Control method of low-power-consumption high-precision electric energy meter

Abstract

The invention relates to the field of energy management, in particular to a control method of a low-power-consumption high-precision electric energy meter, which comprises the following steps of: determining an optimal time period by acquiring historical and real-time power consumption data sequences of a user, traversing data lengths in a preset time period, decomposing and extracting characteristic components and calculating a stability characteristic value; and clustering the stability characteristic values of the users in the optimal time period to obtain user classifications of different power consumption types, and marking the users not belonging to the clusters. And calculating the correlation and the abnormal degree value between the real-time power consumption data sequence of the user and the historical data, and dynamically adjusting the data transmission frequency of the communication module of the electric energy meter so as to optimize the energy consumption management and discover the abnormal power consumption behavior in time. The data transmission frequency is dynamically adjusted by calculating the abnormal degree value of the real-time power consumption data of the user, so that abnormal power consumption behaviors are found and processed in time, the response speed and reliability of the system are improved, the energy consumption is reduced, and the service life is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of energy management, and in particular to a control method for a low-power and high-precision electric energy meter. Background Art

[0002] With growing environmental awareness and rising energy costs, businesses and society must implement scientific and rational energy management to reduce costs, enhance competitiveness, and actively fulfill their social responsibilities. The core of energy management lies in optimizing energy efficiency, reducing waste, and achieving refined management of energy consumption through precise data analysis and real-time monitoring. This not only helps businesses reduce operating costs but also promotes green, low-carbon social development.

[0003] In energy management, the control method for electricity meters is particularly important. As the core metering device in the power system, the performance of the electricity meter directly affects the efficiency and accuracy of energy management. The control method for low-power, high-precision electricity meters intelligently and dynamically adjusts the data transmission frequency, reducing device power consumption while maintaining metering accuracy. This is crucial for improving the overall effectiveness of energy management. Through precise measurement and intelligent control, electricity meters can provide reliable data support for energy management, helping enterprises better monitor and optimize energy use and achieve energy conservation and emission reduction goals.

[0004] Traditional electricity meters typically use a fixed data transmission frequency, resulting in high power consumption of the communication module and an inability to dynamically adjust according to user electricity usage behavior. At the same time, their hardware design and data processing capabilities limit metering accuracy and data integrity, which not only affects the operating efficiency of the electricity meter, but also fails to meet the needs of modern energy management and users for accurate metering and real-time monitoring. Summary of the Invention

[0005] To address the problems of traditional electricity meters resulting from high power consumption, insufficient hardware design and data processing capabilities due to a fixed data transmission frequency, and limited measurement accuracy and data integrity, making them unable to meet modern energy management and user needs for precise measurement and real-time monitoring, the present invention provides solutions in the following aspects.

[0006] A control method for a low-power, high-precision electric energy meter comprises: obtaining a historical electric energy data sequence and a real-time electric energy data sequence of each user; traversing the data length of the historical electric energy data sequence of each user within a preset time period, decomposing the electric energy data sequences of different data lengths, extracting characteristic components, and calculating the stability characteristic values ​​of the electric energy data sequences corresponding to different data lengths of each user based on the trend characteristics of each characteristic component, and determining an optimal time period; clustering the stability characteristic values ​​of the optimal time period of each user, obtaining a classification of users with different electric energy types, marking users that do not belong to each cluster, calculating the correlation between the real-time electric energy data sequence and the historical electric energy data sequence of each user belonging to the cluster, and calculating the abnormality degree value of the user's real-time electric energy data sequence in combination with the stability characteristic value of the user's electric energy behavior; and dynamically adjusting the data transmission frequency of the electric energy meter communication module based on the abnormality degree value to optimize energy consumption management and timely detect abnormal electric energy behavior.

[0007] By dynamically adjusting the data transmission frequency of the electricity meter communication module, energy consumption is optimized and the operating power consumption of the electricity meter is reduced. Through cluster analysis and calculation of abnormality values, users' abnormal electricity consumption behavior can be discovered and handled in a timely manner, thereby enhancing the reliability and security of the system and meeting the needs of modern energy management and users for accurate measurement and real-time monitoring.

[0008] Preferably, the calculation method of the stability characteristic value includes: The characteristic components include trend component, periodic component and residual component. The variance of each characteristic component is calculated and summed to obtain the total variance of the electricity consumption data. The regularity ratio is obtained by calculating the ratio between the sum of the variance of the trend component and the variance of the periodic component and the total variance of the electricity consumption data. The ratio between the standard deviation of the residual component and the mean of all historical electricity consumption data series is exponentially mapped using a negative exponential function to obtain the relative intensity of random fluctuations. The product of the regularity ratio and the relative intensity is used as the stability characteristic value of the historical electricity consumption data series.

[0009] Preferably, the calculation method of the abnormality degree value of the real-time electricity consumption data sequence includes: The average similarity between the periodic components of the user's real-time electricity consumption data series and the historical electricity consumption data series after decomposition is calculated; the minimum value between the stability of the user's historical electricity consumption data series and the stability characteristic value corresponding to the center point of the cluster in which it is located is selected, and the product of the difference between 1 and the average similarity and the minimum value is normalized to obtain the abnormality degree value of the user's real-time electricity consumption data series.

[0010] By calculating the average similarity between the periodic components of a user's real-time electricity usage data series and historical electricity usage data series, and combining the stability eigenvalues ​​of the user and the stability eigenvalues ​​of the cluster centers, we can accurately quantify the degree of anomaly in a user's real-time electricity usage behavior. This method not only considers the stability of individual user behavior but also incorporates the stability eigenvalues ​​of the group, improving the accuracy and reliability of anomaly detection.

[0011] Preferably, the dynamically adjusting the data transmission frequency of the electric energy meter communication module includes: According to the magnitude of the abnormality value, the data transmission frequency of the electric energy meter communication module is divided into different gears, and in response to the abnormality value being less than or equal to the preset threshold When the abnormality value is greater than the preset threshold, the data transmission frequency is set to a low frequency; and is less than the preset threshold When the abnormality value is greater than or equal to the preset threshold, the data transmission frequency is set to medium frequency; When , the data transmission frequency is set to high frequency.

[0012] Preferably, the dynamically adjusting the data transmission frequency of the electric energy meter communication module includes: Set the maximum and minimum values ​​of the data transmission interval, calculate the maximum adjustment range of the transmission interval, use the abnormality value as the adjustment factor for adjusting the transmission interval, and use the sum of the adjustment factor and the minimum value of data transmission as the adjusted data transmission interval.

[0013] Preferably, marking users who do not belong to each cluster includes: DBSCAN is used to cluster the stability characteristic values ​​of each user's optimal time period. Based on the clustering results, users who do not belong to any cluster are marked as abnormal users, and the remaining users are marked as normal users; the real-time electricity consumption data of abnormal users is transmitted at a high frequency.

[0014] By using DBSCAN to cluster user stability eigenvalues, we can effectively identify users of different electricity usage types and distinguish between normal and abnormal users. For abnormal users, high-frequency data transmission is used to ensure timely detection and resolution of abnormal electricity usage, thereby improving system reliability and security. Meanwhile, for normal users, lower-frequency data transmission is used to optimize energy consumption management and reduce the operating power consumption of electricity meters. This not only improves the efficiency of anomaly detection, but also optimizes energy consumption and enhances overall system performance.

[0015] Preferably, obtaining the optimal time period includes the steps of: The data length within the preset time period is traversed and selected, the stability characteristic value of the corresponding data length is calculated, and the preset time period corresponding to the largest stability characteristic value is selected as the user's electricity consumption data sequence length.

[0016] The effect is that by traversing different data lengths within a preset time period and calculating the stability characteristic value corresponding to each length, it is possible to accurately determine the time period when the user's electricity usage behavior is most stable. Selecting the time period with the largest stability characteristic value as the length of the user's electricity usage data sequence ensures that the selected data segment best represents the user's typical electricity usage behavior, providing the most reliable data foundation for subsequent electricity usage analysis, anomaly detection, and data transmission frequency adjustment.

[0017] Preferably, the historical electricity consumption data series and the real-time electricity consumption data series of each user are preprocessed, and the preprocessing step includes: The linear interpolation method is used to calculate the slope between the nearest valid data points on both sides of the missing point. The slope and the coordinates of the data points are used to estimate the value of the missing point, fill the missing values ​​in the data, and verify the rationality of the filling results to make the data smooth and consistent with the overall trend. The supplemented data is normalized to ensure data integrity.

[0018] The present invention has the following effects: 1. This invention can promptly detect abnormal user electricity usage by calculating the abnormality level of a user's real-time electricity usage data series. High-frequency data transmission of abnormal user real-time electricity usage data ensures that abnormal situations can be detected and handled promptly, avoiding potential losses caused by abnormal electricity usage. This not only improves the system's response speed but also enhances its reliability and security.

[0019] 2. This invention adaptively adjusts the data transmission frequency based on the user's real-time abnormality level. When the user's electricity usage is stable, the data transmission frequency is reduced to reduce energy consumption. When the user's electricity usage is abnormal, the data transmission frequency is increased to promptly detect and address the abnormality, significantly reducing the operating energy consumption of the electricity meter and extending the service life of the device. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a method flow chart of steps S1 to S4 in a control method for a low-power and high-precision electric energy meter according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0022] Reference Figure 1 A method for controlling a low-power and high-precision electric energy meter includes steps S1 to S4, specifically as follows: S1: Obtain the historical electricity consumption data series and real-time electricity consumption data series of each user.

[0023] It should be noted that the historical electricity consumption data series and the real-time electricity consumption data series of the user within a preset time period are obtained at fixed time intervals.

[0024] Preprocessing is performed on the historical electricity consumption data series and the real-time electricity consumption data series of each user, wherein the preprocessing step includes: Use the linear interpolation method to calculate the slope between the nearest valid data points on both sides of the missing point, use the slope and the coordinates of the data point to estimate the value of the missing point, fill the missing values ​​in the data, and verify the rationality of the filling result to make the data smooth and consistent with the overall trend, ensuring the integrity of the data.

[0025] It's important to note that when adjusting the data transmission frequency of the energy meter communication module, it's important to consider the user's electricity usage patterns and real-time data characteristics. While user electricity usage is dynamic and unfixed, it does exhibit a certain degree of regularity. If a user's electricity usage is highly regular and predictable, their behavior is relatively stable. In this case, the data transmission frequency of the energy meter communication module can be appropriately reduced to reduce energy consumption.

[0026] S2: Traverse the data length of each user's historical electricity consumption data series within a preset time period, decompose the electricity consumption data series of different data lengths, extract characteristic components, and calculate the stability characteristic value of the electricity consumption data series corresponding to different data lengths of each user based on the trend characteristics of each characteristic component to determine the optimal time period.

[0027] The calculation methods of stability characteristic values ​​include: Among them, the characteristic components include: trend component, periodic component and residual component. The variance of each characteristic component is calculated and summed to obtain the total variance of the electricity consumption data. The regularity ratio is obtained by calculating the ratio between the sum of the variance of the trend component and the variance of the periodic component and the total variance of the electricity consumption data. The ratio between the standard deviation of the residual component and the mean of all historical electricity consumption data series is exponentially mapped using a negative exponential function to obtain the relative intensity of random fluctuations. The product of the regularity ratio and the relative intensity is used as the stability characteristic value of the historical electricity consumption data series.

[0028] It should be noted that the historical electricity consumption data series is decomposed using STL (Seasonal and Trend decomposition using Loess) to obtain trend, cycle, and residual components. To quantify the stability of user electricity consumption behavior, features are extracted from these components. The trend component reflects long-term load level changes, the cycle component reflects fixed periodic fluctuations, and the residual component reflects sudden fluctuations (such as abnormal behavior or noise).

[0029] Specifically, the stability characteristic value satisfies the following relationship: ; Where, represents the stability characteristic value of the historical electricity consumption data series, represents the variance of the trend component, represents the variance of the periodic component, represents the total variance of electricity consumption data, represents the standard deviation of the residual component, Represents the mean of the historical electricity consumption data series.

[0030] By using STL decomposition and calculating the sum of the variances of the trend and periodic components, we can quantify the regularities in electricity consumption data. The trend component reflects long-term changes, while the periodic component reflects periodic changes. Together, these two components constitute the main regular characteristics of electricity consumption data.

[0031] The ratio of the sum of the variances of the trend component and the periodic component to the total variance can be used to evaluate the stability of electricity consumption behavior. If the proportion is relatively large, it means that the electricity consumption behavior is relatively stable. Conversely, if the residual proportion is relatively large, it means that the electricity consumption behavior is relatively unstable. Among them, the trend component and the periodic component are both regular parts, while the residual component is the random fluctuation part.

[0032] It should be noted that the larger the stability characteristic value, the more regular the user's electricity consumption behavior is. If the real-time electricity consumption data sequence is highly similar to the historical electricity consumption data sequence, the data transmission efficiency of the electricity meter communication module can be reduced to reduce energy consumption; the smaller the stability characteristic value, the data transmission frequency of the electricity meter communication module cannot be adjusted too low, to avoid the failure to transmit data in time when abnormal conditions occur, affecting the user's electricity safety.

[0033] The data length within the preset time period is traversed and selected, the stability characteristic value of the corresponding data length is calculated, and the preset time period corresponding to the largest stability characteristic value is selected as the user's electricity consumption data sequence length.

[0034] For example, a preset time period ranges from 3 to 30 days, with the time period length increasing daily. The stability characteristic value within each time period is calculated. When the stability characteristic value reaches a maximum, the optimal time period for the user's electricity usage is determined, indicating that within the optimal time period, the user's electricity usage behavior is the most stable, with the highest regularity and predictability.

[0035] S3: Cluster the stability characteristic values ​​of each user's optimal time period to obtain user classifications of different electricity usage types, mark users that do not belong to each cluster, calculate the correlation between the real-time electricity usage data series and the historical electricity usage data series of each user belonging to the cluster, and calculate the abnormality degree value of the user's real-time electricity usage data series based on the stability characteristic values ​​of the user's electricity usage behavior.

[0036] DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to cluster the stability characteristic values ​​of each user's optimal time period. Based on the clustering results, users who do not belong to any cluster are marked as abnormal users, and the remaining users are marked as normal users. The real-time electricity consumption data of abnormal users is transmitted at a high frequency.

[0037] For example, the clustering radius of the DBSCAN algorithm is 0.1, and the minimum number of points is set to 3, which can be adjusted according to specific circumstances.

[0038] The calculation method of the abnormality value of the real-time electricity consumption data series includes: The average similarity between the periodic components of the user's real-time electricity consumption data series and the historical electricity consumption data series after decomposition is calculated; the minimum value between the stability of the user's historical electricity consumption data series and the stability characteristic value corresponding to the center point of the cluster in which it is located is selected, and the product of the difference between 1 and the average similarity and the minimum value is normalized to obtain the abnormality degree value of the user's real-time electricity consumption data series.

[0039] Analyzing the similarity between a user's real-time electricity usage data series and historical electricity usage data series is intended to quantify the degree of anomaly in a user's real-time electricity usage behavior. By comparing the electricity usage data for all corresponding time periods in the periodic components derived from the STL decomposition of the real-time electricity usage series with the historical electricity usage series, we can determine whether the real-time electricity usage behavior conforms to the user's regular pattern. Historical electricity usage data series provide a rich comparison benchmark, which can improve the accuracy of anomaly detection. The periodic component reflects the cyclical pattern of user electricity usage behavior. By comparing it with the periodic component, we can more accurately identify abnormal changes in cyclical electricity usage patterns.

[0040] Specifically, the abnormality degree value satisfies the following relationship: ; Where, Indicates the abnormality value of the user's real-time electricity consumption data series, Represents the user's real-time electricity consumption data series The periodic component of the historical electricity consumption data series in the preset period The average similarity of all corresponding time period electricity consumption data, Indicates the The stability characteristic value of the historical electricity consumption data of each user, Indicates the The center point of the cluster where the user is located corresponds to the stability eigenvalues, represents the minimum function, Represents the normalization function.

[0041] It should be noted that the range of the mean value of the electricity consumption data similarity is , The value range is , The value of can measure the consistency of the real-time series and the historical period components in the same time period, ensuring that the abnormality value must be greater than 0, and It is negatively correlated with the abnormality value. The minimum value between the stability characteristic value of the user's historical electricity consumption data and the stability characteristic value of the corresponding cluster center is used to reflect the similarity value. When quantifying the degree of abnormality of a user's real-time electricity usage behavior, the larger the confidence value, the more stable the user's electricity usage behavior, and the higher the reliability of the similarity between the periodic components of the real-time electricity usage data and the historical electricity usage data for calculating the degree of abnormality of the user's real-time electricity usage data.

[0042] By calculating the similarity between a user's real-time electricity usage data series and their historical data series, and combining this with the stability characteristic value of the user's electricity usage behavior, a normalized anomaly level is calculated. A higher anomaly level indicates a more abnormal behavior corresponding to the user's real-time electricity usage data series, necessitating appropriate measures (such as increasing the data transmission frequency) to ensure electricity safety. This is because acquiring continuous electricity usage data at a high frequency allows for more accurate analysis of abnormal user behavior, which is closely related to the function of the electricity meter. The electricity meter's communication module is responsible for data transmission, and its energy consumption directly impacts the meter's overall power consumption. By dynamically adjusting the data transmission frequency, energy consumption management can be optimized while ensuring anomaly detection efficiency, achieving an optimal balance between anomaly detection and power consumption management.

[0043] S4: Dynamically adjust the data transmission frequency of the electricity meter communication module based on the abnormality level value to optimize energy consumption management and promptly detect abnormal electricity consumption behavior.

[0044] The method of determining the transmission frequency based on the abnormality degree value includes the following steps: According to the magnitude of the abnormality value, the data transmission frequency of the electric energy meter communication module is divided into different gears, and in response to the abnormality value being less than or equal to the preset threshold When the abnormality value is greater than the preset threshold, the data transmission frequency is set to a low frequency; and is less than the preset threshold When the abnormality value is greater than or equal to the preset threshold, the data transmission frequency is set to medium frequency; When , the data transmission frequency is set to high frequency.

[0045] According to the abnormal degree of the user's real-time electricity consumption behavior, the data transmission frequency of the energy meter communication module is dynamically adjusted. Preset threshold , preset threshold Specifically, when the abnormality level value does not exceed 0.4, the data transmission frequency is set to the first level, for example, once every 30 minutes; when the abnormality level value exceeds 0.4 but is lower than 0.7, it is set to the second level, such as once every 10 minutes; when the abnormality level value is not lower than 0.7, it is set to the third level, such as once every 2 minutes.

[0046] In addition, in another embodiment, by setting the maximum and minimum values ​​of the data transmission interval, the maximum adjustment range of the transmission interval is calculated, the abnormality degree value is used as an adjustment factor for adjusting the transmission interval, and the sum of the adjustment factor and the minimum value of the data transmission is used as the adjusted data transmission interval.

[0047] Specifically, the data transmission frequency of the electric energy meter communication module is dynamically adjusted to satisfy the following relationship: ; Where, Indicates the adjusted data transmission interval, Indicates the minimum value of the data transmission interval, Indicates the abnormality value of the user's real-time electricity consumption data series, Indicates the maximum value of the data transmission interval.

[0048] and They are the value ranges set for the data transmission interval of the electric energy meter communication module, such as That is, transmission is done once every 30 minutes. That is, the data is transmitted once every 2 minutes. The transmission interval setting range is a hyperparameter and can be selected according to actual conditions.

[0049] According to the calculated data transmission interval , and transmits the user's real-time electricity usage data at this interval. This ensures that when the user's electricity usage behavior is abnormal, the abnormal situation can be discovered and handled in time. At the same time, when the user's electricity usage behavior is stable, the data transmission frequency is reduced to save energy.

[0050] It should be noted that those skilled in the art may make various modifications and improvements without departing from the scope of the present invention, and these modifications and improvements fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be based on the appended claims.

Claims

1. A control method for a low-power and high-precision electric energy meter, characterized in that: include: Obtain historical electricity consumption data series and real-time electricity consumption data series for each user; Traverse the data length of each user's historical electricity consumption data series within a preset time period, decompose the electricity consumption data series of different data lengths, extract characteristic components, and calculate the stability characteristic value of the electricity consumption data series corresponding to different data lengths of each user based on the trend characteristics of each characteristic component to determine the optimal time period; Cluster the stability characteristic values ​​of each user's optimal time period to obtain user classifications of different electricity usage types, mark users that do not belong to each cluster, calculate the correlation between the real-time electricity usage data series of each user belonging to the cluster and the historical electricity usage data series, and calculate the abnormality value of the user's real-time electricity usage data series based on the stability characteristic values ​​of the user's electricity usage behavior; The data transmission frequency of the electricity meter communication module is dynamically adjusted based on the abnormality level to optimize energy consumption management and promptly detect abnormal electricity consumption behavior.

2. The control method of a low-power consumption and high-precision electric energy meter according to claim 1, characterized in that: The calculation method of the stability characteristic value includes: The characteristic components include trend component, periodic component and residual component. The variance of each characteristic component is calculated and summed to obtain the total variance of the electricity consumption data. The regularity ratio is obtained by calculating the ratio between the sum of the variance of the trend component and the variance of the periodic component and the total variance of the electricity consumption data. The ratio between the standard deviation of the residual component and the mean of all historical electricity consumption data series is exponentially mapped using a negative exponential function to obtain the relative intensity of random fluctuations. The product of the regularity ratio and the relative intensity is used as the stability characteristic value of the historical electricity consumption data series.

3. The control method of a low-power consumption and high-precision electric energy meter according to claim 1, characterized in that: The calculation method of the abnormality degree value of the real-time electricity consumption data sequence includes: The average similarity between the periodic components of the user's real-time electricity consumption data series and the historical electricity consumption data series after decomposition is calculated; the minimum value between the stability of the user's historical electricity consumption data series and the stability characteristic value corresponding to the center point of the cluster in which it is located is selected, and the product of the difference between 1 and the average similarity and the minimum value is normalized to obtain the abnormality degree value of the user's real-time electricity consumption data series.

4. The control method of a low-power consumption and high-precision electric energy meter according to claim 1, characterized in that: The dynamically adjusting the data transmission frequency of the electric energy meter communication module includes: According to the magnitude of the abnormality value, the data transmission frequency of the electric energy meter communication module is divided into different gears, and in response to the abnormality value being less than or equal to the preset threshold When the abnormality value is greater than the preset threshold, the data transmission frequency is set to a low frequency; and is less than the preset threshold When the abnormality value is greater than or equal to the preset threshold, the data transmission frequency is set to medium frequency; When , the data transmission frequency is set to high frequency.

5. The control method of a low-power and high-precision electric energy meter according to claim 1, characterized in that: The dynamically adjusting the data transmission frequency of the electric energy meter communication module includes: Set the maximum and minimum values ​​of the data transmission interval, calculate the maximum adjustment range of the transmission interval, use the abnormality value as the adjustment factor for adjusting the transmission interval, and use the sum of the adjustment factor and the minimum value of data transmission as the adjusted data transmission interval.

6. The control method of a low-power consumption and high-precision electric energy meter according to claim 1, characterized in that: The marking of users who do not belong to each cluster includes: DBSCAN is used to cluster the stability characteristic values ​​of each user's optimal time period. Based on the clustering results, users who do not belong to any cluster are marked as abnormal users, and the remaining users are marked as normal users; the real-time electricity consumption data of abnormal users is transmitted at a high frequency.

7. The control method of a low-power and high-precision electric energy meter according to claim 1, characterized in that: Obtaining the optimal time period includes the following steps: The data length within the preset time period is traversed and selected, the stability characteristic value of the corresponding data length is calculated, and the preset time period corresponding to the largest stability characteristic value is selected as the user's electricity consumption data sequence length.

8. The control method of a low-power consumption and high-precision electric energy meter according to claim 1, characterized in that: Preprocessing the historical electricity consumption data series and the real-time electricity consumption data series of each user, the preprocessing step includes: The linear interpolation method is used to calculate the slope between the nearest valid data points on both sides of the missing point. The slope and the coordinates of the data points are used to estimate the value of the missing point, fill the missing values ​​in the data, and verify the rationality of the filling results to make the data smooth and consistent with the overall trend. The supplemented data is normalized to ensure data integrity.

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