CN117273337A - Intelligent electric energy meter evaluation method - Google Patents
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- 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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- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R35/00—Testing or calibrating of apparatus covered by the other groups of this subclass
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
- H02J13/00001—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network characterised by the display of information or by user interaction, e.g. supervisory control and data acquisition systems [SCADA] or graphical user interfaces [GUI]
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Abstract
The invention relates to the field of power equipment evaluation methods. The invention relates to an intelligent electric energy meter evaluation method. Which comprises the following steps: the method comprises the steps of collecting user electric energy meter performance data, preprocessing the data, analyzing the stability of the electric energy meter according to user historical electric energy meter data, analyzing the power utilization habit of a user to predict power demand information required by the user in different time periods, predicting the power demand data of the user, and adjusting power resource distribution conditions in real time.
Description
Technical Field
The invention relates to the field of power equipment evaluation methods, in particular to an intelligent electric energy meter evaluation method.
Background
At present, along with the development of energy management and power system intellectualization, the intelligent electric energy meter replaces the traditional ammeter, the intelligent electric energy meter is widely applied as important equipment for electric energy metering and data acquisition, the intelligent electric energy meter provides more convenient, accurate and intelligent electricity utilization service for users and power suppliers through collecting electric energy utilization information, real-time monitoring, data storage, remote communication and other functions, the intelligent electric energy meter promotes convenience for life of the users in the use process, but in the use process of the intelligent electric energy meter, an electric company predicts the load based on supplying power to the users in different areas, and in the prediction process, due to the fact that the user base is large, the power demand deviation is large, power in some areas is excessive, load conditions are generated in some areas, voltage is unstable, and user use experience is affected.
Disclosure of Invention
The invention aims to provide an intelligent electric energy meter evaluation method for solving the problems in the background technology.
In order to achieve the above purpose, an intelligent electric energy meter evaluation method is provided, which comprises the following steps:
s1, collecting performance data of a user electric energy meter, and preprocessing the data;
s2, storing the data to a cloud platform, and analyzing the stability of the electric energy meter according to the historical electric energy meter data of the user;
s3, acquiring data information of the electric energy meter of the user in the S2, and analyzing the electricity utilization habit of the user;
s4, predicting power demand information required by the user in different time periods according to the power consumption habit of the user;
and S5, dividing the power demand areas, collecting the predicted user power demand data of different areas in the S4, and adjusting the power resource allocation situation in real time.
As a further improvement of the technical scheme, the real-time monitoring device is adopted in the step S1 to collect the performance data of the electric energy meter and transmit the performance data to the data processing system.
As a further improvement of the present technical solution, the preprocessing of the data in S1 includes the following steps:
s1.1, cleaning collected performance data, and identifying and correcting missing values, abnormal values and repeated values;
s1.2, setting time stamp information for identifying real-time electricity utilization data information of a user.
As a further improvement of the technical scheme, in the step S2, according to the user history electric energy meter data, the stability of the electric energy meter is analyzed, and the method comprises the following steps:
s2.1, drawing the user electric energy meter data into a histogram by adopting a drawing tool, and visually penetrating the data distribution and change conditions;
s2.2, processing and analyzing historical data of the statistical electric energy meter, and calculating performance indexes of the electric energy meter.
As a further improvement of the technical scheme, the error coefficient of the electric energy meter can be calculated by adopting a least square method in the step S2.2, and the stability of the electric energy meter is high if the error coefficient is small.
As a further improvement of the present technical solution, the step of analyzing the electricity usage habit of the user in S3 includes the following steps:
s3.1, collecting electricity utilization data of the user in the S2.1, and dividing the electricity utilization time period of the user;
s3.2, identifying the electricity consumption of the user in different time periods, and analyzing the electricity consumption habit of the user in different time periods;
and S3.3, providing a personalized electricity utilization strategy for the user according to the electricity utilization habit of the user.
As a further improvement of the present technical solution, the step S4 of predicting the power information required by the user in different time periods specifically includes the following steps:
s4.1, acquiring the electricity consumption of the user in the S3.2 in different time periods, and obtaining the electricity consumption time period distribution characteristics of the user;
s4.2, establishing a power consumption prediction model, and training and verifying the selected prediction model by using historical power consumption data;
and S4.3, predicting the electricity consumption of the user in different time periods based on the electricity consumption prediction model.
As a further improvement of the technical scheme, in S5, users are divided into different areas through GPS, and the electricity consumption of the users in the different areas is collected through an electric energy meter.
As a further improvement of the present technical solution, the step S5 of adjusting the power resource allocation in real time specifically includes the following steps:
s5.1, collecting user electric energy meter data of different areas through a real-time monitoring system;
s5.2, predicting the electricity consumption of the users in different areas according to the electricity prediction model in S4.3, and regulating and distributing the electricity resources in real time through an automatic control system according to the prediction result.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the intelligent electric energy meter evaluation method, the intelligent electric energy meter data of a user are collected, the stability of the electric energy meter is analyzed according to the historical data of the electric energy meter, the stability of the electric energy meter is analyzed, the historical data recorded by the electric energy meter is obtained, the electricity utilization habit of the user is analyzed, a personalized electricity utilization strategy is provided for the user according to the electricity utilization habit of the user, more accurate and reliable electricity utilization data can be provided for the user through the historical data analysis of the electric energy meter, more accurate and reliable data support is provided for an electric power market, real-time monitoring and analysis are carried out on the electric power market, abnormal conditions of electric power load are found timely, and corresponding measures are taken.
2. According to the intelligent electric energy meter evaluation method, the electricity consumption habit of the user is analyzed, the electricity consumption prediction model is established, the electricity consumption of different time periods is predicted according to the habit of the user, and when the electric power resources are distributed to different areas, the electricity consumption of the user is predicted so as to judge the electric quantity required by the areas, so that the loads of the different areas are accurately judged, the distribution of the electric power resources is regulated, the phenomenon of uneven loads in the areas is avoided, and the reliability and the stability of the intelligent power grid are improved.
Drawings
FIG. 1 is an overall flow diagram of the present invention;
FIG. 2 is a block diagram of a process for collecting performance data of an electric energy meter according to the present invention;
FIG. 3 is a block flow diagram of the present invention for analyzing the stability of an electric energy meter;
FIG. 4 is a flow chart for analyzing the electricity utilization habit of a user according to the invention;
FIG. 5 is a block diagram of a predicted consumer power demand flow in accordance with the present invention;
fig. 6 is a flow chart of the present invention for adjusting power resource allocation in real time.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6, the present embodiment is directed to providing an intelligent electric energy meter evaluation method, which includes the following steps:
s1, collecting performance data of a user electric energy meter, and preprocessing the data;
in order to facilitate real-time collection of the ammeter data, therefore, the real-time monitoring device is adopted in the step S1 to collect the ammeter performance data and transmit the ammeter performance data to the data processing system, and the ammeter is provided with the real-time monitoring device, such as a sensor or data collection equipment, for real-time monitoring of the performance parameters of the ammeter, and the equipment can monitor the performance real-time data of the ammeter, such as electric energy metering, power factor, frequency, voltage, current and the like.
Considering that some data are missing or duplicated when the data are collected, the data are chaotic, and therefore, the preprocessing of the data in S1 includes the following steps:
s1.1, cleaning collected performance data, and identifying and correcting missing values, abnormal values and repeated values;
s1.2, setting time stamp information for identifying real-time electricity utilization data information of a user.
The method has the advantages that the data are cleaned, the repeated data are deleted, the missing values are filled through an interpolation method, the quality of the collected data is improved, misleading analysis is reduced, meanwhile, the time stamps are arranged on the data information, the electricity utilization data information can be ordered when needed, so that the electricity utilization trend is conveniently analyzed, and meanwhile, the data information can be screened and extracted according to the time range, so that the subsequent analysis and application are facilitated.
S2, storing the data to a cloud platform, and analyzing the stability of the electric energy meter according to the historical electric energy meter data of the user;
various relational database services are provided on cloud platforms, such as MySQL, postgreSQL, etc., to which data storage, querying and updating operations may be connected and performed by creating a database and storing the data therein in the form of tables, using a database management tool or library of programming languages.
Considering that the electric energy meter may cause inaccurate electric energy meter data due to the problem of years in the use process, therefore, in the step S2, according to the historical electric energy meter data of the user, the stability of the electric energy meter is analyzed, and the method comprises the following steps:
s2.1, drawing the user electric energy meter data into a histogram by adopting a drawing tool, and visually penetrating the data distribution and change conditions;
s2.2, processing and analyzing historical data of the statistical electric energy meter, and calculating performance indexes of the electric energy meter;
and S2.2, the error coefficient of the electric energy meter can be calculated by adopting a least square method, and the error coefficient is small, so that the electric energy meter has high stability, and the method is concretely as follows:
assuming that the electric energy meter has x nodes, current or voltage of the node i needs to be measured, the obtained actual value is yi, and the ideal value is yi≡, the following error equation can be written:
yi＝a0+a1xi^+a2xεi
wherein a0, a1 and a2 are unknown parameters, epsilon i is an error term, and the unknown parameters can be solved by a least square method to obtain an error curve or an error coefficient.
When the power consumption meter is particularly used, basic statistical indexes of historical data of the power meter, such as average value, minimum value, maximum value, standard deviation and the like, are calculated through historical data analysis of the power meter, average power consumption of a user per day is obtained, when the variation amplitude of the recent power consumption of the user is overlarge, the performance index of the power meter is analyzed and calculated, the error coefficient of the power meter can be calculated through a least square method, and the stability of the power meter is proved to be high if the error coefficient is small.
S3, acquiring data information of the electric energy meter of the user in the S2, and analyzing the electricity utilization habit of the user;
in order to obtain the electricity consumption habit of the user in each time period, therefore, the step of analyzing the electricity consumption habit of the user in S3 includes the following steps:
s3.1, collecting electricity utilization data of the user in the S2.1, and dividing the electricity utilization time period of the user;
s3.2, identifying the electricity consumption of the user in different time periods, and analyzing the electricity consumption habit of the user in different time periods;
s3.3, providing a personalized electricity utilization strategy for the user according to the electricity utilization habit of the user;
by equally dividing a day into equal time periods, for example, 24 hours are evenly divided into 24 time periods of 1 hour, the electricity consumption of a user in each time period is identified, peak time periods and valley time periods are determined according to electricity consumption data of the user through electricity consumption peak-valley analysis, the peak time periods generally refer to time periods with higher electricity consumption, the valley time periods refer to time periods with lower electricity consumption, the electricity consumption peak time periods and valley time periods of the user can be known through the electricity consumption of the peak time periods and the valley time periods, and accordingly a better planning electricity consumption strategy is provided for the user according to electricity consumption habits of the user, and comprises the following aspects:
peak-to-valley power usage strategy: according to the electricity consumption difference between the peak time and the valley time, users are encouraged to intensively use high-power electric appliances such as washing machines, dryers, dish washers and the like in the valley time, so that electricity can be used in the valley time with lower electricity price, and electricity cost is saved.
Energy-saving electricity utilization strategy: by providing energy-saving electricity utilization suggestions, users are encouraged to take some energy-saving measures, such as using energy-saving lamps, high-efficiency electrical equipment, a timing switch and the like, so that the consumption of energy sources is reduced, and the electricity utilization cost is reduced;
and (3) balancing an electricity utilization strategy: in the electricity consumption peak period, users are encouraged to adopt a time-interval electricity consumption strategy, so that excessive centralized electricity consumption is avoided; for example, the power utilization tasks are reasonably arranged, and a plurality of high-power electric appliances are avoided being used simultaneously so as to balance loads and reduce the pressure on a power grid.
S4, predicting power demand information required by the user in different time periods according to the power consumption habit of the user;
in order to facilitate the prediction of the electricity consumption of the user in different time periods, and facilitate the adjustment of the power distribution resources, so as to keep the power load stable, the step S4 of predicting the power information required by the user in different time periods specifically includes the following steps:
s4.1, acquiring the electricity consumption of the user in the S3.2 in different time periods, and obtaining the electricity consumption time period distribution characteristics of the user;
s4.2, establishing a power consumption prediction model, and training and verifying the selected prediction model by using historical power consumption data;
s4.3, predicting the electricity consumption of the user in different time periods based on the electricity consumption prediction model;
data visualization: displaying the electricity consumption of the user in different time periods by using a chart or a visual tool, such as a bar chart, a line graph and the like, so as to more intuitively observe and compare the electricity consumption in different time periods;
the electricity consumption prediction model is established to predict the electricity consumption of the user in different time periods, and the specific method is as follows:
collecting historical electricity consumption data: historical electricity consumption data of the acquired users, which need to be predicted, is collected and arranged, including electricity consumption or load data of each time period, and the data can comprise electricity consumption data of day, hour and even smaller time intervals.
Characteristic engineering: and extracting the characteristics of the historical electricity utilization data according to the requirements and the domain knowledge. These characteristics may include temporal characteristics (e.g., hours, days of the week, seasons, etc.), historical power usage statistics (e.g., average, maximum, minimum, etc.), and other relevant characteristics.
Dividing a training set and a verification set: dividing the preprocessed and feature extracted data into training sets, and training a power prediction model through the feature extracted data, so that the model predicts a future time period based on historical power consumption data of a user.
Model training and verification: the training set is used for training the selected power prediction model, verification and evaluation are carried out on the verification set, and the accuracy, precision and reliability of the model can be evaluated by comparing the prediction result with the actual value.
Model application and prediction: after training and verification of the model are completed, the model can be applied to actual electricity consumption prediction, and the model can predict electricity consumption or load of an future time period by inputting new characteristic data such as current time, weather forecast and the like, and correspondingly monitor and schedule.
When the intelligent electric energy meter is specifically used, real-time monitoring and analysis are carried out on an electric power market according to data of the intelligent electric energy meter, abnormal market conditions are found in time, corresponding measures are taken, meanwhile, more accurate and reliable electricity consumption data can be provided for users according to the data of the intelligent electric energy meter, transparency and fairness of the electric power market are improved, the electricity consumption data of the users are grouped and counted according to time periods through obtaining electricity consumption time period distribution characteristics of the users, total electricity consumption or average electricity consumption of each time period is calculated, the electricity consumption of different users in different time periods can be used as characteristics for analysis, the electricity consumption distribution characteristics of different time periods are analyzed according to the electricity consumption data of the users, the electricity consumption proportion or the change trend of the electricity consumption of each time period can be calculated, so that electricity consumption behavior modes of the users in different time periods can be known, the electricity consumption prediction model can be trained through the different time period distribution characteristics of the users, the electricity consumption trend of different users in a future time period can be predicted, and the total electricity consumption in the area can be judged through collecting the electricity consumption trend of the different users, and accurate distribution of power resources can be conveniently realized.
And S5, dividing the power demand areas, collecting the predicted user power demand data of different areas in the S4, and adjusting the power resource allocation situation in real time.
In the step S5, users are divided into different areas through a GPS, the electricity consumption of the users in the different areas is collected through an electric energy meter, a geographic space can be divided into different areas through the GPS, a map is divided into grids with different sizes, and each grid represents one area through combination;
in order to facilitate accurate allocation of the power resources and avoid power resource waste, the step S5 of adjusting the power resource allocation in real time specifically includes the following steps:
s5.1, collecting user electric energy meter data of different areas through a real-time monitoring system;
s5.2, predicting the electricity consumption of the users in different areas according to the electricity prediction model in S4.3, and regulating and distributing the electricity resources in real time through an automatic control system according to the prediction result.
Through the real-time monitoring to the user ammeter to collect user electric energy meter data, be used for knowing current electric power supply and demand condition, predict the electric quantity of different regional users according to electric power prediction model, predict the possibility of future electric power load, according to data analysis and the result of prediction, automated control system formulates corresponding electric power regulation strategy, electric power control strategy includes increasing and decreasing generating set's output power, adjustment transmission line's voltage etc. in order to realize required electric power supply and electric energy balance, actual electric power regulation operation is accomplished by executive equipment and device, such as automatic power generation control system, line switch equipment. The equipment adjusts related parameters and operations according to the received instructions so as to realize real-time control and distribution of power resources, and an automatic control system continuously monitors the state of a power system and receives feedback information of the equipment in the process of implementing regulation and control operation, wherein the information can be used for verifying regulation and control effects, adjusting strategies and monitoring and diagnosing in real time.
During specific use, the intelligent electric energy meter data of the user are collected, the stability of the electric energy meter is analyzed according to the historical data of the electric energy meter, the recorded historical data of the electric energy meter is obtained, the electricity consumption habit of the user is analyzed, the personalized electricity consumption strategy is provided for the user according to the electricity consumption habit of the user, more accurate and reliable electricity consumption data can be provided for the user through the historical data analysis of the electric energy meter, more accurate and reliable data support is provided for an electric power market, the electric power market is monitored and analyzed in real time, abnormal conditions of electric power load are found in time, corresponding measures are taken, the electricity consumption habit of the user is analyzed, an electricity consumption prediction model is established, the electricity consumption of different time periods is predicted according to the electricity consumption habit of the user, and when electric power resources are distributed to different areas, the electricity consumption of the user is predicted based on the electricity consumption habit of the user, so that the area is required, the load of the different areas is accurately judged, the electric power distribution is adjusted, and the phenomenon of uneven load in the area is avoided.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (9)
1. An intelligent electric energy meter evaluation method is characterized in that: the method comprises the following steps:
s1, collecting performance data of a user electric energy meter, and preprocessing the data;
s2, storing the data to a cloud platform, and analyzing the stability of the electric energy meter according to the historical electric energy meter data of the user;
s3, acquiring data information of the electric energy meter of the user in the S2, and analyzing the electricity utilization habit of the user;
s4, predicting power demand information required by the user in different time periods according to the power consumption habit of the user;
and S5, dividing the power demand areas, collecting the predicted user power demand data of different areas in the S4, and adjusting the power resource allocation situation in real time.
2. The intelligent ammeter evaluation method according to claim 1, wherein: and in the step S1, a real-time monitoring device is adopted to collect performance data of the electric energy meter and transmit the performance data to a data processing system.
3. The intelligent ammeter evaluation method according to claim 2, wherein: the preprocessing of the data in the step S1 comprises the following steps:
s1.1, cleaning collected performance data, and identifying and correcting missing values, abnormal values and repeated values;
s1.2, setting time stamp information for identifying real-time electricity utilization data information of a user.
4. The intelligent ammeter evaluation method according to claim 1, wherein: in the step S2, according to the historical electric energy meter data of the user, the stability of the electric energy meter is analyzed, and the method comprises the following steps:
s2.1, drawing the user electric energy meter data into a histogram by adopting a drawing tool, and visually penetrating the data distribution and change conditions;
s2.2, processing and analyzing historical data of the statistical electric energy meter, and calculating performance indexes of the electric energy meter.
5. The intelligent ammeter evaluation method according to claim 4, wherein: and S2.2, the error coefficient of the electric energy meter can be calculated by adopting a least square method, and the stability of the electric energy meter is high if the error coefficient is small.
6. The intelligent ammeter evaluation method according to claim 1, wherein: and S3, analyzing the electricity utilization habit of the user, wherein the method comprises the following steps of:
s3.1, collecting electricity utilization data of the user in the S2.1, and dividing the electricity utilization time period of the user;
s3.2, identifying the electricity consumption of the user in different time periods, and analyzing the electricity consumption habit of the user in different time periods;
and S3.3, providing a personalized electricity utilization strategy for the user according to the electricity utilization habit of the user.
7. The intelligent ammeter evaluation method according to claim 1, wherein: and S4, predicting the power information required by the user in different time periods, wherein the method specifically comprises the following steps of:
s4.1, acquiring the electricity consumption of the user in the S3.2 in different time periods, and obtaining the electricity consumption time period distribution characteristics of the user;
s4.2, establishing a power consumption prediction model, and training and verifying the selected prediction model by using historical power consumption data;
and S4.3, predicting the electricity consumption of the user in different time periods based on the electricity consumption prediction model.
8. The intelligent ammeter evaluation method according to claim 1, wherein: in the step S5, the users are divided into different areas through the GPS, and the electricity consumption of the users in the different areas is collected through the electric energy meter.
9. The intelligent ammeter evaluation method according to claim 8, wherein: and in the step S5, the power resource allocation condition is regulated in real time, and the method specifically comprises the following steps of:
s5.1, collecting user electric energy meter data of different areas through a real-time monitoring system;
s5.2, predicting the electricity consumption of the users in different areas according to the electricity prediction model in S4.3, and regulating and distributing the electricity resources in real time through an automatic control system according to the prediction result.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572331A (en) * | 2024-01-16 | 2024-02-20 | 山东宜美科节能服务有限责任公司 | Method and system for collecting data of intelligent ammeter in real time |
| CN117937374A (en) * | 2024-03-18 | 2024-04-26 | 兴盛电器股份有限公司 | Circuit breaker control method, system, computer equipment and storage medium |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108663651A (en) * | 2018-05-04 | 2018-10-16 | 国网上海市电力公司 | A kind of intelligent electric energy meter evaluation of running status system based on multisource data fusion |
| CN108830437A (en) * | 2018-04-09 | 2018-11-16 | 国电南瑞科技股份有限公司 | A kind of appraisal procedure for intelligent electric energy meter operation |
| CN112686493A (en) * | 2020-11-24 | 2021-04-20 | 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) | Method for evaluating running state and replacing of intelligent electric meter in real time by relying on big data |
| CN116579590A (en) * | 2023-07-13 | 2023-08-11 | 北京圆声能源科技有限公司 | Demand response evaluation method and system in virtual power plant |
| CN116646933A (en) * | 2023-07-24 | 2023-08-25 | 北京中能亿信软件有限公司 | Big data-based power load scheduling method and system |
-
2023
- 2023-09-19 CN CN202311213098.4A patent/CN117273337A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108830437A (en) * | 2018-04-09 | 2018-11-16 | 国电南瑞科技股份有限公司 | A kind of appraisal procedure for intelligent electric energy meter operation |
| CN108663651A (en) * | 2018-05-04 | 2018-10-16 | 国网上海市电力公司 | A kind of intelligent electric energy meter evaluation of running status system based on multisource data fusion |
| CN112686493A (en) * | 2020-11-24 | 2021-04-20 | 国网新疆电力有限公司营销服务中心(资金集约中心、计量中心) | Method for evaluating running state and replacing of intelligent electric meter in real time by relying on big data |
| CN116579590A (en) * | 2023-07-13 | 2023-08-11 | 北京圆声能源科技有限公司 | Demand response evaluation method and system in virtual power plant |
| CN116646933A (en) * | 2023-07-24 | 2023-08-25 | 北京中能亿信软件有限公司 | Big data-based power load scheduling method and system |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117572331A (en) * | 2024-01-16 | 2024-02-20 | 山东宜美科节能服务有限责任公司 | Method and system for collecting data of intelligent ammeter in real time |
| CN117572331B (en) * | 2024-01-16 | 2024-03-26 | 山东宜美科节能服务有限责任公司 | Method and system for collecting data of intelligent ammeter in real time |
| CN117937374A (en) * | 2024-03-18 | 2024-04-26 | 兴盛电器股份有限公司 | Circuit breaker control method, system, computer equipment and storage medium |
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