer equipment and a storage medium.
Background
With the rapid development of economy in China, the demand of society on power resources is continuously improved. In the using process of the power resource, some power utilization peak periods often appear, the occurrence of the power utilization peak periods can seriously affect the safe use of the power resource, and certain influence can be caused on power utilization customers.
At present, when electricity is used at a peak, the electricity consumption of an electricity consumer is predicted based on the historical usage of the electricity consumer, and then corresponding charges are configured for the electricity consumer based on the predicted electricity consumption.
Disclosure of Invention
The invention provides a method and a device for configuring urban peak electricity utilization, computer equipment and a storage medium, which are used for improving the power distribution accuracy and the utilization rate of electric power during peak electricity utilization.
The embodiment of the invention provides a method for configuring urban peak power consumption, which comprises the following steps:
determining a peak time period of the urban electricity utilization based on historical data of the urban electricity utilization;
acquiring a first power utilization area in which the power consumption of the user exceeds a first preset value, a second power utilization area in which the power consumption of the user is lower than a second preset value, and a third power utilization area in which the power consumption of the user is between the first preset value and the second preset value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value;
determining the electricity utilization attributes of electricity utilization users in the first electricity utilization area, the second electricity utilization area and the third electricity utilization area, wherein the electricity utilization attributes are life electricity utilization, production electricity utilization or public electricity utilization;
inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into a self-encoder electricity quantity prediction model to obtain the predicted electricity utilization quantity of each electricity utilization user;
and configuring the electricity consumption for the corresponding electricity consumption user according to the predicted electricity consumption.
The embodiment of the invention provides a configuration device for urban peak electricity utilization, which comprises:
the determining module is used for determining the peak time period of the urban electricity utilization based on the historical data of the urban electricity utilization;
the acquisition module is used for acquiring a first power utilization area in which the user power consumption exceeds a first preset numerical value, a second power utilization area in which the user power consumption is lower than a second preset numerical value and a third power utilization area in which the user power consumption is between the first preset numerical value and the second preset numerical value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value;
the determining module is further configured to determine power consumption attributes of power consumption users in the first power consumption area, the second power consumption area and the third power consumption area, where the power consumption attributes are domestic power consumption, production power consumption or public power consumption;
the prediction module is used for inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into the self-encoder electricity quantity prediction model to obtain the predicted electricity consumption quantity of each electricity utilization user;
and the configuration module is used for configuring the electricity consumption for the corresponding electricity consumption user according to the predicted electricity consumption.
A computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the above method for peak urban power allocation when executing said computer program.
A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the above-mentioned method for peak urban electricity deployment.
The invention provides a method, a device, computer equipment and a storage medium for configuring urban peak electricity consumption, which are characterized in that firstly, the peak time period of the urban electricity consumption is determined based on historical data of the urban electricity consumption; acquiring a first power utilization area in which the power consumption of the user exceeds a first preset value, a second power utilization area in which the power consumption of the user is lower than a second preset value, and a third power utilization area in which the power consumption of the user is between the first preset value and the second preset value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value; inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into an electricity quantity prediction model of a self-encoder to obtain the predicted electricity consumption of each electricity utilization user; and configuring the electricity consumption quantity for the corresponding electricity consumption user according to the predicted electricity consumption quantity. Compared with the prior art that the power consumption of the power consumers is predicted based on the historical power consumption of the power consumers, the predicted power consumption is obtained by predicting the self-encoder power consumption prediction model aiming at different power consumption areas, and therefore the power distribution accuracy and the power utilization rate in peak power consumption are improved through the method and the device.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a flow chart of a method for configuring peak urban electricity usage according to an embodiment of the present invention;
fig. 2 is a block diagram of an arrangement for peak power utilization in a city according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a computer device in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without inventive step based on the embodiments of the present invention, are within the scope of protection of the present invention.
As shown in fig. 1, an embodiment of the present invention provides a method for configuring peak electricity consumption in an urban area, which specifically includes the following steps:
and S101, determining the peak time period of the urban electricity consumption based on the historical data of the urban electricity consumption.
Specifically, the embodiment may acquire historical data of city electricity consumption in the last 5 years, and then analyze the historical data to acquire a peak time period of the electricity consumption. Wherein the peak time period may be a time period of a certain length in hours, days, months or quarters. For example, the peak time period is from 1 month to 10 months and 7 days, 8 months, and 2 pm to 4 pm, which is not specifically limited in this embodiment.
In an optional embodiment provided by the present invention, the determining the peak time period of the urban electricity based on the historical data of the urban electricity comprises:
and S1011, counting the daily electricity consumption in the historical data of the city electricity consumption.
And S1012, calculating daily average power consumption according to the daily power consumption, and calculating the product of the daily average power consumption and M to obtain the electric quantity balance value.
Wherein, M > =1.5.
And S1013, determining the corresponding time when the daily electricity consumption exceeds the electricity balance value in the historical data of the urban electricity consumption as the peak time period.
In this embodiment, if the daily electricity consumption in 8 months exceeds the electricity balance value, determining 8 months as a peak time period; and if the daily electricity consumption from 10 months 1 to 10 months 7 exceeds the electricity balance value, determining the 10 months 1 to 10 months 7 as peak time periods.
S102, acquiring a first power utilization area in which the user power consumption exceeds a first preset numerical value, a second power utilization area in which the user power consumption is lower than a second preset numerical value and a third power utilization area in which the user power consumption is between the first preset numerical value and the second preset numerical value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value.
The clustering algorithm in this embodiment may specifically be a Kmeans, DBSCAN-density-based spatial clustering algorithm, spectral clustering, GMM-gaussian mixture model, meanShift-mean shift, hierarchical clustering, and the like, which is not specifically limited in this embodiment.
In this embodiment, the first preset value may be specifically determined according to M times of the average value of the production electricity, and the second preset value may be determined according to M times of the average value of the public electricity, where M > =1.5.
In an optional embodiment of the present invention, the obtaining, by a clustering algorithm, a first power utilization region where the user power consumption exceeds a first preset value, a second power utilization region where the user power consumption is lower than a second preset value, and a third power utilization region where the user power consumption is between the first preset value and the second preset value in the peak time period includes: performing first clustering calculation on the user power consumption in the peak time period to obtain a first power consumption area in which the user power consumption exceeds a first preset numerical value; performing second clustering calculation on the user power consumption in the peak time period to obtain a second power consumption area in which the user power consumption is lower than a second preset value; and performing third clustering calculation on the user electricity consumption in the peak time period to obtain a third electricity utilization area with the user electricity consumption between the first preset numerical value and the second preset numerical value.
Wherein the radius of the first cluster calculation is larger than the radius of the second cluster calculation and the radius of the third cluster calculation, and the radius of the second cluster calculation is larger than the radius of the third cluster calculation. Specifically, the radius calculated by the first cluster is determined according to the relative distance of each industry in an actual industrial park, the radius calculated by the second cluster is determined according to the relative distance set by each power consumption in public power utilization, and the radius calculated by the third cluster is determined according to the relative distance of each building in a residential area.
S103, determining the electricity utilization attributes of electricity utilization users in the first electricity utilization area, the second electricity utilization area and the third electricity utilization area, wherein the electricity utilization attributes are life electricity utilization, production electricity utilization or public electricity utilization;
in an optional embodiment provided by the present invention, the determining the power consumption attributes of the power consumption users in the first power consumption area, the second power consumption area and the third power consumption area includes: determining the electricity utilization attribute of the electricity utilization users in the first electricity utilization area as production electricity utilization; determining the electricity utilization attribute of the electricity utilization users in the second electricity utilization area as public electricity utilization; and determining the electricity utilization attribute of the electricity utilization users in the third electricity utilization area as living electricity utilization.
S104, inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the electricity utilization data N days before the current time into an electricity quantity prediction model of a self-encoder to obtain the predicted electricity consumption of each electricity utilization user;
before inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the electricity utilization data N days before the current time into the self-encoder electricity quantity prediction model to obtain the predicted electricity consumption of each electricity utilization user, the method further comprises the following steps:
and taking the priority corresponding to the electricity utilization attribute of the electricity utilization users in each area in the peak time period and the N days of electricity utilization data before the current time as sample data, and taking the N +1 th day of electricity utilization data as a sample label for training to obtain the self-encoder electricity quantity prediction model.
Taking the electricity utilization data of the production users corresponding to the first electricity utilization area in the peak time period for N consecutive days as sample data, and taking the electricity utilization data of the (N + 1) th day as a sample label for training to obtain a self-encoder electricity quantity prediction model of the first electricity utilization area;
taking the electricity utilization data of the production users corresponding to the second electricity utilization area in the peak time period for N consecutive days as sample data, and taking the electricity utilization data of the (N + 1) th day as a sample label for training to obtain a self-encoder electricity quantity prediction model of the second electricity utilization area;
and taking the continuous N-day power consumption data of the production users corresponding to the third power consumption area in the peak time period as sample data, and taking the (N + 1) -th-day power consumption data as sample labels for training to obtain a self-encoder power consumption prediction model of the third power consumption area.
And S105, configuring the electricity consumption quantity for the corresponding electricity consumption user according to the predicted electricity consumption quantity.
In an embodiment of the present invention, before configuring the electricity consumption amount for the corresponding electricity consumption user according to the predicted electricity consumption amount, the method further includes: according to the electricity utilization attribute of the electricity utilization users, inputting the N days of electricity utilization data of the electricity utilization users before the current time into the self-encoder electricity quantity prediction model of the corresponding area to obtain the area prediction electricity consumption of each electricity utilization user; and calculating the average value of the area predicted electricity consumption and the predicted electricity consumption to obtain updated predicted electricity consumption. Correspondingly, the configuring the electricity consumption quantity for the corresponding electricity consumption user according to the predicted electricity consumption quantity comprises: and configuring the electricity consumption quantity for the corresponding electricity consumption user according to the updated predicted electricity consumption quantity.
The embodiment of the invention provides a method for configuring urban peak electricity consumption, which comprises the steps of firstly determining the peak time period of the urban electricity consumption based on the historical data of the urban electricity consumption; acquiring a first power utilization area in which the power consumption of the user exceeds a first preset numerical value, a second power utilization area in which the power consumption of the user is lower than a second preset numerical value and a third power utilization area in which the power consumption of the user is between the first preset numerical value and the second preset numerical value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value; inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into an electricity quantity prediction model of a self-encoder to obtain the predicted electricity consumption of each electricity utilization user; and configuring the electricity consumption quantity for the corresponding electricity consumption user according to the predicted electricity consumption quantity. Compared with the prior art that the power consumption of the power consumers is predicted based on the historical power consumption of the power consumers, the predicted power consumption is obtained by predicting the self-encoder power consumption prediction model aiming at different power consumption areas, and therefore the power distribution accuracy and the power utilization rate in peak power consumption are improved through the method and the device.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In one embodiment, an apparatus for allocating peak electricity in a city is provided, and the apparatus for allocating peak electricity in a city corresponds to the method for allocating peak electricity in a city in the above embodiment one to one. As shown in fig. 2, the device for allocating peak electricity in a city comprises: a determination module 21, an acquisition module 22, a prediction module 23, and a configuration module 24. The functional modules are explained in detail as follows:
the determining module 21 is used for determining a peak time period of the urban electricity utilization based on historical data of the urban electricity utilization;
the obtaining module 22 is configured to obtain, through a clustering algorithm, a first power utilization area where the user power consumption exceeds a first preset value, a second power utilization area where the user power consumption is lower than a second preset value, and a third power utilization area where the user power consumption is between the first preset value and the second preset value in the peak time period; the first preset value is greater than the second preset value;
the determining module 21 is further configured to determine power consumption attributes of power consumption users in the first power consumption area, the second power consumption area and the third power consumption area, where the power consumption attributes are domestic power consumption, production power consumption or public power consumption;
the prediction module 23 is configured to input the priority corresponding to the power consumption attribute of the power consumption user and the power consumption data N days before the current time into the self-encoder power consumption prediction model to obtain the predicted power consumption of each power consumption user;
and the configuration module 24 is configured to configure the electricity consumption amount for the corresponding electricity consumption user according to the predicted electricity consumption amount.
In an optional embodiment provided by the present invention, the determining module 21 is specifically configured to:
counting the daily electricity consumption in the historical data of the urban electricity consumption;
calculating daily average power consumption according to the daily power consumption, and calculating the product of the daily average power consumption and M to obtain an electric quantity balance value, wherein M > =1.5;
and determining the corresponding time when the daily electricity consumption exceeds the electricity balance value in the historical data of the urban electricity consumption as the peak time period.
In an optional embodiment provided by the present invention, the obtaining module 22 is specifically configured to:
performing first clustering calculation on the user power consumption in the peak time period to obtain a first power consumption area in which the user power consumption exceeds a first preset numerical value;
performing second clustering calculation on the user power consumption in the peak time period to obtain a second power consumption area in which the user power consumption is lower than a second preset value;
performing third clustering calculation on the user electricity consumption in the peak time period to obtain a third electricity utilization area with the user electricity consumption between the first preset numerical value and the second preset numerical value;
wherein the radius of the first cluster calculation is larger than the radius of the second cluster calculation and the radius of the third cluster calculation, and the radius of the second cluster calculation is larger than the radius of the third cluster calculation.
In an optional embodiment provided by the present invention, the determining module 21 is specifically configured to:
determining the electricity utilization attribute of the electricity utilization users in the first electricity utilization area as production electricity utilization;
determining the electricity utilization attribute of the electricity utilization users in the second electricity utilization area as public electricity utilization;
and determining the electricity utilization attribute of the electricity utilization users in the third electricity utilization area as living electricity utilization.
In an optional embodiment provided by the present invention, the apparatus further comprises: the training module 25:
and the training module 25 is configured to train, using the priority corresponding to the power consumption attribute of the power consumption user in each area in the peak time period and the power consumption data N days before the current time as sample data, and using the power consumption data N +1 days as a sample label, to obtain the self-encoder power consumption prediction model.
In an optional embodiment provided by the present invention, training module 25 is further configured to:
taking the electricity utilization data of the production users corresponding to the first electricity utilization area in the peak time period for N consecutive days as sample data, and taking the electricity utilization data of the (N + 1) th day as a sample label for training to obtain a self-encoder electricity quantity prediction model of the first electricity utilization area;
taking the electricity utilization data of the production users corresponding to the second electricity utilization area in the peak time period for N consecutive days as sample data, and taking the electricity utilization data of the (N + 1) th day as a sample label for training to obtain a self-encoder electricity quantity prediction model of the second electricity utilization area;
and taking the continuous N-day electricity consumption data of the production users corresponding to the third electricity utilization area in the peak time period as sample data, and taking the (N + 1) -th-day electricity consumption data as a sample label for training to obtain a self-encoder electricity quantity prediction model of the third electricity utilization area.
In an optional embodiment provided by the present invention, the prediction module 23 is specifically configured to input, according to the power utilization attribute of the power utilization user, power utilization data of the power utilization user N days before the current time into the self-encoder power prediction model of the corresponding area, to obtain the area predicted power consumption of each power utilization user; calculating the average value of the area predicted electricity consumption and the predicted electricity consumption to obtain updated predicted electricity consumption;
and the configuration module 24 is configured to configure the electricity consumption amount for the corresponding electricity consumption user according to the updated predicted electricity consumption amount.
The specific definition of the configuration device for peak electricity utilization in cities can be referred to the definition of the configuration method for peak electricity utilization in cities, and is not described herein again. The modules in the above-mentioned configuration device for peak urban electricity utilization can be implemented wholly or partially by software, hardware and their combination. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 3. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a method for peak urban electricity distribution.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
determining a peak time period of the urban electricity consumption based on historical data of the urban electricity consumption;
acquiring a first power utilization area in which the power consumption of the user exceeds a first preset numerical value, a second power utilization area in which the power consumption of the user is lower than a second preset numerical value and a third power utilization area in which the power consumption of the user is between the first preset numerical value and the second preset numerical value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value;
determining the electricity utilization attributes of electricity utilization users in the first electricity utilization area, the second electricity utilization area and the third electricity utilization area, wherein the electricity utilization attributes are life electricity utilization, production electricity utilization or public electricity utilization;
inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into an electricity quantity prediction model of a self-encoder to obtain the predicted electricity consumption of each electricity utilization user;
and configuring the electricity consumption for the corresponding electricity consumption user according to the predicted electricity consumption.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
determining a peak time period of the urban electricity consumption based on historical data of the urban electricity consumption;
acquiring a first power utilization area in which the power consumption of the user exceeds a first preset value, a second power utilization area in which the power consumption of the user is lower than a second preset value, and a third power utilization area in which the power consumption of the user is between the first preset value and the second preset value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value;
determining the electricity utilization attributes of electricity utilization users in the first electricity utilization area, the second electricity utilization area and the third electricity utilization area, wherein the electricity utilization attributes are living electricity utilization, production electricity utilization or public electricity utilization;
inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into an electricity quantity prediction model of a self-encoder to obtain the predicted electricity consumption of each electricity utilization user;
and configuring the electricity consumption for the corresponding electricity consumption user according to the predicted electricity consumption.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database or other medium used in the embodiments provided herein can include non-volatile and/or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), rambus (Rambus) direct RAM (RDRAM), direct Rambus Dynamic RAM (DRDRAM), and Rambus Dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for configuring peak electricity in a city, the method comprising:
determining a peak time period of the urban electricity utilization based on historical data of the urban electricity utilization;
acquiring a first power utilization area in which the power consumption of the user exceeds a first preset numerical value, a second power utilization area in which the power consumption of the user is lower than a second preset numerical value and a third power utilization area in which the power consumption of the user is between the first preset numerical value and the second preset numerical value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value;
determining the electricity utilization attributes of electricity utilization users in the first electricity utilization area, the second electricity utilization area and the third electricity utilization area, wherein the electricity utilization attributes are living electricity utilization, production electricity utilization or public electricity utilization;
inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into a self-encoder electricity quantity prediction model to obtain the predicted electricity utilization quantity of each electricity utilization user;
and configuring the electricity consumption for the corresponding electricity consumption user according to the predicted electricity consumption.
2. The method of claim 1, wherein determining peak time periods for the municipal electricity usage based on historical data of the municipal electricity usage comprises:
counting the daily electricity consumption in the historical data of the urban electricity consumption;
calculating daily average power consumption according to the daily power consumption, and calculating the product of the daily average power consumption and M to obtain an electric quantity balance value, wherein M > =1.5;
and determining the corresponding time when the daily electricity consumption exceeds the electricity balance value in the historical data of the urban electricity consumption as the peak time period.
3. The method of claim 2, wherein the obtaining, by a clustering algorithm, a first power usage region where the user power usage exceeds a first preset value, a second power usage region where the user power usage is lower than a second preset value, and a third power usage region where the user power usage is between the first preset value and the second preset value during the peak time period comprises:
performing first clustering calculation on the user power consumption in the peak time period to obtain a first power consumption area in which the user power consumption exceeds a first preset numerical value;
performing second clustering calculation on the user power consumption in the peak time period to obtain a second power consumption area in which the user power consumption is lower than a second preset value;
performing third clustering calculation on the user electricity consumption in the peak time period to obtain a third electricity utilization area with the user electricity consumption between the first preset numerical value and the second preset numerical value;
wherein the radius of the first cluster calculation is greater than the radius of the second and third cluster calculations, the radius of the second cluster calculation being greater than the radius of the third cluster calculation.
4. The method of claim 3, wherein the determining power usage attributes of power users within the first power usage area, the second power usage area, and the third power usage area comprises:
determining the electricity utilization attribute of the electricity utilization users in the first electricity utilization area as production electricity utilization;
determining the electricity utilization attribute of the electricity utilization users in the second electricity utilization area as public electricity utilization;
and determining the electricity utilization attribute of the electricity utilization users in the third electricity utilization area as the living electricity utilization.
5. The method of claim 1, wherein before inputting the priority corresponding to the electricity consumption attribute of the electricity consumption user and the electricity consumption data N days before the current time into the self-encoder electricity quantity prediction model to obtain the predicted electricity consumption of each electricity consumption user, the method further comprises:
and taking the priority corresponding to the power utilization attribute of the power utilization users in each area in the peak time period and the power utilization data of N days before the current time as sample data, and taking the power utilization data of the (N + 1) th day as a sample label for training to obtain the self-encoder power quantity prediction model.
6. The method of claim 5, further comprising:
taking the electricity utilization data of the production users corresponding to the first electricity utilization area in the peak time period for N consecutive days as sample data, and taking the electricity utilization data of the (N + 1) th day as a sample label for training to obtain a self-encoder electricity quantity prediction model of the first electricity utilization area;
taking the electricity utilization data of the production users corresponding to the second electricity utilization area in the peak time period for N consecutive days as sample data, and taking the electricity utilization data of the (N + 1) th day as a sample label for training to obtain a self-encoder electricity quantity prediction model of the second electricity utilization area;
and taking the continuous N-day electricity consumption data of the production users corresponding to the third electricity utilization area in the peak time period as sample data, and taking the (N + 1) -th-day electricity consumption data as a sample label for training to obtain a self-encoder electricity quantity prediction model of the third electricity utilization area.
7. The method of claim 5, wherein before configuring the power usage amount for the corresponding power usage user according to the predicted power usage amount, the method further comprises:
according to the electricity utilization attribute of the electricity utilization users, inputting the N days of electricity utilization data of the electricity utilization users before the current time into the self-encoder electricity quantity prediction model of the corresponding area to obtain the area prediction electricity consumption of each electricity utilization user;
calculating the average value of the area predicted electricity consumption and the predicted electricity consumption to obtain updated predicted electricity consumption;
the configuring the electricity consumption quantity for the corresponding electricity consumption user according to the predicted electricity consumption quantity comprises the following steps:
and configuring the electricity consumption quantity for the corresponding electricity consumption user according to the updated predicted electricity consumption quantity.
8. An arrangement for peak electricity in a city, said arrangement comprising:
the determining module is used for determining the peak time period of the urban electricity utilization based on the historical data of the urban electricity utilization;
the acquisition module is used for acquiring a first power utilization area in which the user power consumption exceeds a first preset numerical value, a second power utilization area in which the user power consumption is lower than a second preset numerical value and a third power utilization area in which the user power consumption is between the first preset numerical value and the second preset numerical value in the peak time period through a clustering algorithm; the first preset value is greater than the second preset value;
the determining module is further configured to determine power consumption attributes of power consumption users in the first power consumption area, the second power consumption area and the third power consumption area, where the power consumption attributes are domestic power consumption, production power consumption or public power consumption;
the prediction module is used for inputting the priority corresponding to the electricity utilization attribute of the electricity utilization users and the N days of electricity utilization data before the current time into the self-encoder electricity quantity prediction model to obtain the predicted electricity consumption quantity of each electricity utilization user;
and the configuration module is used for configuring the electricity consumption for the corresponding electricity consumption user according to the predicted electricity consumption.
9. Computer arrangement comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor, when executing said computer program, implements a method for peak urban power allocation according to any of claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements a method for peak urban electricity distribution according to any one of claims 1 to 7.
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