Method and system for monitoring power consumption conditions based on smart meters

Abstract

The application relates to the technical field of intelligent electric meter, and discloses a monitoring method for power consumption conditions based on an intelligent electric meter, which comprises the following steps: step 1: the intelligent electric meter collects power consumption data of a user and sends the power consumption data to an information processing center, wherein the power consumption data comprises current frequency and power consumption; step 2: the information processing center collects the power consumption data of the user and obtains power consumption characteristics of various types of electric appliances of the user; and step 3: the information processing center generates a risk report of the user and power consumption of various types of electric appliances according to the power consumption characteristics of various types of electric appliances of the user in the current month and the power consumption characteristics of various types of electric appliances in history. In the technical scheme, the power consumption data is collected through the intelligent electric meter, so that the information processing center can obtain the power consumption characteristics of various types of electric appliances according to the power consumption data, and then measure the difference between the current power consumption characteristics and the historical power consumption characteristics according to the historical power consumption characteristics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of smart meters, in particular, relates to a monitoring method and monitoring system for power consumption based on a smart meter. BACKGROUND

[0002] A smart meter is an important device for monitoring user power consumption. The existing smart meter has more and more perfect functions, can collect various power consumption data features, and has an independent communication function. However, in the use process, the user is generally sent the monthly power consumption in order to facilitate the user to pay. However, when the user finds that the power consumption is much larger than expected in daily life, it is not clear whether the abnormal power consumption is caused by the increase of the user's power consumption or by the illegal personnel who steal electricity by privately pulling the power line.

[0003] Therefore, the power consumption report sent by the power supply company to the user only has the total power consumption of the user, and cannot answer the user's doubts about power consumption and give corresponding abnormal alarms. SUMMARY

[0004] The summary part of the present application is used to introduce the concept in a simple form, which will be described in detail in the specific embodiment part. The summary part of the present application is not intended to identify the key features or essential features of the claimed technical solution, nor is it intended to limit the scope of the claimed technical solution.

[0005] As a first aspect of the present application, in order to solve the technical problems mentioned in the background part, some embodiments of the present application provide a monitoring method for power consumption based on a smart meter, comprising the following steps:

[0006] Step 1: The smart meter collects the user's power consumption data and sends the power consumption data to the information processing center. The power consumption data includes current frequency and power consumption;

[0007] Step 2: The information processing center collects the user's power consumption data to obtain the power consumption features of various types of user appliances;

[0008] Step 3: The information processing center generates a risk report for the user and the power consumption of various types of appliances according to the power consumption features of various types of appliances of the user in the month and the historical power consumption features of various types of appliances.

[0009] In the technical solution provided in the present application, the user's power consumption data is collected by the smart meter, so that the information processing center can obtain the power consumption characteristics of various electrical appliances according to the power consumption data, and then measure the difference between the current power consumption characteristics and the historical power consumption characteristics according to the historical power consumption characteristics of the electrical appliances, so as to determine whether the user has an abnormal power consumption risk. In addition, the user can also determine whether there is an abnormality according to the power consumption of various electrical appliances. For example, the report shows that the user's high-frequency power consumption equipment (air conditioner) has increased by 1 times in the current month, and if the user does not additionally start a new air conditioner, it can be basically determined that the user may have a situation of stealing electricity.

[0010] In daily life, the user cannot upload the information of the power consumption equipment every time the user uses electricity. Therefore, the smart meter can only collect the power consumption, and cannot obtain the power consumption of various electrical appliances. In view of this problem, the present application provides the following technical solution:

[0011] Further, step 1 includes the following steps:

[0012] Step 11: The smart meter collects the data of the change of the current frequency of the user with time to obtain frequency data;

[0013] Step 12: The smart meter collects the data of the change of the power consumption of the user with time to obtain power consumption data, takes the power consumption data and the frequency data as power consumption data, and then sends the power consumption data to the information processing center.

[0014] In the technical solution provided in the present application, collecting the power consumption data, current data, and frequency data changing with time is equivalent to collecting the related data capable of representing the power consumption characteristics of the user. Analyzing this kind of data can accurately analyze the power consumption characteristics of the user. Further, the power consumption equipment and the frequency corresponding to each power consumption equipment can be analyzed.

[0015] The existing power consumption equipment is very rich in variety, and there are many brands of air conditioners alone, and each brand has many models, so it is difficult to reasonably classify the types of electrical appliances.

[0016] Further, step 2 includes the following steps:

[0017] Step 21: At least three types of electrical appliances are divided according to the power consumption frequency characteristics of different electrical appliances, which are A1, A2, A3…A j …A k ; A j represents the jth type of electrical appliance, A k represents the last type of electrical appliance, j≤k, k≥3, j and k are integers;

[0018] The frequency interval of the first type of electrical appliances is [w a1 ~ w b1 The frequency interval of the second type of electrical appliances is [w a2 ~ w b2 The frequency interval of the third type of electrical appliances is [w a3 ~ w b3 The frequency interval of the jth type of electrical appliances is [w aj ~ w bj The frequency interval of the kth type of electrical appliances is [w ak ~ w bk ] w aj represents the lower limit of the frequency of the jth type of electrical appliances, and w bj represents the upper limit of the frequency of the jth type of electrical appliances.

[0019] Step 22: The information processing center divides each day into a plurality of time periods, respectively B1, B2, … B i … B n ; wherein B1 represents the first time period of each day, B i represents the ith time period of each day, n represents the number of time periods divided each day, and i and n are positive integers.

[0020] Step 23: The information processing center collects the electrical data of the user obtained in the time period B i , obtains the power consumption of each type of electrical appliances in the time period B i , and takes it as the power consumption characteristics of the user in the time period B i .

[0021] The scheme provided in the present application divides the electrical appliances by frequency, which can reasonably divide the household appliances. Generally speaking, low-frequency electrical appliances belong to lighting devices in the home, and high-frequency electrical appliances are generally air conditioners and the like in the home. This division avoids the problem that too many types of electrical appliances are not easy to divide. Moreover, this division is highly accurate in household appliances and the classification is also reasonable. Moreover, for most users, the production and life style is basically the same every day. In the present scheme, each day is divided into a plurality of time periods, and then the power consumption characteristics in each time period are obtained, so as to obtain the change of the user's power consumption data with time, and then reflect the user's power consumption habits in the day. Therefore, in the present scheme, the power consumption characteristics of the user in the day can maximize the reflection of the user's power consumption habits.

[0022] Furthermore, each day is divided into seven time periods: 0:00-7:00, 7:00-9:00, 9:00-11:30, 11:30-14:00, 14:00-18:00, 18:00-22:00, and 22:00-24:00.

[0023] In the technical solution provided by this application, the division into the above-mentioned 7 natural segments can better conform to the production and living habits of users, and the user's electricity usage characteristics will be presented in each of these time periods.

[0024] In actual electricity usage, users may experience sudden increases in usage of certain electrical equipment during certain periods of time. This can cause abnormal values ​​to accumulate for that equipment over the course of a day, leading to an abnormal increase in risk. For example, a user might not use the air conditioner in the morning, but one morning they do. This creates an initial abnormal value for the air conditioner. In the afternoon, when the user uses the air conditioner normally, this abnormal value accumulates, causing abnormal electricity usage in the afternoon as well, further amplifying the risk.

[0025] Furthermore, step 3 includes the following steps:

[0026] Step 31: On the last day of the month, the information processing center calculates the average electricity consumption of various electrical appliances by users in each time period of the previous month. in, Indicates the user's time period B in the previous month i Average power consumption of electrical appliance j;

[0027] Step 32: The information processing center calculates the average power consumption of each type of electrical appliance in each time period of the user in that month Indicates the user's time period B in the current month i Average power consumption of electrical appliance j;

[0028] Step 33: The information processing center calculates the average power consumption of various electrical appliances by the user in each time period of the previous month. Draw the average number of various electrical appliances used by users in the past month The scatter plot of time changes within 24 hours every day is used to obtain k historical data images, namely M1, M2, ...M j …M k ; where M j is the average power consumption of appliance j in each time period of the previous month images that change over time;

[0029] Step 34: The information processing center calculates the average power consumption of various electrical appliances by the user in each time period of the month. Draw the average electricity consumption of various electrical appliances by users in the month The scatter plot of the changes over time within 24 hours of each day is used to obtain k data images of the current month, namely m1, m2, ...m j …m k ; where m j is the average power consumption of appliance j in each time period of the previous month images that change over time;

[0030] Step 35: The information processing center calculates the distortion values ​​of the monthly data image and the historical data image of each electrical appliance in the user, which are C1, C2, ... C j …C k ;

[0031] Step 36: Calculate the risk Y based on the distortion value of the monthly data image and the historical data image of each electrical appliance in the user, Y = C1 + C2 + ... + C j +…+C k , C j Distortion value of electrical appliance j;

[0032] The information processing center uses the risk degree Y as a risk report and sends the risk report and the electricity consumption of various electrical appliances in that month to the user.

[0033] In the technical solution provided by this application, the electricity consumption in each time period of each day is recalculated, so abnormal values ​​are avoided and accumulated, which leads to abnormal increase in risk and makes it impossible to accurately provide risk levels.

[0034] Furthermore, in step 32, a historical data image is generated based on the least squares method.

[0035] Furthermore, in step 33, a data image of the current month is generated based on the least squares method.

[0036] Furthermore, the method for calculating the distortion value in step 34 includes the following steps:

[0037] Step 341: pre-set an error threshold D;

[0038] Step 342: Calculate m j With M j The integral R of the distance;

[0039] Step 342: C j =RD.

[0040] In the solution provided in this application, the size of the risk assessment report can be controlled by pre-setting the size of the threshold D. For users whose electricity consumption does not change much, the size of the threshold D can be reduced. For users whose electricity consumption is prone to change, the size of the threshold D needs to be increased.

[0041] As a second aspect of the present application, some embodiments of the present application provide a monitoring system of power consumption condition based on smart meter, comprising a smart meter and an information processing platform, the smart meter and the information processing platform are signal connected, the information processing platform generates a risk report of a user and power consumption of various types of electrical appliances according to the aforementioned monitoring method of power consumption condition based on smart meter. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and are used in conjunction with the description to explain the present application, and do not limit the present application in any manner.

[0043] In addition, throughout the drawings, the same or similar reference numerals are used to represent the same or similar elements. It should be understood that the drawings are schematic, and the elements and elements are not necessarily drawn according to the scale.

[0044] In the drawings:

[0045] Figure 1 Flow chart of the monitoring method of power consumption condition based on smart meter.

[0046] Figure 2 Scatter plot of monthly power consumption of electrical appliance j in the time period B1-B5;

[0047] Figure 3 Monthly data image of monthly power consumption of electrical appliance j in the time period B1-B5;

[0048] Figure 4 Scatter plot of historical power consumption of electrical appliance j in the time period B1-B5;

[0049] Figure 5 Monthly data image of historical power consumption of electrical appliance j in the time period B1-B5;

[0050] Figure 6 Schematic diagram for calculating the integral R of the distance of electrical appliance j. DETAILED DESCRIPTION

[0051] Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the present application more thorough and complete. It should be understood that the drawings and embodiments of the present application are only for exemplary purposes, and are not intended to limit the scope of protection of the present application.

[0052] It should be further noted that only parts related to the application are shown in the drawings for the convenience of description. The embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0053] The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0054] Referring to Figure 1 A monitoring method of power consumption condition based on smart meter, comprising the following steps:

[0055] Step 1: The smart meter collects the power consumption data of the user, and sends the power consumption data to the information processing center, wherein the power consumption data includes current frequency and power consumption.

[0056] Step 1 comprises the following steps:

[0057] Step 11: The smart meter collects the data of the change of the current frequency of the user with time, and obtains frequency data.

[0058] Various power consumption devices will produce their unique current characteristics during use, especially air conditioners, refrigerators, washing machines and other devices with motors and compressors, which will produce very obvious power consumption signals when working. As a new generation of current meter, the smart meter can collect the current frequency of the user and upload it.

[0059] Step 12: The smart meter collects the data of the change of the power consumption of the user with time, and obtains power consumption data. The power consumption data and the frequency data are used as power consumption data, and then the power consumption data is sent to the information processing center.

[0060] Step 2: The information processing center collects the power consumption data of the user, and obtains the power consumption characteristics of various power consumption devices of the user.

[0061] Step 2 comprises the following steps:

[0062] Step 21: At least three types of power consumption devices are divided according to the power consumption frequency characteristics of different power consumption devices, which are A1, A2, A3…A j …A k ; A j represents the jth type of power consumption device, A k represents the last type of power consumption device, j≤k, k≥3, j and k are integers; the frequency interval of the first type of power consumption device is [w a1 ~w b1 ], the frequency interval of the second type of power consumption device is [w a2 ~w b2 ], and the frequency interval of the third type of power consumption device is [w a3 ~w b3], the frequency interval of the jth type of electrical appliance is [w aj ~ w bj ], the frequency interval of the kth type of electrical appliance is [w ak ~ w bk ] ; w aj represents the lower limit of the frequency of the jth type of electrical appliance, w bj represents the upper limit of the frequency of the jth type of electrical appliance.

[0063] Different types of electrical appliances have different electrical characteristics, and this characteristic is very prominent here. In this scheme, the frequency is used to divide the types of electrical appliances, and the accuracy of the division is high. In practice, the user does not simply use one electrical device, or use two electrical devices, so the obtained electrical data is very complex. In this scheme, only the classification method of the electrical device is provided, and the specific identification method of the electrical device is to use the non-intrusive load identification technology commonly used in the art. This technology can identify the type of electrical appliance according to the electrical characteristic class. Therefore, in order to cooperate with the use of non-intrusive load identification technology, the smart meter also needs to collect the remaining data required by the technology when used, such as current, voltage, etc.

[0064] Step 22: The information processing center divides each day into several time periods, respectively B1, B2, … Bn. i … Bn. n ; wherein B1 represents the first time period of each day, B i represents the i th time period of each day, n represents the number of time periods divided each day, i and n are positive integers, and n>2.

[0065] Further, each day is divided into 0-7, 7-9, 9-11:30, 11:30-14, 14-18, 18-22 and 22-24 hours.

[0066] There are 24 hours a day, but in the living area of the residents, the power consumption of these 24 hours is actually not consistent. Generally speaking, the power consumption at night is less than that during the day, and the power consumption in the morning is less than that in the afternoon. In this scheme, the 7 time periods can effectively reflect the user's power consumption characteristics each day.

[0067] Step 23: The information processing center collects the user's power consumption data obtained in the time period B i , obtains the power consumption of each type of electrical appliance in the time period B i , and takes it as the user's power consumption characteristics in the time period B i .

[0068] In step 1, the electrical equipment is allocated and non-intrusive load identification technology is used to identify various types of electrical equipment, so the power consumption of each type of electrical equipment can be known. Therefore, for a time period B i , you can obtain the power consumption of each appliance during this time period. For example, from 0:00 to 7:00, A1's power consumption is 0, A2's power consumption is 10, and so on. It is important to note that the power consumption calculation for each time period is independent. For example, from 0:00 to 7:00, A1's power consumption is 11. From 7:00 to 9:00, A1's power consumption needs to be accumulated from 0, not 11.

[0069] Step 3: The information processing center generates a risk report for the user and the electricity consumption of each type of electrical appliance based on the electricity consumption characteristics of the user's various electrical appliances in the current month and the historical electricity consumption characteristics of each type of electrical appliance.

[0070] Step 3 includes the following steps:

[0071] Step 31: On the last day of the month, the information processing center calculates the consumption of various electrical appliances by users in each time period of the previous month.

[0072] Average electricity consumption in, Indicates the user's time period B in the previous month i Average electricity consumption of electrical appliance j.

[0073] Generally speaking, users' daily electricity usage habits are largely consistent. Therefore, average electricity consumption across different time periods is roughly the same. When this range is narrowed down to specific appliances, the variation becomes even smaller. For example, if a user cooks rice in an electric rice cooker daily, the amount of electricity consumed by the rice cooker will not vary significantly from day to day.

[0074] Step 32: The information processing center calculates the average power consumption of each type of electrical appliance in each time period of the user in that month Indicates the user's time period B in the current month i Average power consumption of electrical appliance j;

[0075] refer to Figures 4-5 Step 33: The information processing center calculates the average power consumption of various electrical appliances by the user in each time period of the previous month. Draw the average number of various electrical appliances used by users in the past month The scatter plot of time changes within 24 hours every day is used to obtain k historical data images, namely M1, M2, ...M j …M k ; where M jthe average of the power consumption of the electric appliance j in each time period of the past month the image of the average of the power consumption of the electric appliance j in each time period of the past month

[0076] For example, there are three electric appliances A, B and C. For the electric appliance A, the average of the power consumption of A in each time period of the past month is calculated, and then the image of the average of the power consumption of the electric appliance A in the past month is obtained. As shown in Figure 5 Figure 5 M j .

[0077] Referring to Figures 2-3 , step 34: the information processing center draws the image of the average of the power consumption of each type of electric appliance of the user in each time period of the current month according to the average of the power consumption of each type of electric appliance of the user in each time period of the current month the image of the average of the power consumption of each type of electric appliance of the user in each time period of the current month the scatter plot of the average of the power consumption of each type of electric appliance of the user in each time period of the current month j …m k ; wherein m j is the average of the power consumption of the electric appliance j in each time period of the past month the image of the average of the power consumption of the electric appliance j in each time period of the past month

[0078] Further, in step 32, the historical data image is generated based on the least square method.

[0079] The least square method is prior art in the field, and the specific fitting method is not described here.

[0080] Further, in step 33, the current data image is generated based on the least square method.

[0081] Step 35: the information processing center sequentially calculates the distortion values of each type of electric appliance of the user between the current data image and the historical data image, respectively C1, C2, … C j …C k .

[0082] Further, step 34 includes the following steps:

[0083] Step 351: an error threshold D is set in advance.

[0084] Step 352: the integral R of the distance between m j and M j is calculated.

[0085] Referring to Figure 6 , the integral R of the distance between m j and M j is calculated, which is actually the calculation of the shaded area shown in Figure 6 , because m j and M​j is adopted to carry out the least square fitting, so that the expression of two images can be obtained, and then the shadow area in the image can be solved by integration. Figure 6

[0086] Step 353: C j = R-D.

[0087] Thus, if D is set to be large, the value of C j is easy to be small, and if D is set to be small, the value of C j is easy to be large, so D is a variable for the user to measure the error, which can be adjusted according to the needs.

[0088] Step 36: According to the distortion value of each kind of electrical appliance in the user in the monthly data image and the historical data image, the risk degree Y is calculated, Y = C1+C2+…+C j +…+C k , C j is the distortion value of the electrical appliance j, the risk report is generated according to the risk degree, and then the risk report and the power consumption of each kind of electrical appliance are sent to the user.

[0089] Embodiment 2: A power consumption condition monitoring system based on a smart meter, comprising a smart meter and an information processing platform, the smart meter and the information processing platform are signal connected, and the information processing platform generates the risk report of the user and the power consumption of each kind of electrical appliance according to the power consumption condition monitoring method based on the smart meter.

[0090] The above description is only some preferred embodiments of the present application and the explanation of the applied technical principles. Those skilled in the art should understand that the application range of the embodiments of the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and should also cover other technical solutions formed by the above technical features or their equivalent features in any combination without departing from the above inventive concept. For example, the above features and the technical features disclosed in the embodiments of the present application (but not limited to) with similar functions are replaced with each other to form technical solutions.​

Claims

1. A method for monitoring electricity usage based on a smart meter, characterized by: The steps include: Step 1: Smart meters collect user electricity usage data and send it to an information processing center. The data includes current frequency and power consumption. Step 2: The information processing center collects the user's electricity consumption data and obtains the electricity consumption characteristics of various electrical appliances of the user; Step 3: The information processing center generates a risk report for the user and the power consumption of each type of electrical appliance based on the user's power consumption characteristics of each type of electrical appliance in the current month and the historical power consumption characteristics of each type of electrical appliance; Step 3 includes the following steps: Step 31: On the last day of the month, the information processing center calculates the average electricity consumption of various electrical appliances by users in each time period of the previous month. ,in, Indicates the user's time period B in the previous month i Average power consumption of electrical appliance j; Step 32: The information processing center calculates the average power consumption of each type of electrical appliance in each time period of the user in that month , Indicates the user's time period B in the current month i Average power consumption of electrical appliance j; Step 33: The information processing center calculates the average power consumption of various electrical appliances by the user in each time period of the previous month. , plot the average number of various electrical appliances used by users in the previous month The scatter plot of time changes within 24 hours every day is used to obtain k historical data images, namely M1, M2, ...M j …M k ; where M j is the average power consumption of appliance j in each time period of the previous month images that change over time; Step 34: The information processing center calculates the average power consumption of various electrical appliances by the user in each time period of the month. , plot the average electricity consumption of various electrical appliances by users in that month The scatter plot of the changes over time within 24 hours of each day is used to obtain k data images of the current month, namely m1, m2, ...m j …m k ; where m j is the average power consumption of appliance j in each time period of the previous month images that change over time; Step 34: The information processing center calculates the distortion values ​​of the monthly data image and the historical data image of each electrical appliance in the user, which are C1, C2, ... C j …C k ; Step 35: Calculate the risk Y based on the distortion value of the monthly data image and the historical data image of each electrical appliance in the user, Y=C1+C2+…+C j +…+C k , the information processing center takes the risk degree Y as a risk report, and sends the risk report and the electricity consumption of various electrical appliances in that month to the user.

2. The method for monitoring electricity usage based on a smart meter according to claim 1, characterized in that: Step 1 includes the following steps: Step 11: The smart meter collects data on the user's current frequency changing over time to obtain frequency data; Step 12: The smart meter collects data on the user's electricity consumption over time to obtain electricity consumption data, uses the electricity consumption data and frequency data as electricity consumption data, and then sends the electricity consumption data to the information processing center.

3. The method for monitoring electricity usage based on a smart meter according to claim 2, characterized in that: Step 2 includes the following steps: Step 21: Classify at least three types of electrical appliances according to their power consumption frequency characteristics, namely A1, A2, A3...A j …A k ; A j A represents the jth type of electrical appliance, k Indicates the last type of electrical appliance, j≤k, k≥3, j and k are both integers; The frequency range of the first type of electrical appliances is [w a1 ~w b1 ], the frequency range of the second type of electrical appliances is [w a2 ~w b2 ], the frequency range of the third type of electrical appliances is [w a3 ~w b3 ], ... the frequency range of the jth type of electrical appliances is [w aj ~w bj ]…The frequency range of the kth type of electrical appliances is [w ak ~w bk ];w aj Indicates the lower limit of the frequency of the jth type of electrical appliance, w bj Indicates the upper limit of the frequency of the jth type of electrical appliance; Step 22: The information processing center divides each day into several time periods, namely B1, B2, ...B i …B n ; Among them, B1 represents the first time period of each day, B i represents the i-th time period of each day, n represents the number of time periods divided into each day, i and n are positive integers, n>2; Step 23: Information Processing Center collects time period B i The electricity consumption data of users obtained in time period B i The power consumption of various electrical appliances in time period B i The electricity usage characteristics within.

4. The method for monitoring electricity usage based on a smart meter according to claim 1, characterized in that: Each day is divided into seven time periods: 0:00~7:00, 7:00~9:00, 9:00~11:30, 11:30~14:00, 14:00~18:00, 18:00~22:00, and 22:00~24:

00.

5. The method for monitoring electricity usage based on a smart meter according to claim 1, characterized in that: In step 33, a historical data image is generated based on the least squares method.

6. The method for monitoring electricity usage based on a smart meter according to claim 1, characterized in that: In step 34, a data image of the current month is generated based on the least squares method.

7. The method for monitoring electricity usage based on a smart meter according to claim 1, characterized in that: The method for calculating the distortion value in step 34 includes the following steps: Step 341: pre-set an error threshold D; Step 342: Calculate m j With M j The integral R of the distance; Step 342: C j =RD.

8. A system for monitoring electricity usage based on smart meters, characterized by: The invention comprises a smart meter and an information processing platform, the smart meter and the information processing platform are connected by signals, and the information processing platform generates a user risk report and the power consumption of various electrical appliances according to the monitoring method of the power consumption status based on the smart meter according to any one of claims 1 to 7.

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