A method for calculating household basic electricity power based on power big data

Abstract

The present invention provides a method for calculating household basic power consumption based on power big data, comprising the following steps: A. obtaining user power consumption data; B. preprocessing the data according to the power consumption data obtained in step A; C. determining the time for calculating the basic power consumption according to the power consumption data preprocessed in step B; D. calculating the household basic power consumption according to the time determined in step C. The method for calculating household basic power consumption based on power big data proposed in the present invention does not require the installation of additional devices or sensors, and can provide data basis for health management of community residents.

Description

Technical Field

[0001] The present invention relates to the technical field of power big data application, and in particular to a method for calculating household basic electricity power based on power big data. Background Art

[0002] The basic household power consumption refers to the power consumption of household appliances (including refrigerators, set-top boxes, routers, etc.) after the user rests at night. As long as the appliances are connected to the power supply, and the circuit boards, display screens, and necessary program settings are in standby mode, there will inevitably be power loss. Since each user has different types and power sizes of household appliances and different usage habits, the basic household power consumption of users will also be different. The main types of household appliances include: the first category: normally open type, such as refrigerators, water dispensers, etc.; the second category: standby type, such as televisions, routers, air conditioners, range hoods, microwave ovens, set-top boxes, etc.; the third category: plug-and-play type, such as hair dryers, vacuum cleaners, electric irons, etc. When users rest at night, the first and second category appliances may be in power consumption at the same time. Since the first category appliances have two states, standby and running, when the first and second category appliances are in power consumption, there are two situations, namely, the first and second category appliances are in standby mode, and the first and second category appliances are in operation.

[0003] There are many kinds of household appliances in ordinary households. Although the standby power is very low, if they are in standby mode for 365 days a year, the standby energy consumption of all appliances added together is also a considerable expense. The country encourages individuals to establish a low-carbon, green lifestyle, save electricity, oil, energy, and water, and start from the little things to contribute to carbon peak and carbon neutrality. By conducting basic household power analysis, we can provide data basis for household energy conservation and emission reduction, and help develop good electricity usage habits.

[0004] The Chinese utility model patent "An indoor power detection device" with application number 201320024225.1 discloses an indoor power detection device, including a power detection circuit, an analog-to-digital converter, a processor, a communication interface, and a host computer, etc., which can determine whether the electrical equipment is using electricity normally by detecting power changes. The Chinese patent "Current transformer ratio detection method and power detection method" with application number 201910741519.8 obtains the current transformer ratio within the detection time period according to the rated primary current standard value and the rated secondary current standard value, thereby determining the actual power consumption. The Chinese invention patent "A household power calculation method" with application number 201811366997.7 can analyze the current, and then calculate the active power, reactive power, dispersed power and harmonic power of household electricity respectively, so as to improve the accuracy and fairness of power supply metering.

[0005] The existing patents related to power consumption detection, patents with application numbers 201320024225.1 and 201910741519.8, both require additional installation of devices or sensors to detect actual power consumption through devices, and patents with application number 201811366997.7 mainly calculate active power and reactive power. The above three patents do not involve the calculation of basic household power consumption. The calculation method of basic household power consumption based on power big data proposed in the present invention can calculate the first and second basic household power consumptions, and reflect the total basic power of the second type of household appliances (standby type) through the first basic power consumption, and reflect the total basic power of the first type (normally open type) and the second type of household appliances (standby type) through the second basic power consumption. The calculated basic household power consumption can provide data basis for household energy saving and consumption reduction, and help to develop good electricity consumption habits. Summary of the invention

[0006] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for calculating basic household electricity consumption based on power big data. The calculated basic household electricity consumption can provide a data basis for household energy conservation and consumption reduction, thereby helping to develop good electricity consumption habits.

[0007] The technical solution adopted by the present invention is:

[0008] A method for calculating household basic electricity power based on power big data includes the following steps:

[0009] A. Obtain user power consumption data;

[0010] B. Preprocess the power consumption data obtained in step A;

[0011] C. Determine the time to calculate the basic power consumption according to the power consumption data preprocessed in step B;

[0012] D. Calculate the basic household electricity consumption according to the time for calculating the basic electricity consumption determined in step C.

[0013] Furthermore, the user's power consumption data includes the date and the user's power consumption data at 24 o'clock in a day.

[0014] Furthermore, in step B, the power consumption data obtained in step A is preprocessed, specifically:

[0015] Taking user i as an example, assuming that the power consumption data of n days is collected, the matrix formed by its power consumption and date is P i :

[0016]

[0017] in Indicates that user i is on date D m The power consumption at time j, P ij Represents all the power consumption of user i at time j, and the matrix P i Delete the elements that are 0 or empty.

[0018] Furthermore, in step C, the time for calculating the basic power consumption is determined according to the power consumption data preprocessed in step B, specifically:

[0019] Calculate the standard deviation of the power consumption of users at different times and coefficient of variation V Pi ,

[0020]

[0021] in is the standard deviation of power consumption of user i at time j, is the coefficient of variation of the power consumption of user i at time j;

[0022]

[0023] If for time j, And k≠j, or Then the time j is determined as the time for calculating the basic electric power.

[0024] Furthermore, in step D, the basic household power consumption is calculated according to the time of calculating the basic power consumption determined in step C, specifically:

[0025] According to the time j of calculating the basic power consumption determined in step C, the active power corresponding to the time j is input into SPSS for clustering, and the number of cluster centers is set to M. Clustering obtains the number of cluster centers and their corresponding members. If the cluster centers are recorded as {C1, C2, …, C M}, the number of members corresponding to each class is {NU1,NU2,…,NU M};

[0026] like And q≠m, C m <C q , then C m is the user's first basic power consumption, and the corresponding support is

[0027] like And q≠h, C m <C h <C q , then C his the user's second basic power consumption, and the corresponding support is

[0028] The present invention provides a method for calculating basic household electricity consumption based on electric power big data, which can calculate the first and second basic electricity consumptions of the household. The first basic electricity consumption is used to reflect the total basic power of the second type of electrical appliances (standby type) in the household, and the second basic electricity consumption is used to reflect the total basic power of the first type (normally open type) and the second type of electrical appliances (standby type). The calculated basic household electricity consumption can provide data basis for household energy conservation and consumption reduction, and help to develop good electricity consumption habits. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of one embodiment of a method for calculating basic household electricity consumption based on electric power big data of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the present invention.

[0031] See also Figure 1 , is a flow chart of one embodiment of a method for calculating household basic electricity consumption based on power big data of the present invention, the method comprising the following steps:

[0032] A. Obtain the user's power consumption data, including the date and the user's power consumption data at 24 o'clock a day.

[0033] B. Preprocess the data based on the electricity consumption data obtained in step A, specifically:

[0034] Taking user i as an example, assuming that the power consumption data of n days is collected, the matrix formed by its power consumption and date is P i

[0035]

[0036] in Indicates that user i is on date D m The power consumption at time j, P ij represents the total power consumption of user i at time j. To ensure the correctness of the basic power consumption calculation, the matrix P i Delete the elements that are 0 or empty.

[0037] C. According to the power consumption data preprocessed in step B, determine the time to calculate the basic power consumption, specifically:

[0038] Calculate the standard deviation TD of the user's power consumption at different times Pi and coefficient of variation VPi ,

[0039]

[0040] in is the standard deviation of power consumption of user i at time j, is the coefficient of variation of the power consumption of user i at time j.

[0041]

[0042] For user i, in order to calculate its basic power consumption, it is first necessary to determine the time for calculating the basic power consumption.

[0043] If for time j, And k≠j, or Then the time j is determined as the time for calculating the basic electric power.

[0044] D. Calculate the household basic electricity power according to the time determined in step C, specifically:

[0045] According to the time j of calculating the basic power consumption determined in step C, the active power corresponding to the time j is input into SPSS for clustering, and the number of cluster centers is set to M. Clustering obtains the number of cluster centers and their corresponding members. If the cluster centers are recorded as {C1, C2, …, C M}, the number of members corresponding to each class is {NU1,NU2,…,NU M}.

[0046] like And q≠m, C m <C q , then C m is the user's first basic power consumption, and the corresponding support is

[0047] like And q≠h, C m <C h <C q , then C h is the user's second basic power consumption, and the corresponding support is

[0048] The technical solution and effects of the present invention are described in detail below with a specific embodiment:

[0049] Step A: Obtain user power consumption data. The following Table 1 shows sample data (taking user i as an example).

[0050] Table 1

[0051]

[0052]

[0053] Step B: pre-process the data based on the collected power consumption data of user i.

[0054] Step C, for the preprocessed data, determine the time for calculating the basic power consumption, as shown in Table 2.

[0055] Table 2

[0056]

[0057]

[0058] From the calculation results, we can see that the standard deviation and coefficient of variation of the power consumption at time 3 are both minimum values, so time 3 is selected as the time for calculating the basic power consumption.

[0059] Step D: According to the time of calculating the basic power consumption determined in step C, the power at time 3 is input into SPSS, the cluster center is set to 3, and 3 centers are obtained by clustering, as shown in Table 3.

[0060] Table 3

[0061] Serial number Cluster Center Number of cases Support 1 0.35 1 0.0125 2 0.09 43 0.5375 3 0.02 36 0.45

[0062] Since the smallest cluster center is 0.02, the first basic power consumption of the user is 0.02, and the support degree is 0.45.

[0063] Since the second smallest cluster center is 0.09, the second basic power consumption of the user is 0.09, and the support degree is 0.5375.

[0064] Through on-site visits and verification, the calculation results of the method proposed in the present invention are consistent with the on-site verification results.

[0065] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by technicians in this technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for calculating household basic electricity power based on power big data, characterized in that The following steps are involved: A. Obtain user power consumption data; B. Preprocess the power consumption data obtained in step A; C. Determine the time to calculate the basic power consumption according to the power consumption data preprocessed in step B; D. Calculate the household basic electricity consumption according to the time of calculating the basic electricity consumption determined in step C; The user's power consumption data includes the date and the user's power consumption data at 24 times a day; In step B, the power consumption data obtained in step A is preprocessed, specifically: Collect the power consumption data of user i on n days. The matrix formed by the power consumption data of user i and the date is P i : in Indicates that user i is on date D m The power consumption at time j, P ij represents all the power consumption of user i at time j; In step C, the time for calculating the basic power consumption is determined according to the power consumption data preprocessed in step B, specifically: Calculate the standard deviation of the power consumption of users at different times and coefficient of variation in is the standard deviation of power consumption of user i at time j, is the coefficient of variation of the power consumption of user i at time j; If for time j, And k≠j, or Then the time j is determined as the time for calculating the basic power consumption; In step D, the basic household power consumption is calculated according to the time of calculating the basic power consumption determined in step C, specifically: According to the time j of calculating the basic power consumption determined in step C, the active power corresponding to the time j is input into SPSS for clustering, and the number of cluster centers is set to M. Clustering obtains the number of cluster centers and their corresponding members. If the cluster centers are recorded as {C1, C2, …, C M }, the number of members corresponding to each class is {NU1,NU2,…,NU M }; like And q≠m, C m <C q , then C m is the user's first basic power consumption, and the corresponding support is like And q≠h, C m <C h <C q , then C h is the user's second basic power consumption, and the corresponding support is

Images