CN114519614A - Intelligent selection method and system for electric energy pricing mode - Google Patents
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Abstract
An intelligent selection method for electric energy pricing modes is characterized by comprising the following steps: step 1, collecting customer files of low-voltage residential electricity users, and identifying electricity users which are in accordance with intelligent selection of an electric energy pricing mode; step 2, historical electricity utilization behavior data acquisition is carried out on electricity utilization users which are intelligently selected according with an electric energy pricing mode, and electricity utilization prediction of the year is generated on the basis of the historical electricity utilization behavior data; and 3, generating a power utilization model of the power utilization user according to the annual power utilization prediction, identifying a power charge inflection point in the power utilization model, and intelligently selecting an electric energy pricing mode of the power utilization user based on the power charge inflection point. The method is simple and accurate in result, can provide targeted and accurate-reference-result intelligent selection of the electric energy pricing mode for users, greatly reduces the working pressure of customer service personnel of the electric power company, and improves the service quality.
Description
Technical Field
The invention relates to the field of intelligent power utilization, in particular to an intelligent selection method and system for an electric energy pricing mode.
Background
At present, with the gradual release of birth policy in the country, families with more than five people and even seven people as old people are in one house become more and more. According to the related requirements of 'notice on improving the relevant problems of the residential stepped electricity price' (2021 No. 106) by the ministry of improvement of the Jiangsu province, the 'one-family-multi-person-mouth' stepped electricity price execution requirement is adjusted from 3 months and 1 days in 2021 year, and for urban and rural residential electricity customers who execute 'one-family-one-meter' and execute the stepped electricity price of residents in Jiangsu province, the residential family population reaches 7 or more families, the residential meter-closing electricity price can be selected to be executed without considering the steps and peak-valley time; meanwhile, the original step electricity prices of 5 people and more residents can be continuously executed, the step electricity price base number of 100 degrees is increased every month in the first stage, and whether the time is opened or not is freely selected.
However, in a household of 7 or more people with one household and more population, the actual electricity charge expense is greatly influenced by selecting to execute the step electricity price (time sharing/non-time sharing) or the meter closing electricity price of the residents according to different electricity consumption and living habits. However, since this policy is just being implemented, the electricity consumers do not have a good idea of which electric energy pricing method should be selected. In addition, the charging modes are influenced by the electricity utilization habits of users, and different electricity utilization users and different electricity utilization habits also have influence on the selection of the electric energy charging modes. For example, in the process of power utilization, the influence of time-of-use power rates is fully considered by some users, the valley power proportion in the power utilization data is high, and the users adopt a step time-of-use pricing mode to be more beneficial. And the other part of users have lower valley electricity proportion in the electricity consumption data, but have higher electricity consumption, and the users are more suitable for the pricing mode of closing the electricity price.
However, in real life, not only the user himself does not know his/her electricity usage completely, but also the user himself/herself does not know a certain degree of the plurality of pricing methods. Because various electric energy pricing modes relate to different electricity utilization habits of users, the electric power company is difficult to introduce the electric power consumption habits to the electricity utilization users in a more convenient mode. This results in a lack of guidance for the electricity user in selecting the pricing means of the electrical energy, which is quite confusing.
In the prior art, on the other hand, the electricity consumption data of residents can be effectively obtained by users based on Apsara Uni-manager in the Aliskiu data. The data information can be queried in real time through a web interface, and basic business personnel of the power company can carry out efficient work through the power utilization data of the user, so that the service satisfaction of the user is improved. However, in the prior art, the data still needs to be analyzed by a professional to be provided to the user in a targeted manner, so that the user needs to access the service of the power company through a telephone, a network and the like. This step greatly increases the utility company staff traffic and the manual service process is prone to various unexpected errors.
In view of the above problems, there is a need for an intelligent selection method and system for electric energy pricing manner that can be automatically queried by a user throughout the electricity consumption process.
Disclosure of Invention
In order to solve the defects in the prior art, the invention aims to provide an intelligent selection method and system for an electric energy pricing mode.
The invention adopts the following technical scheme.
The invention relates to an intelligent selection method of an electric energy pricing mode, wherein the method comprises the following steps: step 1, collecting customer files of low-voltage residential electricity users, and identifying electricity users which are in accordance with intelligent selection of an electric energy pricing mode; step 2, collecting historical electricity utilization behavior data of electricity utilization users intelligently selected according with the electric energy pricing mode, and generating the electricity utilization forecast of the year based on the historical electricity utilization behavior data; and 3, generating a power utilization model of the power utilization user according to the annual power utilization prediction, identifying a power charge inflection point in the power utilization model, and intelligently selecting an electric energy pricing mode of the power utilization user based on the power charge inflection point.
Preferably, in step 1, the customer profile of the low-voltage residential electricity user includes user category, current time-sharing condition, certificate number, number of electricity users, and special condition.
Preferably, the user category comprises town resident lighting, rural resident lighting, town resident lighting and form-fitting users, rural lighting and form-fitting users and other users; the current time sharing situation comprises time sharing and non-time sharing; the certificate number comprises an identity card number, a household account number and a residence certificate number; counting the number of the users based on the result of certificate number duplication elimination; the special conditions comprise user name changing, user passing, user logo changing, user capacity increasing, low-security user and preferential user.
Preferably, the electricity users who accord with the intelligent selection of the electric energy pricing mode include: the user category is normal electricity consumption users of low-voltage residents who use electricity for more than or equal to 7 of urban resident illumination, rural resident illumination, urban resident illumination and rural illumination combined meter users; the selectable electric energy pricing modes comprise: 5-person step time-of-use electricity price, 5-person step non-time-of-use electricity price and 7-person meter-closing electricity price.
Preferably, the historical electricity consumption behavior data comprises the annual total electricity quantity, the annual electricity quantity same-ratio acceleration rate, the annual peak electricity quantity, the annual valley electricity proportion and the annual valley electricity proportion change trend of the electricity consumption user in three years according with the intelligent selection of the electric energy pricing mode, and the annual time, the annual peak electricity quantity and the annual valley electricity quantity.
Preferably, the annual power consumption prediction comprises the annual prediction of peak power consumption to be used, the annual prediction of valley power consumption to be used, the annual prediction of total peak power consumption and the annual prediction of total valley power consumption.
Preferably, three electric energy pricing modes of 5-person step time-sharing electricity price, 5-person step non-time-sharing electricity price and 7-person meter closing electricity price are respectively utilized, and the electricity utilization model of the current electricity utilization user is generated based on the used peak electricity quantity in the current year, the used valley electricity quantity in the current year, the predicted to-be-used peak electricity quantity in the current year and the predicted to-be-used valley electricity quantity in the current year.
Preferably, in the power consumption model, the ratio of the predicted peak power amount to the predicted valley power amount is the same as the predicted valley power ratio.
Preferably, the electric charge inflection point is an electric charge value corresponding to a predicted peak electric quantity and a predicted valley electric quantity when electric charges of any two of the three electric energy pricing methods are equal; and the value range of the sum of the predicted peak electric quantity and the predicted valley electric quantity is 0 to an electric quantity threshold value, and the electric quantity threshold value is larger than the sum of the predicted total peak electric quantity and the predicted total valley electric quantity in the same year.
Preferably, the system comprises a transmission device, a processor and a display; the transmission device is used for acquiring customer files of low-voltage residential electricity users and historical electricity consumption behavior data; the processor is used for obtaining the electricity utilization users which accord with the intelligent selection of the electric energy pricing mode, obtaining the electricity utilization forecast of the year and generating an electricity utilization model of the electricity utilization users; and the display is used for outputting and displaying the power utilization model and providing an intelligent selection result.
Compared with the prior art, the method and the system for intelligently selecting the electric energy pricing mode have the advantages that the electricity utilization user can automatically obtain the customer file, the electricity utilization prediction and the intelligent selection result of the electric energy pricing mode, which are intelligently generated by the system and are related to the current user, in a self-query mode. The method is simple and accurate in result, can provide targeted and accurate-reference-result intelligent selection of the electric energy pricing mode for users, greatly reduces the working pressure of customer service personnel of the electric power company, and improves the service quality.
Drawings
FIG. 1 is a schematic flow chart illustrating steps of an intelligent selection method for electric energy pricing schemes according to the present invention;
fig. 2 is a schematic diagram of a power consumption model of a power consumption user generated by an intelligent selection method for an electric energy pricing method according to the present invention.
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
Fig. 1 is a schematic flow chart illustrating steps of an intelligent selection method for an electric energy pricing method according to the present invention. As shown in fig. 1, an intelligent selection method for electric energy pricing modes includes steps 1 to 3.
In step 1 of the invention, the system can firstly collect all the client files of the resident users meeting the requirements in the area range where various pricing modes are expected to be carried out.
Preferably, in step 1, the customer profile of the low-voltage residential electricity user includes a user category, a current time sharing condition, a certificate number, a number of electricity users, and a special condition.
In the invention, in order to ensure the accuracy of subsequent data acquisition, the acquired customer file can be screened, analyzed and verified firstly. In particular, the relevant profiles of all electricity users are not collected in the present invention. In the invention, only the files of all low-voltage resident users in the area range are collected, and the files of high-voltage users and the files of low-voltage non-resident users are screened out.
In addition, the present invention also eliminates the customer profiles in the abnormal power consumption state, for example, those customer profiles that have been sold are not included in the scope of the present invention. In addition, users who have a resident user who includes a plurality of metering points, such as electric energy meters, are rejected, and only the customer file of the meter is reserved.
After the customer files which are not in the scope of the invention are screened out, the storage condition analysis and key data verification are carried out on the rest customer files. For example, in the present invention, it is checked whether the client profile meeting the requirement has an incomplete information record or an incorrect information record. Only the customer profiles that have been screened and meet the conditions are retained.
Preferably, the user category comprises town resident lighting, rural resident lighting, town resident lighting and meter-making users, rural lighting and meter-making users and other users; the current time sharing condition comprises time sharing and non-time sharing; the certificate number comprises an identity card number, a family number and a residence card number; counting the number of the users based on the result of certificate number duplication elimination; the special conditions comprise user name changing, user passing, user label changing, user capacity increasing, low-security user and preferential user.
As described above, the specific information of the archive in the present invention includes information such as user category, current time sharing condition, certificate number, number of users, and special condition. The user category refers to whether the users are town users, rural users, residential lighting users or residential lighting combination users. For the users which do not belong to the four types of users, all the other users are classified into other users without special treatment.
In addition, the client profile also records whether the pricing method currently selected by the user is a time-of-use pricing method or a non-time-of-use pricing method. Although the user selects one of the time-sharing pricing mode and the non-time-sharing pricing mode, the method of the invention can provide the electric charge results of a plurality of different pricing modes to the user when the power utilization model is established, so that the user can more clearly determine the electric charge result caused by selecting the pricing mode.
It should be noted that, in the present invention, the number of people of one user of the current user corresponding to each electric energy meter is obtained by collecting the relevant certificate information of the client. Therefore, when obtaining the certificate number information, it is necessary to delete the certificate information irrelevant to the number of people.
Specifically, in the present application, the certificate type, the certificate number, and the certificate status are collected first. Invalid certificate numbers, such as those that have expired, are then removed therefrom while valid certificate numbers are retained. The types of certificates that are not related to the population, such as real estate titles, low assurance, five assurance, or other certificate numbers, are also deleted, typically leaving only the identity card, the house account, or the resident certificate associated number.
Because a plurality of different certificate numbers corresponding to the same person exist under one electricity consumption meter, duplicate removal operation can be carried out, and the identity information of only one electricity consumption person can be marked on the last remaining number.
After the number of the electricity utilization numbers is counted, the number of the electricity utilization people corresponding to the current ammeter can be obtained.
It should be noted that there may be various situations for the electricity consumers, for example, the actual amount of electricity may be partially erased or favored due to various preferential policies, or the past prediction result may cause the actual electricity consumers to shift due to the situations of buying and selling the houses, etc. Therefore, in the present application, various situations such as user name change, user passing, user swapping, user expansion, low-security user, and premium user are also considered in practice.
Preferably, the electricity users who accord with the intelligent selection of the electric energy pricing mode include: the user category is normal electricity consumption users of low-voltage residents who use electricity for more than or equal to 7 of urban resident illumination, rural resident illumination, urban resident illumination and rural illumination combined meter users; the selectable electric energy pricing modes comprise: 5-person step time-of-use electricity price, 5-person step non-time-of-use electricity price and 7-person meter-closing electricity price.
It should be noted that, in the present invention, some users, for example, users with lighting of urban residents/lighting of rural residents, and the number of people using electricity less than 5, can choose to use ordinary step time-sharing electricity price or ordinary step non-time-sharing electricity price to realize electric energy pricing. And the other part of users, such as urban/rural illumination and users with the number of the used electricity greater than or equal to 5 can choose to realize the electric energy pricing in a way of 5-person step time-sharing electricity price or 5-person step non-time-sharing electricity price.
Furthermore, for the users who have lighting of urban residents/rural residents or lighting of the urban residents/lighting of the rural residents and have the number of the users with electricity more than or equal to 7, the method can not only realize the electric energy pricing in a mode of 5-person step time-sharing electricity price or 5-person step non-time-sharing electricity price, but also realize the pricing in a mode of 7-person meter-sharing electricity price. Therefore, the invention can provide simulation models of three different electric energy pricing modes for the users.
And 2, collecting historical electricity utilization behavior data of electricity utilization users intelligently selected according with the electric energy pricing mode, and generating the electricity utilization forecast of the year based on the historical electricity utilization behavior data.
According to the invention, after the customer file is obtained, the historical electricity consumption behavior data of the corresponding user can be extracted based on the relevant information in the customer file, and after the data are analyzed, the prediction of the electricity consumption data of the year can be accurately obtained.
Preferably, the historical electricity consumption behavior data includes annual total electricity quantity, annual electricity quantity comparison acceleration, annual peak electricity quantity, annual valley electricity proportion change trend, annual time, annual peak electricity quantity and annual valley electricity quantity within three years of the electricity consumption user history according with the intelligent selection of the electricity pricing mode.
Taking the predicted user electricity consumption in 2022 as an example, the method of the present invention continuously records the annual total electricity consumption in three years 2021, 2020, and 2019. Meanwhile, according to the total annual power consumption, the annual power consumption in 2020 and 2021 years is proportionally increased by calculation.
For users with time-sharing data, the method can record the conditions of annual peak electric quantity and annual valley electric quantity in three years. That is, for a certain period of 24 hours, at the peak of electricity usage, the current user's electricity usage during this period will be recorded as the peak electricity amount, and similarly, if the user's electricity usage period is the valley period, the user's electricity usage will be recorded as the valley electricity amount. The total of the valley electric quantity of the user in one year is divided by the total electric consumption of the user in one year, and the annual valley electric proportion of the user can be obtained.
According to the invention, the change trend of the valley electricity proportion can be obtained according to the valley electricity proportion of a plurality of years. The change trend of a certain year is the comparison of the valley electricity proportion of the current year with the valley electricity proportion of the previous year.
In addition, in the present invention, the peak electric quantity, and the total electric quantity that have been used in the current year may also be acquired according to the current year electricity usage situation.
The data related to the electric quantity mentioned in the above section are all historical data obtained according to actual electricity utilization conditions. According to the historical data, the electricity utilization condition of the year can be predicted.
Preferably, the annual power consumption prediction includes annual peak power consumption prediction, annual valley power consumption prediction, annual total peak power consumption prediction and annual total valley power consumption prediction.
According to the total annual power consumption of three historical years, the total annual power consumption is predicted, and then the power consumption used in the year is subtracted, so that the total annual power consumption is predicted to be used.
In the present invention, the annual valley power ratio of the current year can be obtained by solving the trend of the annual valley power ratio change of the plurality of years. The one valley power ratio may be obtained from an average of valley power ratios of a plurality of previous years, or may be similarly predicted by other methods known in the art.
After the predicted valley power proportion of the current year is obtained, the valley power proportion of the current year can be multiplied by the total electric quantity of the current year and the total electric quantity to be used predicted in the current year, so that the peak electric quantity to be used predicted in the current year, the valley electric quantity to be used predicted in the current year, the total peak electric quantity predicted in the current year and the total valley electric quantity predicted in the current year are obtained.
And 3, generating a power utilization model of the power utilization user according to the annual power utilization prediction, identifying a power charge inflection point in the power utilization model, and intelligently selecting an electric energy pricing mode of the power utilization user based on the power charge inflection point.
Preferably, three electric energy pricing modes, namely 5-person step time-sharing electricity price, 5-person step non-time-sharing electricity price and 7-person meter-closing electricity price, are respectively utilized, and the electricity utilization model of the current electricity utilization user is generated based on the used peak electricity quantity, the used valley electricity quantity, the predicted peak electricity quantity to be used and the predicted valley electricity quantity to be used in the current year.
In the invention, electric quantity models obtained by a plurality of different electric energy pricing methods can be obtained based on the pre-measurement. Specifically, in the present invention, the 5-person stepped electricity-time rate and the 5-person stepped electricity-time-free rate can be charged in a stepped manner. For 5 people stairs, when the total electric quantity is less than 3960 kilowatt hours, greater than or equal to 3960 kilowatt hours, less than 6000 kilowatt hours and greater than or equal to 6000 kilowatt hours, different stair electricity prices can be respectively adopted for charging. In addition, for the time-of-use electricity price, different electricity prices can be adopted for calculating the cost in different time periods within 24 hours. And 7 people close the electricity price, the electricity is charged according to the uniform electricity price no matter what the electricity consumption is.
Thus, three different broken lines can be obtained when the predicted valley power ratio is unique and the power consumption amount gradually increases with the age. Each broken line can represent a time-sharing electricity charge condition.
Fig. 2 is a schematic diagram of an electricity consumption model of an electricity consumption user generated by an intelligent selection method for an electric energy pricing method according to the present invention. As shown in fig. 2, since the intervals of the electric quantities on the horizontal axis are different, the slope of the broken line of the closed-up electricity rate is changed, and it can be found from the contents of fig. 2 that the closed-up electricity rate of 7 persons is higher than or equal to about 5 persons for the stepped non-time electricity rate when the annual used amount of electricity is within 6000 kw, and that the closed-up electricity rate of 7 persons is more favorable when the electric quantity exceeds 6000 kw.
In the invention, in order to more clearly show the condition that the time-sharing electricity charges are when the electricity utilization habits of users are different, 5-person step time sharing 1 is realized that all electricity consumption is peak electricity, and 5-person step time sharing 2 is realized that all electricity consumption is valley electricity. Therefore, between the 5-person step time division 1 and the 5-person step time division 2, the electricity consumption rate of the user who uses the peak-to-valley electricity at a certain ratio can be determined for all the normal electricity consumption cases.
It can be found that under the condition that the electricity consumption habit of the user is better and the valley electricity utilization rate is higher, when the annual electricity consumption is between 3000 and 9000 kilowatts hours, the step time-sharing electricity price is more favorable, and when the annual electricity consumption of the user exceeds about 1 kilowatt hour, the 7-person closing electricity price is relatively more favorable.
The method can compare the optimal electricity price with the current electricity price calculating mode adopted by the user to obtain the annual electricity fee difference and provide the annual electricity fee difference to the electricity user, so that the electricity user can more conveniently and visually obtain the optimal price calculating mode.
The method of the invention is based on the national new policy of benefiting the people and using electricity, and helps 7 families to select the optimal electricity price, thereby saving the expenditure of electricity fee, improving the satisfaction degree of high-quality service of customers, and the customers can select different modes according to the actual electricity utilization condition of the customers as appropriate, so that the charging mode is more reasonable.
The intelligent power consumption management system is high in intelligent degree, information is safe, real and accurate based on enterprise data middlings and mass historical power consumption data, power consumption characteristics of each household are intelligently analyzed by means of a 7-port human power price data model, an optimal power price scheme is dynamically output, and the scheme accuracy rate is 99.6% and above.
The invention can compare the expenses under different pricing modes, and form a webpage interface to support real-time query, thereby supporting basic business personnel to efficiently carry out work and improving the satisfaction degree of high-quality service of customers. The analysis function can also be loaded into online application software of the network, thereby further improving the quality service level.
Compared with the prior art, the method and the system for intelligently selecting the electric energy pricing mode have the advantages that the electricity utilization user can automatically obtain the customer file, the electricity utilization prediction and the intelligent selection result of the electric energy pricing mode, which are intelligently generated by the system and are related to the current user, in a self-query mode. The method is simple and accurate in result, can provide targeted and accurate-reference-result intelligent selection of the electric energy pricing mode for users, greatly reduces the working pressure of customer service personnel of the electric power company, and improves the service quality.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are only preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for the purpose of limiting the scope of the present invention, and on the contrary, any modifications or modifications based on the spirit of the present invention should fall within the scope of the present invention.
Claims (10)
1. An intelligent selection method for electric energy pricing modes is characterized by comprising the following steps:
Step 1, collecting customer files of low-voltage residential electricity users, and identifying electricity users which are in accordance with intelligent selection of an electric energy pricing mode;
step 2, collecting historical electricity utilization behavior data of electricity utilization users intelligently selected according with an electric energy pricing mode, and generating electricity utilization prediction in the year based on the historical electricity utilization behavior data;
and 3, generating an electricity utilization model of the electricity utilization user according to the annual electricity utilization prediction, identifying an electricity charge inflection point in the electricity utilization model, and intelligently selecting an electric energy pricing mode of the electricity utilization user based on the electricity charge inflection point.
2. The method of claim 1 for intelligent selection of pricing means for electric energy, wherein:
in the step 1, the customer file of the low-voltage residential electricity user comprises user types, current time-sharing conditions, certificate numbers, electricity consumption number and special conditions.
3. The method of claim 2 for intelligent selection of pricing means for electric energy, wherein:
the user categories comprise town resident lighting, rural resident lighting, town resident lighting and meter-integrating users, rural resident lighting and meter-integrating users and other users;
the current time sharing situation comprises time sharing and non-time sharing;
The certificate number comprises an identity card number, a household account number and a residence certificate number;
the number of the users is counted based on the result of the certificate number after the duplication is removed;
the special conditions comprise user name changing, user passing, user label changing, user capacity increasing, low-security user and preferential user.
4. The intelligent selection method of electric energy pricing method according to claim 3, characterized in that:
the electricity consumption user who accords with electric energy pricing mode intelligent selection includes: the user category is normal electricity consumption users of low-voltage residents who use electricity for more than or equal to 7 of urban resident illumination, rural resident illumination, urban resident illumination and rural illumination combined meter users;
the optional electric energy pricing method comprises the following steps: 5-person step time-of-use electricity price, 5-person step non-time-of-use electricity price and 7-person meter-closing electricity price.
5. The intelligent selection method of electric energy pricing method according to claim 4, characterized in that:
the historical electricity consumption behavior data comprises annual total electricity quantity, annual electricity quantity comparison acceleration, annual peak electricity quantity, annual valley electricity proportion and annual valley electricity proportion change trend, annual time, annual peak electricity quantity and annual valley electricity quantity in three years of electricity consumption user history according with the intelligent selection of the electric energy pricing mode.
6. The method of claim 5 for intelligent selection of pricing means for electric energy, wherein:
the annual power utilization prediction comprises the annual prediction of peak power consumption to be used, the annual prediction of valley power consumption to be used, the annual prediction of total peak power consumption and the annual prediction of total valley power consumption.
7. The intelligent selection method of electric energy pricing method according to claim 6, characterized in that:
and respectively utilizing three electric energy pricing modes of 5-person step time-sharing electricity price, 5-person step non-time-sharing electricity price and 7-person meter-closing electricity price, and generating the electricity utilization model of the current electricity utilization user based on the peak electricity consumption, the valley electricity consumption, the peak electricity consumption to be used and the valley electricity consumption to be used which are predicted in the current year.
8. The intelligent selection method of electric energy pricing method according to claim 7, characterized in that:
in the power utilization model, the ratio of the predicted peak power quantity to the predicted valley power quantity is the same as the predicted valley power ratio.
9. The intelligent selection method of electric energy pricing method according to claim 8, characterized in that:
the electric charge inflection point is an electric charge value corresponding to predicted peak electric quantity and predicted valley electric quantity when electric charges of any two electric energy pricing modes are equal;
The sum of the predicted peak electric quantity and the predicted valley electric quantity ranges from 0 to an electric quantity threshold value, and the electric quantity threshold value is larger than the sum of the predicted total peak electric quantity and the predicted total valley electric quantity in the current year.
10. An electric energy pricing means intelligent selection system according to any one of claims 1-9, characterized by:
the system comprises a transmission device, a processor and a display; wherein, the first and the second end of the pipe are connected with each other,
the transmission device is used for acquiring the customer file of the low-voltage residential electricity user and the historical electricity consumption behavior data;
the processor is used for obtaining the electricity utilization users meeting the intelligent selection of the electric energy pricing mode, obtaining the electricity utilization forecast of the year and generating the electricity utilization model of the electricity utilization users;
and the display is used for outputting and displaying the power utilization model and providing an intelligent selection result.
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