Data analysis system for intelligent electric meter

Abstract

The invention discloses a data analysis system for an intelligent electric meter, which belongs to the field of electric power data analysis and comprises the intelligent electric meter, a transmission network and a rear-end platform. The intelligent electric meter is used for acquiring power consumption data of a user, and the power consumption data comprises power consumption, a power factor, voltage, current, an electric meter address or time data; by integrating an intelligent electric meter, a transmission network and a back-end platform, an integrated process from data acquisition to analysis to application is realized. According to the design, the precision and efficiency of data acquisition are improved, more personalized and accurate services are provided for users through the powerful data processing capability and analysis capability of the rear end, meanwhile, an important decision support tool is provided for an electric power company, and optimization of electric power resource allocation and improvement of service quality are facilitated.

Description

Technical Field

[0001] The present invention belongs to the field of power data analysis, and in particular, relates to a data analysis system for a smart electric meter. Background Art

[0002] Smart meters are smart terminals of smart grids. In addition to the basic electricity consumption metering function of traditional electricity meters, in order to adapt to the use of smart grids and new energy, they also have two-way multi-rate metering function, user-side control function, two-way data communication function with multiple data transmission modes, anti-electricity theft function and other intelligent functions. Smart meters represent the development direction of intelligent terminals for end users of future energy-saving smart grids.

[0003] As the number of smart meters increases rapidly, the amount of data measured by smart meters is also increasing rapidly. At present, it has become a top priority for power grid companies to conduct big data analysis on the massive data of smart meters, explore user electricity consumption information, and predict the operation status of the power grid.

[0004] In view of this, this application is hereby filed. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a data analysis system for smart meters. The present invention is implemented through the following technical solutions:

[0006] A data analysis system for smart meters, the system comprising smart meters, a transmission network and a back-end platform; the smart meters are used to obtain users' electricity usage data, the electricity usage data including electricity usage, power factor, voltage, current, meter address or time and other data; the transmission network comprises a collector and a concentrator, which are used to collect data from the smart meters and transmit them to the concentrator; the concentrator is used to forward users' electricity usage data to the back-end platform; the back-end platform comprises a data processing center, a data analysis engine and an application platform; the data processing center is used to receive users' electricity usage data and clean, integrate and store the users' electricity usage data; the data analysis engine is used to conduct in-depth mining of users' electricity usage patterns through data analysis and identify potential energy-saving opportunities; the application platform is used to present energy-saving suggestions to users or display the operating status of the power grid.

[0007] Furthermore, when cleaning, integrating and storing user electricity consumption data, data cleaning includes consistency verification of user electricity consumption data, and removing, supplementing or standardizing duplicate, missing or erroneous data to ensure the integrity and standardization of the data.

[0008] Furthermore, the steps of cleaning, integrating and storing user electricity usage data include: segmenting the user electricity usage data by date according to the meter address and time, splitting and merging the user electricity usage data of the same slice and the same date, the splitting and merging including time matching, meter address matching and electricity usage matching, and storing the split and merged electricity usage data in a database in a columnar manner.

[0009] Furthermore, the data analysis engine includes an electricity consumption curve identification device, an abnormality analysis module, an energy efficiency evaluation module, a prediction module and an energy-saving suggestion module; the electricity consumption curve identification device is used to obtain the user's daily electricity consumption curve and identify the weekday electricity consumption curve, weekend electricity consumption curve and holiday electricity consumption curve in the electricity consumption curve based on the electricity consumption information in the user's electricity consumption data after sharding; the abnormality analysis module is used to judge the user's operations outside the normal operating behavior based on the user's electricity consumption curve, and to issue an early warning for abnormal situations; the energy efficiency evaluation module is used to evaluate the user's energy efficiency based on the user's electricity consumption data after sharding; the prediction module is used to predict the future user's electricity demand based on the user's electricity consumption data after sharding, weather and holiday time; the energy-saving suggestion module is used to provide energy efficiency optimization suggestions based on future user electricity demand.

[0010] Furthermore, the step of obtaining the user's daily electricity consumption curve includes: extracting effective electricity consumption stage data through the current data in the user's electricity consumption data, and fitting the power consumption and power factor to obtain the power curve respectively, and interpolating the power curve to obtain the electricity consumption curve. The electricity consumption stage includes the standby stage, the cold loading stage, the voltage stabilization stage and the heat dissipation stage.

[0011] Furthermore, the step of identifying the working day power consumption curve in the power consumption curve includes: performing an initial division according to the rising and falling characteristics of the power consumption curve, and then using a clustering module to further divide the rising curve, falling curve and horizontal curve obtained by the division according to the current size, and finally obtaining the working day power consumption curve by comprehensively considering the time distribution, the rising and falling ratio and the current size; the categories of the working day power consumption curve include the first category, the second category, the third category, the fourth category, the fifth category and the sixth category; among which, the first category is the curve of the meter in stable operation; the second category is the standby curve; the third category is the first category The superposition of curves, when there are more than two first-category curves, take the average value to obtain the third-category electricity consumption curve; the fourth category is the superposition of the second-category curves, when there are more than two second-category curves, take the average value to obtain the fourth-category electricity consumption curve; the fifth category is the superposition of the third-category electricity consumption curve and the other-category electricity consumption curves, take the average value to obtain the fifth-category electricity consumption curve; the sixth category is the superposition of the fourth-category electricity consumption curve and the other-category electricity consumption curves, take the average value to obtain the sixth-category electricity consumption curve; the method for identifying the weekend electricity consumption curve and the holiday electricity consumption curve in the electricity consumption curve is the same as the method for identifying the weekday electricity consumption curve.

[0012] Furthermore, the step of identifying the working day electricity consumption curve in the electricity consumption curve also includes the step of identifying abnormal user operations: using the current detection, meter address detection and time detection in the abnormal analysis module to identify whether an operation occurs outside the normal operating behavior, whether the operation occurs outside the normal operating time of the user, and whether the meter address changes when the meter address remains unchanged. When an operation occurs outside the normal operating behavior, or an operation occurs outside the normal operating time of the user, or the meter address changes when the meter address remains unchanged, it is determined that the user has performed an abnormal operation.

[0013] Furthermore, the energy efficiency evaluation module evaluates the energy efficiency of users by indicators including electric energy efficiency indicators, power grid energy efficiency indicators and environmental energy efficiency indicators; the electric energy efficiency indicators include unit electric energy consumption and year-on-year energy efficiency comparison indicators; the unit electric energy consumption is the value obtained by dividing the total power consumption of users in the unit by the number of people in the unit, and the unit is kilowatts per person; the year-on-year energy efficiency comparison indicator is the value obtained by comparing the unit electric energy consumption of users in the unit with the maximum value of the unit electric energy consumption in the same period last year, and its value range is 0-1. When the value is 0, it indicates that the electric energy efficiency is at the lowest level, and when the value is 1, it indicates that the electric energy efficiency is at the highest level; the power grid energy efficiency indicators include meter energy efficiency comparison and meter energy efficiency change; the meter energy efficiency comparison is the comparison of the number of meters in the unit with the number of meters in other units, with the maximum value of the number of meters in other units as the benchmark value; the meter energy efficiency change is the difference between the meter energy efficiency indicator on the current date and the same date last year The energy efficiency index of the electric meter is compared with the energy efficiency index of the current date; the value range of the energy efficiency index of the electric meter is 0-1. When the value is 0, it means that the energy efficiency of electricity consumption is at the lowest level, and when the value is 1, it means that the energy efficiency of electricity consumption is at the highest level; the environmental energy efficiency index includes environmental energy consumption, environmental unit energy efficiency comparison and environmental energy efficiency change; the environmental energy consumption is the energy consumption in the unit, and the energy consumption is the carbon dioxide emissions generated by the unit electricity consumption; the environmental unit energy efficiency comparison is the comparison of the environmental energy consumption in the unit with the environmental energy consumption in other units, and the maximum value of the environmental energy consumption in other units is taken as the benchmark value; the environmental energy efficiency change is the comparison of the environmental energy efficiency index of the current date with the environmental energy efficiency index of the same date last year; the value range of the environmental energy efficiency index is 0-1. When the value is 0, it means that the energy efficiency of electricity consumption is at the lowest level, and when the value is 1, it means that the energy efficiency of electricity consumption is at the highest level.

[0014] Furthermore, the prediction module's prediction indicators for future user electricity demand include electricity forecast and power grid forecast; the electricity forecast includes electricity forecast value and electricity forecast correction value, both in kilowatts; the electricity forecast value is the value obtained by calculating the deviation and weight of the unit electricity consumption of the current user's electricity meter on the corresponding date and the unit electricity consumption on the same date as the same period last year; wherein the deviation is the percentage of the difference between the unit electricity consumption on the current date and the unit electricity consumption on the same date as the same period last year in the unit electricity consumption of the same period last year, calculated as (unit electricity consumption on the current date - unit electricity consumption on the same date as the same period last year) / unit electricity consumption on the same period last year; the weight is a comparison table between the current date and holiday time, which is 1 for a holiday date and 0 for other dates; the power grid forecast includes power grid forecast value and power grid forecast correction value, both in kilowatts; the power grid forecast value is the value obtained by predicting and correcting using the same method.

[0015] Furthermore, the energy efficiency optimization suggestions in the energy-saving suggestion module include power-saving suggestions; the power-saving suggestions calculate the power-saving potential by using the user's unit power consumption on the current date and the unit power consumption in the same period last year, and the calculation method is: unit power consumption + the rising amplitude of the power consumption curve + the falling amplitude of the power consumption curve + the voltage stabilization amplitude of the power consumption curve; wherein, the rising amplitude of the power consumption curve is the distance from the highest point in the rising curve to the starting point of the rising curve; the falling amplitude of the power consumption curve is the distance from the highest point in the falling curve to the starting point of the falling curve; the voltage stabilization amplitude of the power consumption curve is the sum of the current distance and the starting distance of the horizontal power consumption curve; the power-saving potential is sorted to obtain energy efficiency optimization suggestions.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] (1) By integrating smart meters, transmission networks, and back-end platforms, an integrated process from data collection to analysis and application is achieved. This design not only improves the accuracy and efficiency of data collection, but also provides users with more personalized and accurate services through the powerful data processing and analysis capabilities of the back-end. It also provides important decision-making support tools for power companies, helping to optimize power resource allocation and improve service quality.

[0018] (2) The system uses data cleaning steps to ensure the integrity and standardization of the data, which is crucial for subsequent data analysis. By removing duplicate, missing or erroneous data, the accuracy of data analysis can be significantly improved, and erroneous conclusions caused by data quality issues can be avoided, thereby improving the overall performance of the system and user satisfaction.

[0019] (3) The system processes user electricity consumption data in slices according to meter address and time, and splits and merges data of the same slice and the same date, which not only improves the efficiency of data storage but also facilitates subsequent data query and analysis. The columnar storage method further improves data access speed and enhances the system's responsiveness.

[0020] (4) The multifunctional design of the data analysis engine enables the system to comprehensively analyze the user's electricity usage behavior. By combining functions such as electricity curve recognition, anomaly analysis, energy efficiency evaluation, electricity demand forecasting, and energy-saving suggestions, it can not only discover the user's electricity usage patterns and potential problems, but also provide specific solutions to help users achieve the goal of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a system schematic diagram of the present invention. DETAILED DESCRIPTION

[0022] The present invention will be further described below in conjunction with the accompanying drawings.

[0023] like Figure 1 As shown, this embodiment provides a data analysis system for smart meters, the system including smart meters, transmission networks and back-end platforms; the smart meters are used to obtain users' electricity usage data, the electricity usage data including electricity usage, power factor, voltage, current, meter address or time and other data; the transmission network includes a collector and a concentrator, which are used to collect data from smart meters and transmit them to the concentrator; the concentrator is used to forward users' electricity usage data to the back-end platform; the back-end platform includes a data processing center, a data analysis engine and an application platform; the data processing center is used to receive users' electricity usage data and clean, integrate and store the users' electricity usage data; the data analysis engine is used to conduct in-depth mining of users' electricity usage patterns through data analysis and identify potential energy-saving opportunities; the application platform is used to present energy-saving suggestions to users or display the operation status of the power grid.

[0024] Smart meters are used to monitor residents' electricity consumption in real time, and big data analysis technology is used to conduct in-depth mining of massive user electricity consumption data to provide energy-saving strategies.

[0025] Data cleaning includes consistency verification of user electricity consumption data: removing, supplementing or standardizing duplicate, missing or incorrectly formatted data to ensure the integrity and standardization of the data.

[0026] The steps of cleaning, integrating and storing user electricity consumption data include: dividing the user electricity consumption data into date slices according to the meter address and time, splitting and merging the user electricity consumption data of the same slice and the same date, the splitting and merging includes time matching, meter address matching and electricity consumption matching, and storing the split and merged electricity consumption data in the database in column format.

[0027] Data cleaning and integration is mainly in the splitting and merging, mainly splitting and merging the data of the same piece and the same date. Since it is carried out according to the time period, there are a large number of missing values ​​in the electricity consumption data, and some missing values ​​need to be filled. The method for missing values ​​is: if the missing value is less than 10%, directly delete the column where the missing value is located; if the missing value is greater than 10% and less than 16%, interpolate and supplement the missing value; if the missing value is greater than 16%, the data of that date will be discarded.

[0028] The data analysis engine includes an electricity consumption curve recognition device, an abnormal analysis module, an energy efficiency evaluation module, a prediction module and an energy-saving suggestion module; the electricity consumption curve recognition device is used to obtain the user's daily electricity consumption curve and identify the weekday electricity consumption curve, weekend electricity consumption curve and holiday electricity consumption curve in the electricity consumption curve based on the electricity consumption information in the user's electricity consumption data after sharding; the abnormal analysis module is used to judge the user's operations outside the normal operation behavior based on the user's electricity consumption curve, and to issue an early warning for abnormal situations; the energy efficiency evaluation module is used to evaluate the user's energy efficiency based on the user's electricity consumption data after sharding; the prediction module is used to predict the future user's electricity demand based on the user's electricity consumption data after sharding, weather and holiday time; the energy-saving suggestion module is used to provide energy efficiency optimization suggestions based on future user electricity demand.

[0029] The steps of obtaining the user's daily electricity consumption curve include: extracting effective electricity consumption stage data through the current data in the user's electricity consumption data, fitting the power consumption and power factor to obtain the power curve respectively, and interpolating the power curve to obtain the electricity consumption curve. The electricity consumption stage includes the standby stage, cold loading stage, voltage stabilization stage and heat dissipation stage.

[0030] The step of identifying the working day power consumption curve in the power consumption curve includes: performing initial division according to the rising and falling characteristics of the power consumption curve, and then using the clustering module to further divide the rising curve, the falling curve and the horizontal curve obtained by the division according to the current size, and finally classifying the working day power consumption curve by comprehensively considering the time distribution, the rising and falling ratio and the current size; the categories of the working day power consumption curve include the first category, the second category, the third category, the fourth category, the fifth category and the sixth category; among them, the first category is the curve of the meter in stable operation; the second category is the standby curve; the third category is the first category curve The fourth category is the superposition of the second category curves. When there are more than two first category curves, the average value is taken to obtain the third category electricity consumption curve; the fourth category is the superposition of the second category curves. When there are more than two second category curves, the average value is taken to obtain the fourth category electricity consumption curve; the fifth category is the superposition of the third category electricity consumption curve and other categories of electricity consumption curves, the average value is taken to obtain the fifth category electricity consumption curve; the sixth category is the superposition of the fourth category electricity consumption curve and other categories of electricity consumption curves, the average value is taken to obtain the sixth category electricity consumption curve; the method for identifying the weekend electricity consumption curve and the holiday electricity consumption curve in the electricity consumption curve is the same as the method for identifying the weekday electricity consumption curve.

[0031] The step of identifying the working day electricity consumption curve in the electricity consumption curve also includes the step of identifying abnormal user operations: using the current detection, meter address detection and time detection in the abnormal analysis module to identify whether an operation occurs outside the normal operation behavior, whether the operation occurs outside the normal operating time of the user, and whether the meter address changes when the meter address remains unchanged. When an operation occurs outside the normal operation behavior, or an operation occurs outside the normal operating time of the user, or the meter address changes when the meter address remains unchanged, it is determined that the user has performed an abnormal operation.

[0032] The energy efficiency evaluation module evaluates the energy efficiency of users by indicators including electric energy efficiency indicators, power grid energy efficiency indicators and environmental energy efficiency indicators; electric energy efficiency indicators include unit electric energy consumption and year-on-year energy efficiency comparison indicators; unit electric energy consumption is the value obtained by dividing the total power consumption of users in the unit by the number of people in the unit, and the unit is kilowatts per person; the year-on-year energy efficiency comparison indicator is the value obtained by comparing the unit electric energy consumption of users in the unit with the maximum value of the unit electric energy consumption in the same period last year, and its value range is 0-1. When the value is 0, it means that the electric energy efficiency is at the lowest level, and when the value is 1, it means that the electric energy efficiency is at the highest level; power grid energy efficiency indicators include meter energy efficiency comparison and meter energy efficiency change; meter energy efficiency comparison is the comparison of the number of meters in the unit with the number of meters in other units, with the maximum value of the number of meters in other units as the benchmark value; meter energy efficiency change is the difference between the meter energy efficiency indicator on the current date and the same period last year The value range of the energy efficiency index of the meter is 0-1. When the value is 0, it means that the energy efficiency of electricity consumption is at the lowest level, and when the value is 1, it means that the energy efficiency of electricity consumption is at the highest level. The environmental energy efficiency index includes environmental energy consumption, environmental unit energy efficiency comparison and environmental energy efficiency change. The environmental energy consumption is the energy consumption in the unit, and the energy consumption is the carbon dioxide emissions generated by the unit electricity consumption. The environmental unit energy efficiency comparison is the comparison of the environmental energy consumption in the unit with the environmental energy consumption in other units, and the maximum value of the environmental energy consumption in other units is taken as the benchmark value. The environmental energy efficiency change is the comparison of the environmental energy efficiency index of the current date with the environmental energy efficiency index of the same period last year. The value range of the environmental energy efficiency index is 0-1. When the value is 0, it means that the energy efficiency of electricity consumption is at the lowest level, and when the value is 1, it means that the energy efficiency of electricity consumption is at the highest level.

[0033] The prediction module's prediction indicators for future user electricity demand include electricity forecast and power grid forecast; the electricity forecast includes electricity forecast value and electricity forecast correction value, in kilowatts; the electricity forecast value is the value obtained by calculating the deviation and weight of the unit electricity consumption of the current user's electricity meter on the corresponding date and the unit electricity consumption on the same date as the same period last year; among them, the deviation is the percentage of the difference between the unit electricity consumption on the current date and the unit electricity consumption on the same period last year in the unit electricity consumption of the same period last year, and the calculation method is (unit electricity consumption on the current date - unit electricity consumption on the same period last year) / unit electricity consumption on the same period last year; the weight is a comparison table between the current date and holiday time, which is 1 when it is a holiday date and 0 for other dates; the power grid forecast includes power grid forecast value and power grid forecast correction value, in kilowatts; the power grid forecast value is the value obtained by predicting and correcting using the same method.

[0034] The energy efficiency optimization suggestions in the energy-saving suggestion module include power-saving suggestions; the power-saving suggestions use the user's unit power consumption on the current date and the unit power consumption in the same period last year to calculate the power-saving potential, and the calculation method is: unit power consumption + the rising amplitude of the power consumption curve + the falling amplitude of the power consumption curve + the voltage stabilization amplitude of the power consumption curve; among which, the rising amplitude of the power consumption curve is the distance from the highest point in the rising curve to the starting point of the rising curve; the falling amplitude of the power consumption curve is the distance from the highest point in the falling curve to the starting point of the falling curve; the voltage stabilization amplitude of the power consumption curve is the sum of the current distance and the starting distance of the horizontal power consumption curve; the power-saving potential is sorted to obtain energy efficiency optimization suggestions.

[0035] The energy efficiency optimization suggestions in the energy-saving suggestion module include energy-saving suggestions; the energy-saving suggestions include the unit power consumption, holiday time information and energy-saving index in the energy efficiency evaluation module; the unit power consumption is the product of the power consumption and the power consumption time of the power consumption curve, in hours; the energy-saving index is calculated as: (maximum unit power consumption-average unit power consumption) / maximum unit power consumption; wherein, the maximum unit power consumption is the largest unit power consumption in the historical data of the day; the average unit power consumption is the average value of the largest unit power consumption in the historical data of the day; the power-saving time is the sum of the rising time and the falling time of the power consumption curve; wherein, the distance from the starting value to the highest point in the rising curve is the power consumption time, and the distance from the highest point to the end value in the falling curve is the power consumption time; wherein, the highest point in the rising curve and the falling curve is the maximum value point in the power consumption curve; the energy-saving suggestions include adjusting the power consumption time, controlling the power consumption of equipment, installing energy-saving equipment, replacing energy-saving meters or replacing energy-saving lamps.

[0036] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A data analysis system for a smart meter, characterized in that: The system includes a smart meter, a transmission network and a backend platform; Smart meters are used to obtain users' electricity consumption data, including electricity consumption, power factor, voltage, current, meter address or time data; The transmission network includes a collector and a concentrator, which is used to collect data from smart meters and transmit it to the concentrator; Concentrator, used to forward user electricity consumption data to the back-end platform; Backend platform, including data processing center, data analysis engine and application platform; The data processing center is used to receive, clean, integrate and store user electricity consumption data; Data analysis engine, which is used to deeply mine user electricity usage patterns through data analysis and identify potential energy-saving opportunities; Application platform, used to present energy-saving suggestions to users or display the operating status of the power grid.

2. A data analysis system for a smart meter according to claim 1, characterized in that: Data cleaning includes consistency verification of user electricity consumption data: removing, supplementing or standardizing duplicate, missing or incorrectly formatted data to ensure the integrity and standardization of the data.

3. A data analysis system for a smart meter according to claim 2, characterized in that: The steps of cleaning, integrating and storing user electricity consumption data include: dividing the user electricity consumption data into date slices according to the meter address and time, splitting and merging the user electricity consumption data of the same slice and the same date, the splitting and merging includes time matching, meter address matching and electricity consumption matching, and storing the split and merged electricity consumption data in the database in column format.

4. A data analysis system for a smart meter according to claim 2, characterized in that: The data analysis engine includes a power consumption curve recognition device, an abnormal analysis module, an energy efficiency evaluation module, a prediction module, and an energy-saving suggestion module; The power consumption curve identification device is used to obtain the user's daily power consumption curve according to the power consumption information in the user's power consumption data after slicing, and to identify the weekday power consumption curve, weekend power consumption curve and holiday power consumption curve in the power consumption curve; The abnormal analysis module is used to judge the user's operation outside the normal operation behavior according to the user's power consumption curve, and issue an early warning for abnormal situations; Energy efficiency evaluation module, used to evaluate the energy efficiency of users based on the sharded user electricity consumption data; The prediction module is used to predict future user electricity demand based on the sharded user electricity consumption data, weather and holiday time; The energy-saving suggestion module is used to provide suggestions for energy efficiency optimization based on future user electricity demand.

5. A data analysis system for a smart meter according to claim 4, characterized in that: The steps of obtaining the user's daily electricity consumption curve include: extracting effective electricity consumption stage data through the current data in the user's electricity consumption data, fitting the power consumption and power factor to obtain the power curve respectively, and interpolating the power curve to obtain the electricity consumption curve. The electricity consumption stage includes the standby stage, cold loading stage, voltage stabilization stage and heat dissipation stage.

6. A data analysis system for a smart meter according to claim 5, characterized in that: The step of identifying the working day power consumption curve in the power consumption curve includes: performing initial division according to the rising and falling characteristics of the power consumption curve, and then further dividing the rising curve, the falling curve and the horizontal curve obtained by the division according to the current size by using the clustering module, and finally obtaining the working day power consumption curve by comprehensively considering the time distribution, the rising and falling ratio and the current size; The categories of weekday electricity consumption curves include the first category, the second category, the third category, the fourth category, the fifth category and the sixth category; Among them, the first category is the curve of the meter in stable operation; The second category is the standby curve; The third category is the superposition of the first category curve. When there are more than two first category curves, the average value is taken to obtain the third category power consumption curve; The fourth category is the superposition of the second category curve. When there are more than two second category curves, the average value is taken to obtain the fourth category electricity consumption curve; The fifth category is the superposition of the third category power consumption curve and the other category power consumption curves, and the average value is taken to obtain the fifth category power consumption curve; The sixth category is the superposition of the fourth category power consumption curve and the other category power consumption curves, and the average value is taken to obtain the sixth category power consumption curve; The method for identifying the weekend power consumption curve and the holiday power consumption curve in the power consumption curve is the same as the method for identifying the weekday power consumption curve.

7. A data analysis system for a smart meter according to claim 6, characterized in that: The step of identifying the working day electricity consumption curve in the electricity consumption curve also includes the step of identifying abnormal user operations: using the current detection, meter address detection and time detection in the abnormal analysis module to identify whether an operation occurs outside the normal operation behavior, whether the operation occurs outside the normal operating time of the user, and whether the meter address changes when the meter address remains unchanged. When an operation occurs outside the normal operation behavior, or an operation occurs outside the normal operating time of the user, or the meter address changes when the meter address remains unchanged, it is determined that the user has performed an abnormal operation.

8. A data analysis system for a smart meter according to claim 7, characterized in that: The energy efficiency evaluation module evaluates the energy efficiency of users using indicators including electric energy efficiency indicators, power grid energy efficiency indicators and environmental energy efficiency indicators; Electric energy efficiency indicators include unit electricity consumption and year-on-year comparison indicators of energy efficiency; The unit power consumption is the value obtained by dividing the total power consumption of users in the unit by the number of people in the unit, and the unit is kilowatt / person; The year-on-year comparison index of energy efficiency is the value obtained by comparing the unit electricity consumption of users in the unit with the maximum unit electricity consumption of the same period last year. Its value range is 0-1. When the value is 0, it means that the electricity energy efficiency is at the lowest level, and when the value is 1, it means that the electricity energy efficiency is at the highest level. Grid energy efficiency indicators include meter energy efficiency comparison and meter energy efficiency change; The energy efficiency comparison of electric meters is to compare the number of electric meters in the unit with the number of electric meters in other units, with the maximum number of electric meters in other units as the benchmark value; The change in energy efficiency of the electric meter is obtained by comparing the energy efficiency index of the electric meter on the current date with the energy efficiency index of the electric meter on the same date last year; The energy efficiency index of the electric meter ranges from 0 to 1. When the value is 0, it means that the energy efficiency of electricity consumption is at the lowest level, and when the value is 1, it means that the energy efficiency of electricity consumption is at the highest level. Environmental energy efficiency indicators include environmental electricity consumption, environmental unit energy efficiency comparison and environmental energy efficiency change; Environmental power consumption is the power consumption in the unit, which is the carbon dioxide emissions generated by the unit's electricity consumption; The environmental unit energy efficiency comparison is to compare the environmental electric energy consumption in the unit with the environmental electric energy consumption in other units, and the maximum environmental electric energy consumption in other units is used as the benchmark value; The change in environmental energy efficiency is obtained by comparing the environmental energy efficiency index of the current date with the environmental energy efficiency index of the same date last year; The environmental energy efficiency index ranges from 0 to 1. When the value is 0, it means that the electricity energy efficiency is at the lowest level, and when the value is 1, it means that the electricity energy efficiency is at the highest level.

9. A data analysis system for a smart meter according to claim 8, characterized in that: The forecasting module predicts the future user electricity demand, including power forecast and power grid forecast; The power forecast includes the power forecast value and the power forecast correction value, both in kilowatts; The power forecast value is the value obtained by calculating the deviation and weight between the unit power consumption of the current user's power meter on the corresponding date and the unit power consumption of the same period last year; The deviation is the percentage of the difference between the unit energy consumption on the current date and the unit energy consumption in the same period last year in the unit energy consumption in the same period last year, and is calculated as (unit energy consumption on the current date - unit energy consumption in the same period last year) / unit energy consumption in the same period last year; The weight is a comparison table between the current date and time and the holiday time. If it is a holiday date, it is 1, and other dates are 0; The power grid forecast includes the power grid forecast value and the power grid forecast correction value, in kilowatts; The power grid prediction value is predicted and corrected using the same method.

10. A data analysis system for a smart meter according to claim 9, characterized in that: The energy efficiency optimization suggestions in the energy saving suggestion module include power saving suggestions; The power saving suggestion uses the user's unit power consumption on the current date and the unit power consumption of the same period last year to calculate the power saving potential. The calculation method is: unit power consumption + the rising range of the power curve + the falling range of the power curve + the voltage stabilization range of the power curve; Among them, the rising amplitude of the electricity consumption curve is the distance from the highest point in the rising curve to the starting point of the rising curve; The decline of the electricity consumption curve is the distance from the highest point in the decline curve to the starting point of the decline curve; The voltage stabilization amplitude of the power consumption curve is the sum of the current distance and the starting distance of the horizontal power consumption curve; Rank the electricity saving potentials and get suggestions for energy efficiency optimization.

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