CN116523496A - Method and device for replacing intelligent ammeter based on load prediction and data double confirmation - Google Patents
Method and device for replacing intelligent ammeter based on load prediction and data double confirmation Download PDFInfo
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a method and a device for replacing an intelligent ammeter based on load prediction and data double confirmation, wherein the method combines the following two methods: a load prediction method for replacing a smart meter comprises the following steps: acquiring field data and historical data of the intelligent ammeter, and constructing a short-day load prediction data set by adopting the historical data; establishing a PLS regression equation by using the data set; and carrying out load data prediction by using a PLS regression equation. A method for replacing a smart meter based on double confirmation of field and export data comprises the following steps: obtaining prediction data of the replacement date and intelligent ammeter stop code data of field replacement by using short-day load prediction, and carrying out primary confirmation of ammeter replacement comparison; and (5) performing reconfirmation of ammeter replacement comparison. The invention realizes the flow of the replacement work, automatically collects and identifies the field data, remarkably improves the automation degree, saves manpower and material resources and improves the replacement reliability of the field meter.
Description
Technical Field
The invention relates to a method and a device for replacing an intelligent ammeter based on load prediction and data double confirmation, and belongs to the technical field of replacement of an electric energy metering device.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
In recent years, the technology for collecting the electric energy information of users is deepened and developed continuously, the requirements on the efficiency and the data processing capacity of the electric energy meter replacement system are higher and higher, and the field and background work burden is increased continuously due to batch replacement of the electric energy meters. Various power supply companies in China all keep a large number of electric energy meters, and are limited by service life and technology update of the intelligent electric energy meters, and a plurality of electric energy meters enter a replacement period at the same time, so that the electric energy meter replacement work becomes a daily work with long-term existence and huge workload on site.
The work content related to the replacement work of the electric energy metering device is relatively complex. Besides the work of correspondence between new and old table numbers, transcription of old representation numbers, collection of new and old table photos and start and stop confirming single photos and the like, a background system also needs to complete a series of confirming work and reloading processes, and the problems of low accuracy of the work process, poor data processing capability and the like exist in the work of the background system at present: 1) After receiving the ammeter stop code returned by field photographing, the background system only adopts a simple average method to compare with frozen data, and confirms that the ammeter has no error and larger error; 2) After the replacement of the electric energy meter is completed, new meter data is called the next day, but the method is only used for detecting whether data acquisition is successful or not and is not used for expansion application; 3) The simple average processing method refers to a method of multiplying daily average electric quantity by the number of days of interval, and is low in accuracy because environmental factors such as illumination, air temperature and the like are not considered.
Because the on-site electric energy metering device is large in batch replacement quantity and heavy in work task, the background replacement system is used as the last procedure for comparing and confirming the new meter and the old meter, and the work of the on-site electric energy metering device faces a great challenge. Any meter comparison error can cause the electricity charge calculation error of the user, bring bad service perception to the user, influence the reliability of power supply and cause unnecessary economic loss and image damage. Therefore, there is a need for a smart meter replacement measure that ensures data validation accuracy and replacement reliability.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a device for replacing the intelligent ammeter based on load prediction and data double confirmation, which can realize the process of replacing the ammeter, automatically collect and identify field data, remarkably improve the degree of automation, save manpower and material resources and improve the reliability of replacing the ammeter on the site.
The technical scheme adopted for solving the technical problems is as follows:
in a first aspect, a load prediction method for a smart meter replacement provided by an embodiment of the present invention includes the following steps:
acquiring field data and historical data of the intelligent ammeter, and constructing a short-day load prediction data set by adopting the historical data;
establishing a PLS regression equation by using the data set;
and carrying out load data prediction by using a PLS regression equation.
As a possible implementation manner of this embodiment, the collecting field data and historical data of the smart meter, and constructing a short-day load prediction data set by using the historical data includes:
collecting the stop code data of the intelligent ammeter which is replaced on site;
the method comprises the steps of exporting historical data of the intelligent ammeter through a marketing technical support system;
constructing a data set by adopting 17 data, and constructing 10 groups of data sets by taking load samples of 24 days before the expiration date of historical data, wherein y is an output load and x is 1 -x 16 To input data, x 1 -x 14 For the power load of the first 14 days, x 15 、x 16 The cloud cover and the air temperature of the current day are respectively predicted.
As a possible implementation manner of this embodiment, the establishing a PLS regression equation using the data set includes:
carrying out standardization processing on the data set, and extracting a residual equation for constructing a regression equation through a main component;
obtaining regression coefficients of the output matrix with respect to the main components through calculation after checking and crossing;
and obtaining a regression equation of the output matrix and the input matrix through a standardized inverse process.
As a possible implementation manner of this embodiment, the load data prediction using PLS regression equation includes:
carrying out load data prediction by using a PLS regression equation to obtain a first predicted value;
the obtained predicted value is used as input to construct new input set to predict the load data of the next day, so that the prediction is repeated, and finally, the load predicted value of the current day of field replacement is obtained;
comparing the load predicted value of the current day of field replacement with the code data of the intelligent ammeter of the field replacement, if the error requirement is met, considering that the prediction is accurate, otherwise, returning to the main component extraction of the regression coefficient calculation process until the error requirement is met.
In a second aspect, the method for replacing the smart meter based on double confirmation of the on-site and derived data provided by the embodiment of the invention comprises the following steps:
obtaining prediction data of the replacement date and intelligent ammeter stop code data of field replacement by using short-day load prediction, and carrying out primary confirmation of ammeter replacement comparison;
and (5) performing reconfirmation of ammeter replacement comparison.
As a possible implementation manner of this embodiment, the obtaining, by using the short-day load prediction, the prediction data of the current day of replacement and the smart meter stop code data of the on-site replacement, and performing one-time confirmation of the meter replacement comparison includes:
obtaining predicted data of the on-site replacement date by using the load prediction method of the intelligent ammeter replacement according to any of the above;
comparing the predicted data of the current day of field replacement with the stop code data collected on the field, so that the error of the predicted data meets the error requirement;
or comparing the predicted data of the current day of field replacement with the predicted data of the traditional method of multiplying the average daily electric quantity by the number of days of interval, so that the error of the predicted data meets the error requirement.
As a possible implementation manner of this embodiment, the reconfirming of the ammeter replacement comparison includes:
initiating a list changing process in a marketing system, initiating an acquisition scheme process after the process is filed, issuing an acquisition task, and remotely acquiring list codes to obtain data of the next day list codes;
continuously predicting by using the load prediction method for replacing the intelligent ammeter according to any of the above, so as to obtain prediction data of the next day of replacing the ammeter on site;
and comparing the predicted data of the next day of field replacement with the code data of the next day table, and enabling the error of the predicted data to meet the error requirement.
As one possible implementation manner of this embodiment, the method for replacing a smart meter based on double confirmation of on-site and export data further includes the following steps:
and carrying out batch replacement of the intelligent electric meters.
As a possible implementation manner of this embodiment, the performing batch reloading of the smart meter includes:
acquiring basic information of operators of the electric energy metering device and information of on-site replacement equipment;
acquiring the task of the on-site replacement metering device and the latest meter reading information of the metering device;
the data acquisition and recognition are carried out on the replacement site, the picture and digital information of the site metering device are obtained, and the information quality verification and normalized storage are carried out on the related information;
based on the process information, the metering data and the information quality basic information of the field metering device replacement operation, the final overall quality information of the replacement operation is formed.
In a third aspect, an embodiment of the present invention provides a load prediction device for a replacement of an intelligent electric meter, including:
the data acquisition module is used for acquiring field data and historical data of the intelligent ammeter and constructing a short-day load prediction data set by adopting the historical data;
the regression equation building module is used for building a PLS regression equation by utilizing the data set;
and the load data prediction module is used for predicting the load data by using the PLS regression equation.
In a fourth aspect, an embodiment of the present invention provides a smart meter reloading device based on double confirmation of on-site and export data, including:
the primary confirmation module is used for carrying out primary confirmation of ammeter replacement comparison by utilizing the prediction data of the short-day load to obtain the replacement date and the intelligent ammeter stop code data of the field replacement;
and the reconfirming module is used for reconfirming the replacement comparison of the ammeter.
As one possible implementation manner of this embodiment, the smart meter reloading device based on double confirmation of the on-site and the derived data further includes:
and the batch replacement module is used for carrying out batch replacement of the intelligent electric meters.
In a fifth aspect, the method for replacing the smart meter based on load prediction and data double confirmation provided by the embodiment of the invention includes the following steps:
the load prediction method for the replacement of the intelligent electric meter comprises the following steps;
and/or
The steps of the smart meter reloading method based on double confirmation of the field and the export data are as described above.
In a sixth aspect, an apparatus for replacing a smart meter based on load prediction and data double-confirmation provided by an embodiment of the present invention includes:
the load prediction device for the replacement of the intelligent ammeter;
and/or
The intelligent ammeter reloading device based on double confirmation of the field and the derived data.
In a seventh aspect, an embodiment of the present invention provides a computer device, including a processor, a memory, and a bus, where the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions to perform the steps of the load prediction method for a smart meter replacement as described in any of the above, and the steps of the method for a smart meter replacement based on double confirmation of field and export data as described in any of the above.
In an eighth aspect, an embodiment of the present invention provides a storage medium, where a computer program is stored, where the computer program is executed by a processor to perform the steps of the method for load prediction of a smart meter reload according to any of the above, and/or the steps of the method for smart meter reload according to any of the above, based on double confirmation of live and export data.
The technical scheme of the embodiment of the invention has the following beneficial effects:
(1) According to the invention, according to the electricity consumption habit of the user and the influence of environmental factors on the load, a short daily load data prediction method suitable for a small number of samples is adopted, and compared with the traditional prediction method of multiplying the daily average electric quantity by the interval days, the prediction accuracy is high, the error is small, and the time limit of the operation time is smaller compared with that of a complex machine learning prediction method.
(2) The invention adopts a data comparison double-confirmation method, and improves the replacement accuracy rate by comparing the predicted data with the actual table codes twice before and after the replacement.
According to the method, the marketing technical support system data and the on-site stop code data are obtained, the predicted data and the actual stop code data are compared through a data regression algorithm, and the fact that no error occurs in meter replacement is confirmed. And comparing the daily forecast dosage data with the data fetched by the new table of the next day, and checking whether the replacement of the table is wrong again to realize data comparison double confirmation. The invention realizes the flow of the replacement work, automatically collects and identifies the field data, remarkably improves the automation degree, saves manpower and material resources and improves the replacement reliability of the field meter. After the replacement of the new and old electric meters is confirmed, the invention uploads the field materials (the meter replacement notice, the start and stop code confirmation list, the replacement photo and the like) to the background system so as to facilitate the inquiry of a user, thereby improving the accuracy of the data processing capability and the replacement reliability of the field meter.
Drawings
FIG. 1 is a flow chart illustrating a method of smart meter retrofitting based on load prediction and data double validation according to an exemplary embodiment;
FIG. 2 is a schematic diagram illustrating an apparatus for smart meter retrofitting based on load prediction and data double validation in accordance with an exemplary embodiment;
FIG. 3 is a flow chart illustrating another method of smart meter retrofitting based on load prediction and data double validation according to an exemplary embodiment;
FIG. 4 is a schematic diagram illustrating another smart meter retrofit device based on load prediction and data double validation in accordance with an exemplary embodiment;
FIG. 5 is a flowchart illustrating an implementation of load prediction in conjunction with partial least squares regression of environmental factors, according to an exemplary embodiment;
FIG. 6 is a flowchart showing an implementation of a smart meter retrofit based on double confirmation of field and export data, according to an example embodiment;
FIG. 7 is a schematic diagram illustrating a metering device batch replacement control system master station function in accordance with an exemplary embodiment.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
in order to clearly illustrate the technical features of the present solution, the present invention will be described in detail below with reference to the following detailed description and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different structures of the invention. In order to simplify the present disclosure, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and processes are omitted so as to not unnecessarily obscure the present invention.
Example 1
As shown in fig. 1, the load prediction method for a smart meter replacement provided by the embodiment of the invention includes the following steps:
acquiring field data and historical data of the intelligent ammeter, and constructing a short-day load prediction data set by adopting the historical data;
establishing a PLS regression equation by using the data set;
and carrying out load data prediction by using a PLS regression equation.
As a possible implementation manner of this embodiment, the collecting field data and historical data of the smart meter, and constructing a short-day load prediction data set by using the historical data includes:
collecting the stop code data of the intelligent ammeter which is replaced on site;
the method comprises the steps of exporting historical data of the intelligent ammeter through a marketing technical support system;
constructing data sets by adopting 17 data, and constructing 10 groups of data by taking load samples of 24 days before the cut-off date of historical dataA set, where y is the output load, x 1 -x 16 To input data, x 1 -x 14 For the power load of the first 14 days, x 15 、x 16 The cloud cover and the air temperature of the current day are respectively predicted.
As a possible implementation manner of this embodiment, the establishing a PLS regression equation using the data set includes:
carrying out standardization processing on the data set, and extracting a residual equation for constructing a regression equation through a main component;
obtaining regression coefficients of the output matrix with respect to the main components through calculation after checking and crossing;
and obtaining a regression equation of the output matrix and the input matrix through a standardized inverse process.
As a possible implementation manner of this embodiment, the load data prediction using PLS regression equation includes:
carrying out load data prediction by using a PLS regression equation to obtain a first predicted value;
the obtained predicted value is used as input to construct new input set to predict the load data of the next day, so that the prediction is repeated, and finally, the load predicted value of the current day of field replacement is obtained;
comparing the load predicted value of the current day of field replacement with the code data of the intelligent ammeter of the field replacement, if the error requirement is met, considering that the prediction is accurate, otherwise, returning to the main component extraction of the regression coefficient calculation process until the error requirement is met.
Example 2
As shown in fig. 2, based on the load prediction method of the smart meter replacement according to embodiment 1, the load prediction device of the smart meter replacement according to the embodiment of the present invention includes:
the data acquisition module is used for acquiring field data and historical data of the intelligent ammeter and constructing a short-day load prediction data set by adopting the historical data;
the regression equation building module is used for building a PLS regression equation by utilizing the data set;
and the load data prediction module is used for predicting the load data by using the PLS regression equation.
Example 3
As shown in fig. 3, the method for replacing the smart meter based on double confirmation of the on-site and derived data provided by the embodiment of the invention comprises the following steps:
obtaining prediction data of the replacement date and intelligent ammeter stop code data of field replacement by using short-day load prediction, and carrying out primary confirmation of ammeter replacement comparison;
and (5) performing reconfirmation of ammeter replacement comparison.
As a possible implementation manner of this embodiment, the obtaining, by using the short-day load prediction, the prediction data of the current day of replacement and the smart meter stop code data of the on-site replacement, and performing one-time confirmation of the meter replacement comparison includes:
obtaining predicted data of the on-site replacement date by using the load prediction method of the intelligent ammeter replacement according to any of the above;
comparing the predicted data of the current day of field replacement with the stop code data collected on the field, so that the error of the predicted data meets the error requirement;
or comparing the predicted data of the current day of field replacement with the predicted data of the traditional method of multiplying the average daily electric quantity by the number of days of interval, so that the error of the predicted data meets the error requirement.
As a possible implementation manner of this embodiment, the reconfirming of the ammeter replacement comparison includes:
initiating a list changing process in a marketing system, initiating an acquisition scheme process after the process is filed, issuing an acquisition task, and remotely acquiring list codes to obtain data of the next day list codes;
continuously predicting by using the load prediction method for replacing the intelligent ammeter according to any of the above, so as to obtain prediction data of the next day of replacing the ammeter on site;
and comparing the predicted data of the next day of field replacement with the code data of the next day table, and enabling the error of the predicted data to meet the error requirement.
As one possible implementation manner of this embodiment, the method for replacing a smart meter based on double confirmation of on-site and export data further includes the following steps:
and carrying out batch replacement of the intelligent electric meters.
As a possible implementation manner of this embodiment, the performing batch reloading of the smart meter includes:
acquiring basic information of operators of the electric energy metering device and information of on-site replacement equipment;
acquiring the task of the on-site replacement metering device and the latest meter reading information of the metering device;
the data acquisition and recognition are carried out on the replacement site, the picture and digital information of the site metering device are obtained, and the information quality verification and normalized storage are carried out on the related information;
based on the process information, the metering data and the information quality basic information of the field metering device replacement operation, the final overall quality information of the replacement operation is formed.
Example 4
As shown in fig. 4, based on the method for reloading a smart meter based on double confirmation of field and export data in embodiment 3, the smart meter reloading device based on double confirmation of field and export data provided by the embodiment of the invention includes:
the primary confirmation module is used for carrying out primary confirmation of ammeter replacement comparison by utilizing the prediction data of the short-day load to obtain the replacement date and the intelligent ammeter stop code data of the field replacement;
and the reconfirming module is used for reconfirming the replacement comparison of the ammeter.
As one possible implementation manner of this embodiment, the smart meter reloading device based on double confirmation of the on-site and the derived data further includes:
and the batch replacement module is used for carrying out batch replacement of the intelligent electric meters.
Example 5
The embodiment of the invention provides a method for replacing an intelligent electric meter based on load prediction and data double confirmation, which comprises the following steps:
(1) And a load prediction process of the intelligent ammeter replacement combined with the environment factor partial least square regression.
A short-day load prediction dataset is constructed. The load difference between the data exported by the marketing technical support system and the stop code data collected by the on-site reloading is several days, generally 3 days, and in order to judge that the list replacement is correct by a load comparison method, the load prediction of a short day is needed. The original method of multiplying the daily average electric quantity by the interval days is too simple, and the calculation speed is high, but the accuracy is low. The invention combines environmental factors, utilizes partial least squares regression (PLS) to predict, not only can solve the problem of small sample number, but also considers the environmental factors and satisfies the calculation time limit. Considering the environmental factors affecting the general users, wherein the illumination intensity affects the use of solar energy, and the temperature affects the use of air conditioners and water heaters, which are the main factors affecting the electricity consumption of users. Secondly, the user generally has own habit of using electricity, and historical electricity using data of the user are needed, so the invention adopts 17 data to construct a data set, wherein y is output load and x 1 -x 16 To input data, x 1 -x 14 For the power load of the first 14 days, x 15 、x 16 And respectively predicting the cloud cover and the air temperature of the current day, and taking load samples 24 days before the expiration date of the derived data to construct 10 groups of data sets.
Establishment of PLS regression equation. After the data set is constructed, the regression coefficient of the PLS equation is calculated. After the data set is subjected to standardization processing, a residual equation of a regression equation is constructed through extraction of main components, regression coefficients of an output matrix relative to the main components are obtained through calculation after checking and crossing, and a regression equation of the output matrix and an input matrix is obtained through a standardized inverse process.
And carrying out load data prediction by using a PLS regression equation. And (5) after the partial least square regression equation is obtained, load data prediction can be performed. Taking 3 days as an example, after obtaining a first predicted value, a new input set is constructed by taking the predicted value as input to predict the load of the next day, so as to obtain the load predicted value of the current day of field replacement. If the error requirement is met, the prediction is considered to be accurate, otherwise, the main component extraction in the regression coefficient calculation process is returned until the error requirement is met.
And/or
(2) And (3) a smart meter reloading process based on double confirmation of the field and the export data.
The second aspect of the invention provides a smart meter replacement comparison method based on double confirmation of field and export data, which comprises the following steps:
and (5) one-time confirmation of ammeter replacement comparison. By adopting the load prediction method in the short day, after the predicted data of the current day of replacement is obtained, the predicted data is compared with the stop code data collected on site, and as the data of different users generally have larger gaps, the data error can be accurately judged if controlled in a smaller range. Or comparing the predicted data of the current day of replacement with the predicted data of the traditional method of multiplying the average daily electric quantity by the number of days of interval, and judging that the replacement is accurate if the error is smaller.
And (5) reconfirming the replacement comparison of the ammeter. After the accurate replacement is determined, the background initiates a list replacement process in the marketing system, initiates an acquisition scheme process after the process is filed, issues an acquisition task, remotely acquires a list code and checks the next day. And by the prediction method, the load data of the next day is continuously predicted and compared with the table code data of the next day, and the replacement is confirmed to be correct again with smaller error.
And the intelligent electric meter is replaced in batches by adopting a master station of the management and control system. The main function of the management and control system main station is to import and store basic information of operators of the electric energy metering device; managing field replacement equipment information; storing the task of the on-site replacement metering device and the latest meter reading information of the metering device; storing information such as pictures, numbers and the like acquired and identified on site to a site metering device, and carrying out information quality check and normalized storage on related information; based on basic information such as process information, measurement data, information quality and the like of the field metering device replacement operation, the integral quality information of the final replacement operation is formed, the scientificity and the management and control level of the batch replacement management and control of the electric energy metering devices in the distribution area are further improved, and the electric energy metering devices are easy to inquire by a user.
Example 6
Based on the method for reloading the intelligent ammeter based on the load prediction and the data double confirmation in the embodiment 5, the device for reloading the intelligent ammeter based on the load prediction and the data double confirmation provided by the embodiment of the invention comprises the following steps:
the load prediction device for the replacement of the intelligent ammeter;
and/or
The intelligent ammeter reloading device based on double confirmation of the field and the derived data.
The device for replacing the intelligent electric meter based on the load prediction and the data double confirmation is adopted for replacing the intelligent electric meter as follows.
In order to solve the problems mentioned in the background art, an effective way is to improve the replacement accuracy of the new and old electric energy meters. Under the condition that the time limit requirement is met, the power consumption load of the 14 days before and the cloud amount and temperature information of the predicted current day are taken as model input, the current day load is taken as model output, and the regression equation is calculated through 10 sets of data sets, so that the power consumption habit of a user is considered, the environmental information factor is considered, the error of the predicted data is smaller than that of the traditional method, and the reliability of field ammeter replacement is improved.
According to the method, the regression coefficient is obtained in a short time by constructing a partial least square fitting model according to the association relation between the derived data of the marketing system and the site stop code, and the load data is predicted with high precision.
1. Load prediction combined with partial least squares regression of environmental factors.
As shown in fig. 5, the specific steps of load prediction combined with the environment factor partial least squares regression are as follows:
step S101: a short-day load prediction dataset is constructed. The invention takes the two factors as the environmental factors influencing the load, and x is respectively taken as the factors 15 、x 16 Indicating the predicted current day cloud cover and air temperature. Besides the influence of the external factors, different users have different electricity utilization habits, and the invention uses x 1 -x 14 The power consumption load of the 14 days before prediction is represented, so that the power consumption habit of the user is reflected. Output as predicted load on day toy represents that 17 data form a data set, and 10 groups of data sets are constructed by taking historical data of the first 24 days and relevant weather information.
Step S102: establishing PLS regression equation and calculating regression coefficient. After the data set is constructed, the regression coefficient of the PLS equation is calculated. The calculation process is briefly described as follows: after the data set is subjected to standardization processing, a residual equation of a regression equation is constructed through extraction of main components, regression coefficients of an output matrix relative to the main components are obtained through calculation after checking and crossing, and a regression equation of the output matrix and an input matrix is obtained through a standardized inverse process.
Step S103: and (5) comparing and confirming the predicted data with the field stop code. And (5) after the partial least square regression equation is obtained, load data prediction can be performed. Taking 3 days as an example, after obtaining a first predicted value, a new input set is constructed by taking the predicted value as input to predict the load of the next day, so as to obtain the load predicted value of the current day of field replacement. If the error requirement is met, the prediction is considered to be accurate, otherwise, the main component extraction in the regression coefficient calculation process is returned until the error requirement is met.
When the replacement is carried out on the day, if the prediction result is smaller than the error of the traditional method, the error compared with the on-site code stop data is within the allowable range, and the replacement is considered to be correct.
2. And (5) comparing the replacement of the intelligent ammeter based on double confirmation of the field and the derived data.
After the replacement prediction data are obtained, the replacement is correct through primary confirmation, the marketing system replacement process is initiated, the secondary confirmation of the prediction data is carried out after the next day of table code data are acquired, the replacement is checked, the ammeter replacement work flow is completed, and various data in the replacement process are stored in a metering device batch replacement management and control system main station for scientific management and control.
As shown in fig. 6, the specific steps of the smart meter replacement comparison based on the double confirmation of the site and the derived data are as follows:
step S201: after load prediction in a short day, prediction data of the current day of replacement is obtained, and then the prediction data is compared with stop code data collected on site, and as data of different users generally have a large gap, if data errors are controlled in a small range, the judgment is accurate.
Specifically: firstly, calculating predicted data to the on-site replacement date, comparing the predicted data with the on-site code number, and under the condition that the error requirement is met, the replacement can be considered to be accurate, the predicted data can be compared with the data predicted by the traditional method of multiplying the average daily electric quantity by the interval days, and the replacement can be judged to be accurate with smaller error. If the error requirement is not met, the regression model is required to be readjusted for calculation, and the meter replacement is ensured not to be in error.
Step S202: and (5) reconfirming the replacement comparison of the ammeter. After the accurate replacement is determined, the background initiates a list replacement process in the marketing system, initiates an acquisition scheme process after the process is filed, issues an acquisition task, remotely acquires a list code and checks the next day. And by the prediction method, the load data of the next day is continuously predicted and compared with the table code data of the next day, and the replacement is confirmed to be correct again with smaller error.
Step S203: after double-confirmation of the replacement, various information in the replacement process is stored in a main station of the batch replacement management and control system of the metering device. As shown in fig. 7, the main function of the master station system is to import and store basic information of operators of the electric energy metering device; managing field replacement equipment information; storing the task of the on-site replacement metering device and the latest meter reading information of the metering device; storing information such as pictures, numbers and the like acquired and identified on site to a site metering device, and carrying out information quality check and normalized storage on related information; based on basic information such as process information, measurement data, information quality and the like of the field metering device replacement operation, the integral quality information of the final replacement operation is formed, the scientificity and the management and control level of the batch replacement management and control of the electric energy metering devices in the distribution area are further improved, and the electric energy metering devices are easy to inquire by a user.
The invention designs the framework and the functions of the main station of the batch replacement management and control system of the metering device, so that the replacement steps of the ammeter are more standard, and the reliability, the openness and the expandability are improved.
Example 7
The embodiment of the invention provides a computer device, which comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the device runs, the processor and the memory are communicated through the bus, and the processor executes the machine-readable instructions to execute the steps of the load prediction method of the smart meter replacement according to any of the above, and/or the steps of the method of the smart meter replacement based on double confirmation of on-site and derived data according to any of the above.
In particular, the above-mentioned memory and processor can be general-purpose memory and processor, without being limited thereto, and when the processor runs the computer program stored in the memory, the steps of the load prediction method for a smart meter replacement as described in any of the above, and/or the steps of the method for a smart meter replacement based on double confirmation of the presence and the export data as described in any of the above can be performed.
It will be appreciated by those skilled in the art that the structure of the computer device is not limiting of the computer device and may include more or fewer components than shown, or may be combined with or separated from certain components, or may be arranged in a different arrangement of components.
In some embodiments, the computer device may further include a touch screen operable to display a graphical user interface (e.g., a launch interface of an application) and to receive user operations with respect to the graphical user interface (e.g., launch operations with respect to the application). A particular touch screen may include a display panel and a touch panel. The display panel may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), or the like. The touch panel may collect touch or non-touch operations on or near the user and generate preset operation instructions, for example, operations of the user on or near the touch panel using any suitable object or accessory such as a finger, a stylus, or the like. In addition, the touch panel may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth and the touch gesture of a user, detects signals brought by touch operation and transmits the signals to the touch controller; the touch controller receives touch information from the touch detection device, converts the touch information into information which can be processed by the processor, sends the information to the processor, and can receive and execute commands sent by the processor. In addition, the touch panel may be implemented by various types such as resistive, capacitive, infrared, and surface acoustic wave, or may be implemented by any technology developed in the future. Further, the touch panel may overlay the display panel, and a user may operate on or near the touch panel overlaid on the display panel according to a graphical user interface displayed by the display panel, and upon detection of an operation thereon or thereabout, the touch panel is transferred to the processor to determine a user input, and the processor then provides a corresponding visual output on the display panel in response to the user input. In addition, the touch panel and the display panel may be implemented as two independent components or may be integrated.
Example 8
Corresponding to the method for starting the application program, the embodiment of the invention further provides a storage medium, and the storage medium stores a computer program, and the computer program executes the steps of the method for predicting the load of the smart meter replacement according to any of the above, and/or the steps of the method for replacing the smart meter based on double confirmation of the field and the derived data according to any of the above when being run by a processor.
The starting device of the application program provided by the embodiment of the application program can be specific hardware on the equipment or software or firmware installed on the equipment. The device provided in the embodiments of the present application has the same implementation principle and technical effects as those of the foregoing method embodiments, and for a brief description, reference may be made to corresponding matters in the foregoing method embodiments where the device embodiment section is not mentioned. It will be clear to those skilled in the art that, for convenience and brevity, the specific operation of the system, apparatus and unit described above may refer to the corresponding process in the above method embodiment, which is not described in detail herein.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of modules is merely a logical function division, and there may be additional divisions in actual implementation, and for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted, or not performed. In addition, the coupling or direct coupling or communication connection shown or discussed with respect to each other may be through some communication interface, indirect coupling or communication connection of devices or modules, electrical, mechanical, or other form.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments provided in the present application may be integrated in one processing module, or each module may exist alone physically, or two or more modules may be integrated in one module.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, 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 specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.
Claims (16)
1. The load prediction method for the replacement of the intelligent electric meter is characterized by comprising the following steps of:
acquiring field data and historical data of the intelligent ammeter, and constructing a short-day load prediction data set by adopting the historical data;
establishing a PLS regression equation by using the data set;
and carrying out load data prediction by using a PLS regression equation.
2. The method for predicting load of a smart meter replacement according to claim 1, wherein the collecting smart meter field data and history data and constructing a short-day load prediction data set using the history data comprises:
collecting the stop code data of the intelligent ammeter which is replaced on site;
the method comprises the steps of exporting historical data of the intelligent ammeter through a marketing technical support system;
constructing a data set by adopting 17 data, and constructing 10 groups of data sets by taking load samples of 24 days before the expiration date of historical data, wherein y is an output load and x is 1 -x 16 To input data, x 1 -x 14 For the power load of the first 14 days, x 15 、x 16 The cloud cover and the air temperature of the current day are respectively predicted.
3. The smart meter replacement load prediction method of claim 2, wherein the establishing a PLS regression equation using the data set includes:
carrying out standardization processing on the data set, and extracting a residual equation for constructing a regression equation through a main component;
obtaining regression coefficients of the output matrix with respect to the main components through calculation after checking and crossing;
and obtaining a regression equation of the output matrix and the input matrix through a standardized inverse process.
4. The load prediction method of a smart meter replacement according to claim 1, wherein the load data prediction using PLS regression equation comprises:
carrying out load data prediction by using a PLS regression equation to obtain a first predicted value;
the obtained predicted value is used as input to construct new input set to predict the load data of the next day, so that the prediction is repeated, and finally, the load predicted value of the current day of field replacement is obtained;
comparing the load predicted value of the current day of field replacement with the code data of the intelligent ammeter of the field replacement, if the error requirement is met, considering that the prediction is accurate, otherwise, returning to the main component extraction of the regression coefficient calculation process until the error requirement is met.
5. The intelligent ammeter reloading method based on double confirmation of the site and the derived data is characterized by comprising the following steps:
obtaining prediction data of the replacement date and intelligent ammeter stop code data of field replacement by using short-day load prediction, and carrying out primary confirmation of ammeter replacement comparison;
and (5) performing reconfirmation of ammeter replacement comparison.
6. The method for replacing a smart meter based on double confirmation of field and export data according to claim 5, wherein the obtaining the predicted data of the replacement date by using the short-day load prediction and the smart meter stop code data of the field replacement, performing one confirmation of meter replacement comparison, comprises:
obtaining predicted data of the on-site replacement date by using the load prediction method of the smart meter replacement according to any one of claims 1 to 4;
comparing the predicted data of the current day of field replacement with the stop code data collected on the field, so that the error of the predicted data meets the error requirement;
or comparing the predicted data of the current day of field replacement with the predicted data of the traditional method of multiplying the average daily electric quantity by the number of days of interval, so that the error of the predicted data meets the error requirement.
7. The method for performing double-confirmation of smart meter replacement based on field and export data according to claim 5, wherein the re-confirmation of meter replacement comparison comprises:
initiating a list changing process in a marketing system, initiating an acquisition scheme process after the process is filed, issuing an acquisition task, and remotely acquiring list codes to obtain data of the next day list codes;
continuing to predict by using the load prediction method of the smart meter replacement according to any one of claims 1 to 4 to obtain prediction data of the next day of field replacement;
and comparing the predicted data of the next day of field replacement with the code data of the next day table, and enabling the error of the predicted data to meet the error requirement.
8. The method for smart meter replacement based on double confirmation of presence and export data according to any of claims 5 to 7, further comprising the steps of:
and carrying out batch replacement of the intelligent electric meters.
9. The method for replacing smart meters based on double confirmation of field and export data according to claim 8, wherein the performing the batch replacement of smart meters comprises:
acquiring basic information of operators of the electric energy metering device and information of on-site replacement equipment;
acquiring the task of the on-site replacement metering device and the latest meter reading information of the metering device;
the data acquisition and recognition are carried out on the replacement site, the picture and digital information of the site metering device are obtained, and the information quality verification and normalized storage are carried out on the related information;
based on the process information, the metering data and the information quality basic information of the field metering device replacement operation, the final overall quality information of the replacement operation is formed.
10. The utility model provides a load prediction device of smart electric meter dress, its characterized in that includes:
the data acquisition module is used for acquiring field data and historical data of the intelligent ammeter and constructing a short-day load prediction data set by adopting the historical data;
the regression equation building module is used for building a PLS regression equation by utilizing the data set;
and the load data prediction module is used for predicting the load data by using the PLS regression equation.
11. Intelligent ammeter reloading device based on-site and export data double-confirmation, which is characterized by comprising:
the primary confirmation module is used for carrying out primary confirmation of ammeter replacement comparison by utilizing the prediction data of the short-day load to obtain the replacement date and the intelligent ammeter stop code data of the field replacement;
and the reconfirming module is used for reconfirming the replacement comparison of the ammeter.
12. The smart meter retrofit device based on field and export data dual validation of claim 11, further comprising:
and the batch replacement module is used for carrying out batch replacement of the intelligent electric meters.
13. The intelligent ammeter reloading method based on load prediction and data double confirmation is characterized by comprising the following steps of:
the method for predicting the load of a smart meter replacement according to any one of claims 1 to 4;
and/or
A method of field and export data dual validation based smart meter retrofitting according to any of claims 5 to 9.
14. The utility model provides a device of smart electric meter reload based on load prediction and data double-confirmation which characterized in that includes:
the smart meter retrofit load prediction device of claim 10;
and/or
The smart meter retrofit device of claim 11 or 12 based on dual confirmation of field and export data.
15. A computer device comprising a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory communicating over the bus when the computer device is in operation, the processor executing the machine readable instructions to perform the steps of the load prediction method of a smart meter replacement as claimed in any one of claims 1 to 4 and/or the steps of the method of a smart meter replacement based on double confirmation of live and export data as claimed in any one of claims 5 to 9.
16. A storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for load prediction of a smart meter retrofit according to any one of claims 1 to 4 and/or the steps of the method for smart meter retrofit based on double confirmation of presence and export data according to any one of claims 5 to 9.
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