CN115759436A - User electric quantity and electricity charge prediction method and system under power grid big data - Google Patents
User electric quantity and electricity charge prediction method and system under power grid big data Download PDFInfo
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Abstract
The invention discloses a method and a system for predicting electric quantity and electric charge of a user under power grid big data, which comprises the steps of obtaining monthly average power consumption data of each user and monthly total power consumption duration of each user from the power grid big data; step 2, collecting the types and power data of household appliances of the users in the area, and calculating the service life of each type of appliance and the power consumption of each type of appliance; step 3, constructing a user electric quantity and electric charge prediction model; and 4, calculating the monthly electricity consumption and monthly electricity charge price of each household based on the model, and judging the prediction accuracy of the model. The utility model has the advantages of reasonable design, can make up the calculation to various electrical apparatus power data commonly used and the time spent data that the resident used, be favorable to reacing the prediction calculation formula that every family's power consumption corresponds, improve the accurate nature of user's electric quantity charges of electricity prediction greatly, optimize for power grid operation and provide reliable data parameter.
Description
Technical Field
The application relates to the field of power grid application, in particular to a method and a system for predicting user electric quantity and electric charge under power grid big data.
Background
At present, the monthly electricity selling amount of the Hebei company reaches 175 hundred million kilowatt hours, the electricity selling income is about 103 million yuan, huge cash flow puts higher requirements on the electricity charge and financial management of the company, and the Hebei electric power financial department of the national network has built a new generation settlement system and has the functions of cash budget and collection and payment management.
The method is influenced by factors such as user load, electricity price adjustment, new installation and modification, meter reading example days and the like, and the monthly electricity charge income estimated by using the traditional experience has larger errors. Therefore, a method for predicting the electricity quantity and the electricity charge of the user under the large data of the power grid is provided for solving the problems.
Prior art document 1 (201811367413.8) discloses a residential electricity consumption prediction method based on an electrical appliance index, which includes the following steps: 1) Counting the total preserved quantity Ni and the average power Pi of hundreds of households of the main household appliance; 2) Acquiring the service time of the household appliance, and calculating various household appliance frequency factors; 3) Calculating a correction factor lambada i of the household appliance; 4) Calculating an electrification index HEA; 5) The method comprises the steps of constructing a multiple linear regression model, taking an electrical appliance index HEA, a total number of residents Aj and a per capita dominant income Bj as input of the multiple linear regression model, training the resident power consumption Yj as an output value, and predicting the resident power consumption according to the trained multiple linear regression model.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides a method and a system for predicting the electric quantity and the electric charge of a user under the condition of large data of a power grid.
The invention adopts the following technical scheme.
A method for predicting electric quantity and electric charge of a user under big data of a power grid comprises the following steps:
step 1, acquiring monthly average power consumption data of each household and monthly average total power consumption duration of each household from power grid big data;
step 2, collecting the types and power data of household appliances of the users in the area, and calculating the service life of each type of appliance and the power consumption of each type of appliance;
step 3, constructing a user electric quantity and electric charge prediction model;
and 4, calculating the monthly electricity consumption and monthly electricity charge price of each household based on the model, and judging the prediction accuracy of the model.
And 2, taking the regional residents as prediction objects, acquiring the types and power data of household appliances purchased by the regional residents by using the large data information of the commercial logistics, and summarizing different types of the household appliances into a group of prediction calculation elements to be optimized.
In the step 2, dividing three quarters of the total monthly electricity utilization time length H of each household into three electricity utilization peak time periods, and calculating and representing the operation utilization probability P of the electrical appliance in each month; wherein, the three peak time periods of electricity consumption are respectively 05-00 and 11:00 and 18.
The service life of each electrical appliance is h I H · P; the formula for calculating the electricity consumption of each electric appliance is as follows: x I ·h I ＝Y I ，X I For appliance power, I represents the appliance type.
The step 3 comprises the following steps:
according to
And M is<And I are the number of the electric appliance types, and M different electric appliance power data are randomly selected and combined into a group to obtain N calculation combinations related to the step 3.1.
Step 3 also includes: calculating the corresponding electricity consumption of each calculation combination: x 1 h 1 +X 2 h 2 ……+X I h I ＝Y N And (2) comparing the power consumption with the monthly average power consumption Y of the user collected in the step (1), screening out the closest power consumption, and calculating and combining the power consumption to form the user power consumption prediction model.
And screening and comparing the power consumption data of each group of calculation combination with the big data of the power grid to obtain a power consumption prediction calculation formula of the corresponding user, and simultaneously performing management coding.
Step 4 comprises the following steps: based on the user electric quantity prediction model after the user management coding, calculating the monthly electric charge price predicted by the user in the month: y is M Z = Q, Z represents the unit price of the electricity fee, Q represents the monthly electricity fee amount;
and calculating a difference value between the predicted monthly electricity charge price in the month and the actual monthly charge of the user, calculating a power difference value P according to the difference value, and determining the type of the electric appliance used by the user as the type of the electric appliance in the prediction model if the power difference value P is lower than a set value.
A prediction system for electric quantity and electric charge of a user under the condition of large data of an electric network comprises:
the data acquisition module is used for acquiring monthly electricity consumption data of each user, monthly total electricity consumption time of each user, types of household appliances of the users in the area and power data;
the data processing module is used for calculating and calculating the service life of each type of electric appliance, the power consumption of each type of electric appliance, the monthly power consumption of each household and the monthly electricity fee price;
the model construction module is used for constructing a user electric quantity and electric charge prediction model;
and the logic judgment module is used for judging whether the power difference value P is lower than a set value so as to determine the type of the electric appliance.
Compared with the prior art, the method has the advantages that various common electric appliance power data and time data used by residents can be combined and calculated, a prediction calculation formula corresponding to the electricity consumption of each household can be obtained, the accuracy of the prediction of the electricity quantity and the electricity charge of the user is greatly improved, and reliable data parameters are provided for the operation optimization of the power grid.
Drawings
Fig. 1 is a flowchart of a method for predicting electric quantity and electric charge of a user under the big data of a power grid.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are only some embodiments of the invention, and not all embodiments. All other embodiments obtained by a person skilled in the art without any inventive step based on the spirit of the present invention are within the scope of the present invention.
The first embodiment is as follows:
as shown in fig. 1, the method for predicting the electricity quantity and the electricity charge of the user under the big data of the power grid includes the following steps:
step one, acquiring the monthly power consumption of residential users in the area: acquiring the monthly electricity consumption data of each user from the big data of the power grid, namely Y represents;
secondly, collecting the types and power data of household appliances of the users in the area;
taking regional residents as prediction objects, acquiring the types and power data of home appliances purchased by the regional residents by using the large data information of the commercial logistics, and summarizing different types of the home appliances into a group of prediction calculation elements to be optimized;
the method is characterized by comprising the following steps of (1) marking with electric appliance power and time:
the total duration can be obtained by dividing the total electric quantity by the total power, respectively by X 1 、X 2 、X 3 、…、X I Expressed in kilowatts per hour for various conventional household appliancesThe monthly time is respectively h 1 、h 2 、h 3 、…、h I The unit of time consumption is hour, and the calculation formula of the monthly electricity consumption of the user is as follows:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I = Y; in the formula, I represents the type of an electric appliance;
further, the total electricity consumption time of each household user in the usual month is obtained from the big data of the power grid, namely H represents, and the electricity consumption time of each electric appliance is H I The power consumption calculation formula of each electric appliance is as follows: x I ·h I ＝Y I 。
Step three,
And (3) screening out an optimal calculation formula by combined calculation: according to
And M is less than I, I is the number of the electric appliance types, M different electric appliance power data are selected and combined into a group, N different calculation combinations in the step two are obtained, meanwhile, a quantum calculation mode is adopted for high-speed operation, and one combination formula in N with the result value being closest to Y is screened out to be used as the monthly power consumption calculation normal formula of the user:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y M ；
and transmitting the electricity consumption data of each group of combined calculation positions to the big data of the power grid for screening and comparison, obtaining an electric quantity and electricity charge prediction calculation formula of the corresponding user, and simultaneously carrying out management coding.
Step four, obtaining the monthly electricity charge price of each household: y is M Z = Q, Z represents the unit price of electricity fee, Q represents the monthly electricity fee amount;
and step five, summarizing the consumption amount of the users in each area to obtain a monthly electricity consumption amount data report of the users in the whole area. And simulating the electricity utilization data of each region into a dynamic analysis chart so as to learn the peak electricity utilization time period.
Further, three quarters of the time of H are divided into three peak electricity periods, i.e., 05:00 and 18. The total peak of the household electricity is in the three stages, the most prominent household electrical appliance types in the household electrical appliances can be classified through the steps, namely the household fixed electricity utilization appliances are used, and the subsequent determination range of the electrical appliance types is narrowed.
Furthermore, the predicted price of the electric quantity and the electric charge of each user after management coding is compared with the actual charge paid by the user to obtain a difference value, the power difference value P is reversely calculated according to the difference value and a formula, and the change of the difference value every month is counted.
And further, taking the power difference value P as reference data, calculating the difference value by a difference method between the power value of each combined electrical appliance and the actually used power of the electrical appliance of the user, if the difference value and the reference data are zero, determining the type of the electrical appliance used by the user, otherwise, changing the group and continuing to calculate the difference value.
Further, after the types of the electric appliances used by the user are obtained, the electric appliances are divided into three-meal electric appliances and entertainment electric appliances according to the using duration.
The entertainment appliances comprise a television, a mobile phone, a lighting device and the like, the three-meal appliances comprise a refrigerator, an electric cooker, a microwave oven and the like, the proportion coefficient of the time spent by the entertainment appliances and the time spent by the three-meal appliances is obtained through comparison, and the daily use condition of a user is known.
Example two
The method for predicting the electric quantity and the electric charge of the user under the big data of the power grid comprises the following steps:
step one, acquiring monthly power consumption of a user: acquiring the monthly electricity consumption data of each user from the big data of the power grid, namely Y represents;
step two, marking with the power and the time of an electric appliance: respectively using X according to the power of various conventional household appliances in the national standard 1 、X 2 、X 3 、…、X I It means that the unit of power is kilowatt per hour, and the monthly time of various conventional household appliances is h 1 、h 2 、h 3 、…、h I To representThe unit of time is hour, and the calculation formula of the electricity consumption is as follows:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y；
step three, screening out an optimal calculation formula through combined calculation: according to
And M is less than I, I is the number of the electric appliance types, M different electric appliance power data are randomly selected and combined into a group, N different calculation combinations in the step two are obtained, high-speed operation is carried out in a quantum calculation mode, the result value is the same as Y, and one combination formula in N is obtained through optimization and is used as a normal formula for calculating the monthly power consumption of the user:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y M ；
step four, obtaining the monthly electricity charge price of each household: y is M Z = Q, Z represents the unit price of electricity fee, Q represents the monthly electricity fee amount;
and step five, summarizing the consumption amount of the users in each area to obtain a monthly electricity consumption amount data report of the users in the whole area.
Further, in the second step,.
And further, transmitting the electricity consumption data of each group of combined calculation positions in the third step to the power grid big data for screening and comparison, obtaining an electric quantity and electricity charge prediction calculation formula of the corresponding user, and simultaneously performing management coding.
Furthermore, the predicted price of the electric quantity and the electric charge of each user after management coding is compared with the actual charge paid by the user to obtain a difference value, the P power difference value is reversely calculated according to the difference value and a formula, and the change of the difference value of each month is counted.
And further, taking the P power difference value as reference data, calculating the power value of each combined electrical appliance and the actually used power of the electrical appliance of the user by a difference method to obtain a difference value, if the difference value and the reference data are zero, determining the type of the electrical appliance used by the user, and otherwise, changing the group and continuing to calculate the difference value.
Further, after the types of the electric appliances used by the user are obtained, the electric appliances are divided into three-meal electric appliances and entertainment electric appliances according to the using duration.
Furthermore, the entertainment appliances are televisions, mobile phones, lighting and the like, the three-meal appliances are refrigerators, electric cookers, microwave ovens and the like, the proportion coefficient of the time spent by the entertainment appliances and the time spent by the three-meal appliances is obtained through comparison, and the daily use condition of a user is known.
Further, in the fifth step, the electricity utilization data of each region is simulated into a dynamic analysis chart so as to learn the peak time period of electricity utilization.
EXAMPLE III
The method for predicting the electric quantity and the electric charge of the user under the big data of the power grid comprises the following steps:
step one, acquiring monthly power consumption of a user: acquiring the monthly electricity consumption data of each user from the big data of the power grid, namely Y represents;
step two, performing standardization by using the power and the time of the electric appliance: referring to the power of various conventional household appliances in the national standard, respectively using X 1 、X 2 、X 3 、…、X I It means that the unit of power is kilowatt per hour, and the monthly time of various conventional household appliances is h 1 、h 2 、h 3 、…、h I The unit of time is hour, and the formula of the electricity consumption is as follows:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y；
step three, screening out an optimal calculation formula through combined calculation: according to C I M (= I! M! (I-M)! And M is less than I, I is the number of the electric appliances, M different electric appliance power data are randomly selected and combined into a group, N different calculation combinations in the step two are obtained, meanwhile, a quantum calculation mode is adopted for high-speed operation, the result value is the same as Y, and one combination formula in N is obtained through optimization and is used as a normal calculation formula of the monthly power consumption of the user:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y M ；
step four, obtaining the monthly electricity charge price of each household: y is M Z = Q, Z represents the unit price of electricity fee, Q represents the monthly electricity fee amount;
and step five, summarizing the consumption amount of the users in each area to obtain a monthly electricity consumption amount data report of the users in the whole area.
Furthermore, the predicted price of the electric quantity and the electric charge of each user after management coding is compared with the actual charge paid by the user to obtain a difference value, the P power difference value is reversely calculated according to the difference value and a formula, and the change of the difference value of each month is counted.
And further, taking the P power difference value as reference data, calculating the power value of each combined electrical appliance and the actually used power of the electrical appliance of the user by a difference method to obtain a difference value, if the difference value and the reference data are zero, determining the type of the electrical appliance used by the user, and otherwise, changing the group and continuing to calculate the difference value.
Example four
The method for predicting the electric quantity and the electric charge of the user under the big data of the power grid comprises the following steps:
step one, acquiring monthly power consumption of a user: acquiring the monthly electricity consumption data of each user from the big data of the power grid, namely Y represents;
step two, performing standardization by using the power and the time of the electric appliance: respectively using X according to the power of various conventional household appliances in the national standard 1 、X 2 、X 3 、…、X I It means that the unit of power is kilowatt per hour, and the monthly time of various conventional household appliances is h 1 、h 2 、h 3 、…、h I The unit of time is hour, and the formula of the electricity consumption is as follows:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y；
step three, screening out an optimal calculation formula through combined calculation: according to
And M is less than I, I is the number of the electric appliance types, M different electric appliance power data are randomly selected and combined into a group, N different calculation combinations in the step two are obtained, high-speed operation is carried out in a quantum calculation mode, the result value is the same as Y, and one combination formula in N is obtained through optimization and is used as a normal formula for calculating the monthly power consumption of the user:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y M ；
step four, obtaining the monthly electricity charge price of each household: y is M Z = Q, Z represents the unit price of electricity fee, Q represents the monthly electricity fee amount;
and step five, summarizing the consumption amount of the users in each area to obtain a monthly electricity consumption amount data report of the users in the whole area.
Further, the power consumption of each power utilization area is obtained, and the power consumption of the next quarter or the 2 nd year is predicted according to the current statistical power utilization condition of the year.
EXAMPLE five
The method for predicting the electric quantity and the electric charge of the user under the big data of the power grid comprises the following steps:
step one, acquiring monthly power consumption of a user: acquiring the monthly electricity consumption data of each user from the big data of the power grid, namely Y represents;
step two, marking with the power and the time of an electric appliance: respectively using X according to the power of various conventional household appliances in the national standard 1 、X 2 、X 3 、…、X I It means that the unit of power is kilowatt per hour, and the monthly time of various conventional household appliances is h 1 、h 2 、h 3 、…、h I The unit of time is hour, and the formula of the electricity consumption is as follows:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y；
step three, screening out an optimal calculation formula through combined calculation: according to
And M is less than I, I is the number of the electric appliance types, M different electric appliance power data are randomly selected and combined into a group, N different calculation combinations in the step two are obtained, high-speed operation is carried out in a quantum calculation mode, the result value is the same as Y, and one combination formula in N is obtained through optimization and is used as a normal formula for calculating the monthly power consumption of the user:
X 1 ·h 1 +X 2 ·h 2 +X 3 ·h 3 +…X I ·h I ＝Y M ；
step four, obtaining the monthly electricity charge price of each household: y is M Z = Q, Z represents the unit price of the electricity fee, Q represents the monthly electricity fee amount;
and step five, summarizing the consumption amount of the users in each area to obtain a monthly electricity consumption amount data report of the users in the whole area.
Further, after the types of the electric appliances used by the user are obtained, the electric appliances are divided into three-meal electric appliances and entertainment electric appliances according to the using duration.
Furthermore, the entertainment appliances are televisions, mobile phones, lighting and the like, the three-meal appliances are refrigerators, electric rice cookers, microwave ovens and the like, the proportion coefficient of the time spent by the entertainment appliances and the time spent by the three-meal appliances is obtained through comparison, and the daily use condition of a user is known.
Further, in the fifth step, the electricity utilization data of each area is simulated into a dynamic analysis chart so as to learn the peak time period of electricity utilization.
According to the method for predicting the electric quantity and the electric charge of the user, various common electric appliance power data and time consumption data used by residents can be combined and calculated, a prediction calculation formula corresponding to the electric consumption of each household can be obtained, the accuracy of prediction of the electric quantity and the electric charge of the user is greatly improved, and reliable data parameters are provided for operation optimization of a power grid. This embodiment still provides a user's electric quantity charges of electricity prediction system under electric wire netting big data, includes:
the data acquisition module is used for acquiring monthly electricity consumption data of each user, monthly total electricity consumption duration of each user, types of household appliances of the users in the area and power data;
the data processing module is used for calculating and calculating the service life of each type of electric appliance, the power consumption of each type of electric appliance, the monthly power consumption of each household and the monthly electricity fee price;
the model construction module is used for constructing a user electric quantity and electric charge prediction model;
and the logic judgment module is used for judging whether the power difference value P is lower than a set value so as to determine the type of the electric appliance.
Compared with the prior art, the method has the advantages that various common electric appliance power data and time-consuming data used by residents can be combined and calculated, a prediction calculation formula corresponding to the electricity consumption of each household can be obtained, accuracy of prediction of the electricity quantity and the electricity charge of the user is greatly improved, and reliable data parameters are provided for operation optimization of a power grid.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (11)
1. A method for predicting electric quantity and electric charge of a user under big data of a power grid is characterized by comprising the following steps: the method comprises the following steps:
step 1, acquiring monthly average power consumption data of each household and monthly average total power consumption duration of each household from power grid big data;
step 2, collecting the types and power data of household appliances of the users in the area, and calculating the service life of each type of appliance and the power consumption of each type of appliance;
step 3, constructing a user electric quantity and electric charge prediction model;
and 4, calculating the monthly electricity consumption and monthly electricity charge price of each household based on the model, and judging the prediction accuracy of the model.
2. The method for predicting the electric quantity and the electric charge of the user under the large data of the power grid according to claim 1, wherein the method comprises the following steps:
and 2, taking the regional residents as prediction objects, acquiring the types and power data of household appliances purchased by the regional residents by using the large data information of the commercial logistics, and summarizing different types of the household appliances into a group of prediction calculation elements to be optimized.
3. The method for predicting the electric quantity and the electric charge of the user under the large data of the power grid as claimed in claim 2, wherein the method comprises the following steps:
in step 2, dividing three quarters of the total monthly electricity utilization time length H of each household into three electricity utilization peak time periods, and calculating and representing the operating utilization probability P of the electrical appliance in each month; wherein, the three power consumption peak time periods are respectively 05-00 and 11-00: 00 and 18.
4. The method for predicting the electric quantity and the electric charge of the user under the large data of the power grid according to claim 3, wherein the method comprises the following steps:
the service life of each electrical appliance is h I H · P; the formula for calculating the electricity consumption of each electric appliance is as follows: x I ·h I ＝Y I ，X I For appliance power, I represents the appliance type.
5. The method for predicting the electric quantity and the electric charge of the user under the large data of the power grid according to claim 1, wherein the method comprises the following steps:
the step 3 comprises the following steps:
according to
And M<And I are the number of the types of the electric appliances, and M different electric appliance power data are randomly selected and combined into a group to obtain N calculation combinations related to the step 3.1.
6. The method for predicting the electric quantity and the electric charge of the user under the large data of the power grid according to claim 5, wherein the method comprises the following steps:
step 3 further comprises: calculating the corresponding electricity consumption of each calculation combination: x 1 h 1 +X 2 h 2 ……+X I h I ＝Y N Comparing with the monthly average power consumption Y of the user collected in the step 1, screening out the nearest power consumption, and calculating and combining the corresponding power consumption to the useAnd (4) a household electricity quantity prediction model.
7. The method for predicting the electric quantity and the electric charge of the user under the large data of the power grid according to claim 6, wherein the method comprises the following steps:
and screening and comparing the power consumption data of each group of calculation combination with the big data of the power grid to obtain a power consumption prediction calculation formula of the corresponding user, and simultaneously performing management coding.
8. The method for predicting the electric quantity and the electric charge of the user under the large data of the power grid according to claim 7, characterized by comprising the following steps of:
step 4 comprises the following steps: based on the user electric quantity prediction model after the user management coding, calculating the monthly electric charge price predicted by the user in the month: y is M Z = Q, Z represents the unit price of electricity fee, Q represents the monthly electricity fee amount;
and calculating a difference value between the predicted monthly electricity charge price in the month and the actual monthly charge of the user, calculating a power difference value P according to the difference value, and if the power difference value P is lower than a set value, determining the type of the electric appliance used by the user as the type of the electric appliance in the prediction model.
9. A prediction system of electricity quantity and electricity charge of a user under the condition of large data of a power grid is based on the method of any one of claims 1 to 8, and is characterized in that:
the data acquisition module is used for acquiring monthly electricity consumption data of each user, monthly total electricity consumption time of each user, types of household appliances of the users in the area and power data;
the data processing module is used for calculating and calculating the service life of each type of electric appliance, the power consumption of each type of electric appliance, the monthly power consumption of each household and the monthly electricity fee price;
the model construction module is used for constructing a user electric quantity and electric charge prediction model;
and the logic judgment module is used for judging whether the power difference value P is lower than a set value so as to determine the type of the electric appliance.
10. A terminal comprising a processor and a storage medium; the method is characterized in that:
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 8.
11. Computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 8.
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