A control method of a low-power-consumption high-precision electric energy meter

Abstract

This invention relates to the field of energy management, and more particularly to a control method for a low-power, high-precision electricity meter. The method includes: acquiring historical and real-time electricity consumption data sequences of users; traversing the data length within a preset time period; decomposing and extracting feature components; calculating stability feature values ​​to determine the optimal time period; clustering the stability feature values ​​of each user's optimal time period to obtain user classifications for different electricity consumption types; and marking users not belonging to any cluster. The method also calculates the correlation and anomaly degree values ​​between the user's real-time electricity consumption data sequence and historical data, and dynamically adjusts the data transmission frequency of the electricity meter's communication module to optimize energy consumption management and promptly detect abnormal electricity consumption behavior. This invention, by calculating the anomaly degree values ​​of users' real-time electricity consumption data and dynamically adjusting the data transmission frequency, can promptly detect and handle abnormal electricity consumption behavior, improve system response speed and reliability, reduce energy consumption, and extend service life.

Description

Technical Field

[0001] This invention relates to the field of energy management. In particular, it relates to a control method for a low-power, high-precision energy meter. Background Technology

[0002] With increasing environmental awareness and rising energy costs, businesses and society must reduce costs, enhance competitiveness, and actively fulfill their social responsibilities through scientific and rational energy management. 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 a greener and lower-carbon development of society.

[0003] In energy management, the control methods for electricity meters are particularly important. As the core metering device in the power system, the performance of electricity meters directly affects the efficiency and accuracy of energy management. Low-power, high-precision electricity meter control methods intelligently and dynamically adjust the data transmission frequency, reducing device power consumption while ensuring metering accuracy. This is crucial for improving the overall effectiveness of energy management. Through precise metering 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. This makes it impossible to dynamically adjust the frequency based on user electricity consumption behavior. Furthermore, 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 high power consumption, insufficient hardware design and data processing capabilities, and limited metering accuracy and data integrity caused by the fixed data transmission frequency of traditional electricity meters, which prevent them from meeting the needs of modern energy management and users for accurate metering and real-time monitoring, this invention provides solutions in the following aspects.

[0006] A control method for a low-power, high-precision electricity meter includes: acquiring historical and real-time electricity consumption data sequences for each user; traversing the data length of each user's historical electricity consumption data sequence within a preset time period, decomposing the electricity consumption data sequences of different lengths, extracting feature components, and calculating the stability feature values ​​of the electricity consumption data sequences corresponding to different data lengths for each user based on each feature component, and determining the optimal time period, wherein the feature components include: trend components, periodic components, and residual components; clustering the stability feature values ​​of each user's optimal time period to obtain user classifications for different electricity consumption types, and marking users who do not belong to each cluster; calculating the correlation between the real-time electricity consumption data sequence and the historical electricity consumption data sequence for each user belonging to a cluster, and calculating the anomaly degree value of the user's real-time electricity consumption data sequence based on the stability feature value of the user's electricity consumption behavior; and dynamically adjusting the data transmission frequency of the electricity meter's communication module based on the anomaly degree value to optimize energy consumption management and promptly detect abnormal electricity consumption behavior.

[0007] By dynamically adjusting the data transmission frequency of the electricity meter's communication module, energy consumption optimization management is achieved, reducing the electricity meter's operating power consumption. Through cluster analysis and anomaly calculation, abnormal electricity consumption behavior of users can be detected and handled in a timely manner, enhancing the system's reliability and security, and meeting the needs of modern energy management and users for accurate metering and real-time monitoring.

[0008] Preferably, the method for calculating the stability eigenvalue includes:

[0009] The variances of each characteristic component are calculated and summed to obtain the total variance of the electricity consumption data. The regularity proportion is obtained by calculating the ratio between the sum of the variances of the trend component and the periodic component and the total variance of the electricity consumption data. The relative intensity of random fluctuations is obtained by using a negative exponential function to map the ratio between the standard deviation of the residual component and the mean of all historical electricity consumption data sequences. The product of the regularity proportion and the relative intensity is used as the stability characteristic value of the historical electricity consumption data sequence.

[0010] Preferably, the method for calculating the anomaly level of the real-time electricity consumption data sequence includes:

[0011] Calculate the average similarity between the user's real-time electricity consumption data sequence and the periodic components of the historical electricity consumption data sequence after decomposition; select the minimum value between the stability feature value of the user's historical electricity consumption data sequence and the stability feature value corresponding to the center point of the cluster, and normalize the product between the difference of 1 minus the average similarity and the minimum value to obtain the anomaly value of the user's real-time electricity consumption data sequence.

[0012] By calculating the average similarity between the periodic components of a user's real-time electricity consumption data sequence and historical electricity consumption data sequences, and combining this with the user's stability feature value and the stability feature value of the cluster centroid, the degree of anomaly in a user's real-time electricity consumption behavior can be accurately quantified. This approach considers not only the stability of individual user behavior but also the stability feature value of the group, improving the accuracy and reliability of anomaly detection.

[0013] Preferably, the dynamic adjustment of the data transmission frequency of the energy meter communication module includes:

[0014] Based on the magnitude of the anomaly, the data transmission frequency of the electricity meter's communication module is divided into different levels, responding to an anomaly value less than or equal to a preset threshold. When the anomaly level exceeds a preset threshold, the data transmission frequency is set to a low frequency; And less than the preset threshold When the abnormality level is greater than or equal to a preset threshold, the data transmission frequency is set to a medium frequency; When this is the case, the data transmission frequency is set to a high frequency.

[0015] Preferably, the dynamic adjustment of the data transmission frequency of the energy meter communication module includes:

[0016] Set the maximum and minimum values ​​for the data transmission interval, calculate the maximum adjustment range of the transmission interval, use the abnormality level 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.

[0017] Preferably, the step of marking users who do not belong to any of the clusters includes:

[0018] DBSCAN is used to cluster the stability feature values ​​of each user during the best time period. Based on the clustering results, users who do not belong to any cluster are marked as abnormal users, while the rest are marked as normal users. Real-time power consumption data of abnormal users are transmitted at a high frequency.

[0019] By using DBSCAN to cluster user stability characteristic values, different types of users can be effectively identified, distinguishing between normal and abnormal users. For abnormal users, high-frequency data transmission ensures timely detection and handling of abnormal electricity consumption, thereby improving system reliability and security. Conversely, lower-frequency data transmission can be used for normal users, optimizing energy management and reducing the operating power consumption of the electricity meter. This not only improves the efficiency of anomaly detection but also optimizes energy consumption, enhancing the overall system performance.

[0020] Preferably, obtaining the optimal time period includes the following steps:

[0021] The data lengths within a preset time period are traversed and selected. The stability feature value of the corresponding data length is calculated, and the preset time period corresponding to the largest stability feature value is selected as the length of the user's electricity consumption data sequence.

[0022] Its effect is that by traversing different data lengths within a preset time period and calculating the stability characteristic value corresponding to each length, the time period in which the user's electricity consumption behavior is most stable can be accurately determined. Selecting the time period with the largest stability characteristic value as the length of the user's electricity consumption data sequence ensures that the selected data segment best represents the user's typical electricity consumption behavior, providing the most reliable data foundation for subsequent electricity consumption behavior analysis, anomaly detection, and data transmission frequency adjustment.

[0023] Preferably, the historical and real-time electricity consumption data sequences of each user are preprocessed. The preprocessing steps include:

[0024] The slope between the nearest valid data points on both sides of the missing point is calculated using linear interpolation. The value of the missing point is estimated using the slope and the coordinates of the data points. The missing values ​​in the data are filled in, and the rationality of the filling results is verified to make the data smooth and consistent with the overall trend. The filled data is then normalized to ensure the integrity of the data.

[0025] The present invention has the following effects:

[0026] 1. This invention can promptly detect abnormal electricity consumption behavior by calculating the degree of anomaly in the user's real-time electricity consumption data sequence. By transmitting the real-time electricity consumption data of abnormal users at a high frequency, it ensures that abnormal situations can be detected and handled in a timely manner, avoiding potential losses caused by abnormal electricity consumption behavior. This not only improves the system's response speed but also enhances the system's reliability and security.

[0027] 2. This invention adaptively adjusts the data transmission frequency based on the degree of abnormality in the user's real-time electricity consumption behavior. When the user's electricity consumption behavior is stable, the data transmission frequency is reduced to decrease energy consumption; when the user's electricity consumption behavior is abnormal, the data transmission frequency is increased to promptly detect and handle abnormal situations, thereby significantly reducing the energy consumption of the electricity meter and extending the service life of the equipment. Attached Figure Description

[0028] Figure 1 This is a flowchart of steps S1-S4 in a control method for a low-power, high-precision energy meter according to an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0030] Reference Figure 1 A control method for a low-power, high-precision energy meter includes steps S1-S4, as detailed below:

[0031] S1: Obtain the historical and real-time electricity consumption data sequences for each user.

[0032] It should be noted that the system acquires the user's historical electricity consumption data sequence and real-time electricity consumption data sequence within a preset time period at fixed time intervals.

[0033] The historical and real-time electricity consumption data sequences of each user are preprocessed. The preprocessing steps include:

[0034] The slope between the nearest valid data points on both sides of the missing point is calculated using linear interpolation. The value of the missing point is estimated using the slope and the coordinates of the data points. The missing values ​​in the data are then filled in, and the reasonableness of the filling results is verified to make the data smooth and consistent with the overall trend, thus ensuring the integrity of the data.

[0035] It should be noted that when adjusting the data transmission frequency of the electricity meter's communication module, the user's electricity consumption patterns and preferences, as well as the characteristics of real-time electricity consumption data, must be comprehensively considered. Although a user's electricity consumption behavior is dynamic and not fixed, it does exhibit certain regularities. If the regularity and predictability of a user's electricity consumption behavior are strong, then their electricity consumption behavior is relatively stable. In this case, the data transmission frequency of the electricity meter's communication module can be appropriately reduced to decrease energy consumption.

[0036] S2: Iterate through the historical electricity consumption data sequence of each user within the preset time period, decompose the electricity consumption data sequence of different data lengths, extract feature components, and calculate the stability feature value of the electricity consumption data sequence corresponding to different data lengths for each user based on each feature component, and determine the optimal time period. The feature components include: trend component, periodic component and residual component.

[0037] The methods for calculating stability eigenvalues ​​include:

[0038] The variances of each characteristic component are calculated and summed to obtain the total variance of the electricity consumption data. The regularity proportion is obtained by calculating the ratio between the sum of the variances of the trend component and the periodic component and the total variance of the electricity consumption data. The relative intensity of random fluctuations is obtained by using a negative exponential function to map the ratio between the standard deviation of the residual component and the mean of all historical electricity consumption data sequences. The product of the regularity proportion and the relative intensity is used as the stability characteristic value of the historical electricity consumption data sequence.

[0039] It should be noted that the historical electricity consumption data series was decomposed using STL (Seasonal and Trend Decomposition using Loess) to obtain trend components, periodic components, and residual components. To quantify the stability of user electricity consumption behavior, features were extracted from these components. The trend component reflects long-term load level changes, the periodic component reflects fixed periodic fluctuations, and the residual component reflects sudden fluctuations (such as abnormal behavior or noise).

[0040] Specifically, the stability eigenvalues ​​satisfy the following relationship:

[0041] ;

[0042] In the formula, This represents the stability characteristic value of the historical electricity consumption data sequence. The variance representing the trend component. Represents the variance of the periodic component. This represents the total variance of electricity consumption data. The standard deviation of the residual components. This represents the mean of a historical electricity consumption data series.

[0043] By using STL decomposition to calculate the sum of the variances of the trend component and the periodic component, the regularity in electricity consumption data can be quantified. The trend component reflects long-term changes, while the periodic component reflects periodic changes; together, these two components constitute the main regularity characteristics of electricity consumption data.

[0044] The ratio of the sum of the variances of the trend component and the periodic component to the total variance can be used to assess the stability of electricity consumption behavior. A larger ratio indicates that the electricity consumption behavior is relatively stable, while a smaller ratio indicates that the residuals are relatively unstable. The trend component and the periodic component are regular components, while the residuals are random fluctuations.

[0045] It should be noted that a larger stability characteristic value indicates that the user's electricity consumption behavior is more regular. 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. Conversely, a smaller stability characteristic value means that the data transmission frequency of the electricity meter communication module cannot be adjusted too low to avoid data transmission failure in case of abnormal situations, which could affect the user's electricity safety.

[0046] The data lengths within a preset time period are traversed and selected. The stability feature value of the corresponding data length is calculated, and the preset time period corresponding to the largest stability feature value is selected as the length of the user's electricity consumption data sequence.

[0047] For example, the preset time period is 3-30 days, and the time period length is increased day by day. The stability characteristic value within each time period is calculated. When the stability characteristic value reaches its maximum, the optimal time period for user electricity consumption is obtained, indicating that the user's electricity consumption behavior is most stable and has the highest regularity and predictability within the optimal time period.

[0048] S3: Cluster the stability feature values ​​of each user's best time period to obtain user classifications for different electricity consumption types, and mark users who do not belong to each cluster. Calculate the correlation between the real-time electricity consumption data sequence and the historical electricity consumption data sequence of each user belonging to the cluster, and combine the stability feature values ​​of user electricity consumption behavior to calculate the abnormality value of the user's real-time electricity consumption data sequence.

[0049] DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is used to cluster the stability feature values ​​of each user during their optimal time period. Based on the clustering results, users who do not belong to any cluster are marked as abnormal users, while the rest are marked as normal users. Real-time power consumption data of abnormal users are transmitted at a high frequency.

[0050] 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.

[0051] The methods for calculating the anomaly level of real-time electricity consumption data sequences include:

[0052] Calculate the average similarity between the user's real-time electricity consumption data sequence and the periodic components of the historical electricity consumption data sequence after decomposition; select the minimum value between the stability feature value of the user's historical electricity consumption data sequence and the stability feature value corresponding to the center point of the cluster, and normalize the product between the difference of 1 minus the average similarity and the minimum value to obtain the anomaly value of the user's real-time electricity consumption data sequence.

[0053] Analyzing the similarity between real-time and historical electricity consumption data sequences aims to quantify the degree of anomaly in users' real-time electricity consumption behavior. By comparing the electricity consumption data of all corresponding time periods in the periodic components obtained from STL decomposition of real-time and historical electricity consumption data sequences, it can be determined whether the real-time electricity consumption behavior conforms to the user's regular pattern. Historical electricity consumption data sequences provide rich comparative benchmarks, which can improve the accuracy of anomaly detection. The periodic components reflect the periodic patterns of users' electricity consumption behavior; by comparing with the periodic components, abnormal changes in periodic electricity consumption patterns can be identified more accurately.

[0054] Specifically, the anomaly level values ​​satisfy the following relationship:

[0055] ;

[0056] In the formula, This value represents the degree of anomaly in a user's real-time electricity consumption data sequence. Represents a sequence of real-time electricity consumption data for users. Periodic components of historical electricity consumption data sequences within a preset time period The average similarity of electricity consumption data for all corresponding time periods in the data. Indicates the first Stability characteristics of historical electricity consumption data for individual users Indicates the first The first user's cluster center point corresponds to the... One stability eigenvalue, Describes the minimum value function. This represents the normalization function.

[0057] It should be noted that the mean value of the electricity consumption data similarity ranges from [value missing]. , The range of values ​​for is , The value can measure the consistency between real-time sequences and historical periodic components over the same time period, ensuring that the anomaly level value is always greater than 0, and making... It is negatively correlated with the degree of abnormality. The minimum value between the stability feature value of a user's historical electricity consumption data and the stability feature value of the corresponding cluster center is used to reflect the similarity value. When quantifying the degree of anomaly in real-time user electricity consumption behavior, a higher confidence level indicates more stable user electricity consumption behavior, and a higher reliability of the periodic component similarity between real-time electricity consumption data and historical electricity consumption data in calculating the degree of anomaly in real-time user electricity consumption data.

[0058] By calculating the similarity between real-time and historical electricity consumption data sequences and combining this with the stability characteristics of user electricity behavior, a normalized anomaly score is obtained. A higher anomaly score indicates more abnormal behavior corresponding to the real-time electricity consumption data sequence, requiring corresponding measures (such as increasing data transmission frequency) to ensure electricity safety. This is because acquiring continuous electricity consumption data at a high frequency allows for more accurate analysis of abnormal user behavior and is closely related to the function of the electricity meter. The communication module of the electricity meter is responsible for data transmission, and its energy consumption directly affects the overall power consumption of the meter. By dynamically adjusting the data transmission frequency, energy management can be optimized while ensuring anomaly detection efficiency, achieving the best balance between anomaly detection and power consumption management.

[0059] S4: Dynamically adjust the data transmission frequency of the electricity meter's communication module based on the anomaly level value to optimize energy consumption management and promptly detect abnormal electricity usage behavior.

[0060] Determining the transmission frequency based on the degree of anomaly includes the following steps:

[0061] Based on the magnitude of the anomaly, the data transmission frequency of the electricity meter's communication module is divided into different levels, responding to an anomaly value less than or equal to a preset threshold. When the anomaly level exceeds a preset threshold, the data transmission frequency is set to a low frequency; And less than the preset threshold When the abnormality level is greater than or equal to a preset threshold, the data transmission frequency is set to a medium frequency; When this is the case, the data transmission frequency is set to a high frequency.

[0062] The data transmission frequency of the electricity meter's communication module is dynamically adjusted based on the degree of abnormality in the user's real-time electricity consumption behavior. (Preset threshold) Preset threshold Specifically, when the anomaly level value does not exceed 0.4, the data transmission frequency is set to level one, such as once every 30 minutes; when the anomaly level value exceeds 0.4 but is lower than 0.7, it is set to level two, such as once every 10 minutes; when the anomaly level value is not lower than 0.7, it is set to level three, such as once every 2 minutes.

[0063] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A control method for a low-power, high-precision energy meter, characterized in that, include: Obtain historical and real-time electricity consumption data sequences for each user; The system iterates through the historical electricity consumption data sequences of each user within a preset time period, decomposes the electricity consumption data sequences of different data lengths, extracts feature components, and calculates the stability feature values ​​of the electricity consumption data sequences of different data lengths for each user based on each feature component, thereby determining the optimal time period. The feature components include: trend components, periodic components, and residual components. Cluster the stability feature values ​​of each user's best time period to obtain user classifications for different electricity consumption types, and mark users who do not belong to each cluster. Calculate the correlation between the real-time electricity consumption data sequence and the historical electricity consumption data sequence of each user belonging to the cluster, and combine the stability feature values ​​of user electricity consumption behavior to calculate the abnormality value of the user's real-time electricity consumption data sequence. The data transmission frequency of the electricity meter's communication module is dynamically adjusted based on the degree of anomaly, in order to optimize energy consumption management and promptly detect abnormal electricity usage behavior.

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

3. The control method for a low-power, high-precision energy meter according to claim 1, characterized in that, The calculation method for the anomaly level value of the real-time electricity consumption data sequence includes: Calculate the average similarity between the user's real-time electricity consumption data sequence and the periodic components of the historical electricity consumption data sequence after decomposition; select the minimum value between the stability feature value of the user's historical electricity consumption data sequence and the stability feature value corresponding to the center point of the cluster, and normalize the product between the difference of 1 minus the average similarity and the minimum value to obtain the anomaly value of the user's real-time electricity consumption data sequence.

4. The control method for a low-power, high-precision energy meter according to claim 1, characterized in that, The dynamic adjustment of the data transmission frequency of the electricity meter communication module includes: Based on the magnitude of the anomaly, the data transmission frequency of the electricity meter's communication module is divided into different levels, responding to an anomaly value less than or equal to a preset threshold. When the anomaly level exceeds a preset threshold, the data transmission frequency is set to a low frequency; And less than the preset threshold When the abnormality level is greater than or equal to a preset threshold, the data transmission frequency is set to a medium frequency; When this is the case, the data transmission frequency is set to a high frequency.

5. The control method for a low-power, high-precision energy meter according to claim 1, characterized in that, The process of marking users who do not belong to any of the clusters includes: DBSCAN is used to cluster the stability feature values ​​of each user during the best time period. Based on the clustering results, users who do not belong to any cluster are marked as abnormal users, while the rest are marked as normal users. Real-time power consumption data of abnormal users are transmitted at a high frequency.

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

7. The control method for a low-power, high-precision energy meter according to claim 1, characterized in that, The historical and real-time electricity consumption data sequences of each user are preprocessed. The preprocessing steps include: The slope between the nearest valid data points on both sides of the missing point is calculated using linear interpolation. The value of the missing point is estimated using the slope and the coordinates of the data points. The missing values ​​in the data are filled in, and the rationality of the filling results is verified to make the data smooth and consistent with the overall trend. The filled data is then normalized to ensure the integrity of the data.

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