KR20220061713A - Method of replacing missing value in smart meter and control system of smart meter using the same - Google Patents

Abstract

Provided are a method of replacing a missing value in a smart meter and a smart meter control system using the same. The method of replacing the missing value in the smart meter comprises: a step of selecting a similar user having sameness with a propensity score based on the propensity score of a user in which the missing value occurs; a step of generating the amount of electricity consumption of the similar user; a step of normalize the pattern to be generated into a vector; and a step of calculating a replaced value replacing the missing value by using the vector.

Description

A method for replacing missing values in a smart meter and a smart meter control system using the same

The present invention relates to a method for replacing missing values in a smart meter and a smart meter control system using the same.

With the spread of smart meters, an advanced metering infrastructure (AMI) environment has been prepared that can monitor and control the power consumption of consumers in real time. Accordingly, various attempts related to the stability of electricity supply and demand such as energy efficiency programs and demand response based on real-time collection and analysis of electricity data became possible. However, in smart meters, missing values or outliers may occur in data due to communication quality and malfunction of communication equipment, which lowers the quality of power data and adversely affects data analysis. there is There is a need for a method to compensate for these data gaps, which inevitably occur, through post-hoc analysis.

Republic of Korea Patent No. 1243982 (published on March 15, 2013)

An object of the present invention is to provide a method for replacing missing values of a smart meter that improves the quality of power data and a smart meter control system using the same.

In order to achieve the task to be solved, the missing value replacement method of the smart meter according to embodiments of the present invention selects a similar user having the sameness as the propensity score based on the propensity score of the user in which the missing value occurs to do; generating a power usage pattern of the similar user; generating a vector by normalizing the pattern; and calculating a replacement value to replace the missing value by using the vector.

In order to achieve the object to be solved, a smart meter control system according to embodiments of the present invention includes: a detection unit for detecting occurrence of a missing value; and a calculating unit that calculates a replacement value to replace the missing value based on the propensity score of the user in which the missing value detected by the detection unit occurs.

According to the embodiments of the present invention, when power consumption is measured through a smart meter, missing data inevitably occurring in a smart meter of any one household is effectively replaced by using the power usage pattern of another similar household, thereby improving energy efficiency and demand response. It is possible to prevent obstacles caused by omission of power data in the implementation of programs, etc.

In order to more fully understand the drawings recited in the Detailed Description, a detailed description of each drawing is provided.
1 is a block diagram illustrating a smart meter control system according to an embodiment of the present invention.
2 is a flowchart illustrating a method of replacing missing values of a smart meter according to an embodiment of the present invention.
3 is a diagram for explaining a process of generating a similar user's power usage pattern in the method of replacing missing values of a smart meter according to an embodiment of the present invention.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed herein are only exemplified for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiment according to the concept of the present invention These may be embodied in various forms and are not limited to the embodiments described herein.

Since the embodiments according to the concept of the present invention may have various changes and may have various forms, the embodiments will be illustrated in the drawings and described in detail herein. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes all modifications, equivalents, or substitutes included in the spirit and scope of the present invention.

Terms such as first or second may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from another, for example without departing from the scope of the inventive concept, a first component may be termed a second component and similarly a second component A component may also be referred to as a first component.

When a component is referred to as being “connected” or “connected” to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle. Other expressions describing the relationship between components, such as "between" and "immediately between" or "neighboring to" and "directly adjacent to", etc., should be interpreted similarly.

The terms used herein are used only to describe specific embodiments, and are not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In this specification, terms such as "comprise" or "have" are intended to designate that a feature, number, step, operation, component, part, or combination thereof described herein exists, but one or more other features It should be understood that it does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present specification. does not

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference numerals in each figure indicate like elements.

1 is a block diagram illustrating a smart meter control system according to an embodiment of the present invention.

Referring to FIG. 1 , the smart meter control system 1000 (which may be named by a different name, such as a smart meter control device, etc.) includes a detector 100 that detects the occurrence of a missing value and a user who has a missing value detected by the detector 100 . Based on the propensity score, it includes a calculator 200 that calculates a replacement value to replace the missing value.

2 is a flowchart illustrating a method of replacing missing values of a smart meter according to an embodiment of the present invention.

Referring to FIGS. 1 and 2 , when the detection unit 100 detects that a missing value has occurred in the smart meter, the power user in which the missing value occurred, and the date and time of occurrence of the missing value may be identified ( S110 ). In the case of power usage data, it is collected through an automated metering infrastructure (AMI), and power usage is recorded at regular time intervals (eg, 15 minute intervals). Here, data measured by the smart meter (eg, power consumption data) is received through a wired/wireless communication network or received through a predetermined input interface and stored in a storage unit (not shown) that can be included in the smart meter control system 100 . can

In this case, the power user in which the missing value is generated is referred to as a user, the time when the missing value is generated is referred to as a missing time point (t), and the day when the missing value is generated is referred to as a missing date (D). If the missing time point (t) is identified, power usage data 24 hours before and 24 hours including the missing time point (t) can be collected. In this case, the time range between 24 hours before and 24 hours after including the missing time point t is referred to as the user's data period.

As an example, looking at Table 1 below, when a missing value occurs at 12:45 (t) on January 2, the power consumption data from 12:45 on January 1 to 12:45 on January 3 collect

[Table 1]

In this case, when other missing values exist within 24 hours before and after the missing time point t, power usage data up to just before the time of the other missing values can be collected.

As an example, in Table 2 below, a missing value occurs at 12:45 (t ₁ ) on January 2, another missing value at 0:15 (t ₂ ) on January 2 and 23:30 on January 2 When another missing value occurs at (t ₃ ), only power consumption data between t ₂ and t ₃ can be collected.

[Table 2]

The calculating unit 200 may obtain a user's propensity score (S120). The propensity score may use household characteristics including residential area, number of household members, housing area, and income decile (or income level), as well as power consumption. In particular, the more the household characteristics, the more accurate the propensity score can be obtained.

In this embodiment, the propensity score may be calculated using logistic regression. See Equation 1 below.

[Equation 1]

이고,

이다. 또한,

는 절편이고,

는 회귀 계수이고,

는 공변량 벡터이며,

는 관찰된 변수이다. 또한, 로지스틱 회귀에서 종속 변수의 경우,

이며 독립 변수의 경우에,

이다.At this time,

ego,

am. In addition,

is the intercept,

is the regression coefficient,

is the covariate vector,

is the observed variable. Also, for the dependent variable in logistic regression,

and for the independent variable,

am.

Hereinafter, a specific method of calculating the propensity score using the characteristics of households using smart meters will be described.

The propensity score calculation method is shown in Equation 2 below.

[Equation 2]

가 0일 경우 조작을 받지 않았으며, 1일 경우 조작을 받은 경우를 의미한다. 단, 스마트 미터가 설치된 시점에서 조작을 받았다고 가정하므로

는 모두 1의 값을 가질 수 있다.

When is 0, no manipulation was received, and when is 1, it means that manipulation was received. However, since it is assumed that the smart meter was operated at the time it was installed,

may all have a value of 1.

는 주택면적을 나타내는 변수를 의미할 수 있다. 구체적으로,

는 미리 정해진 주택면적의 구간에 따라 상이한 값을 가질 수 있다. 즉, 제1 구간의 면적(예컨대, 20평 미만의 주택면적)일 경우

의 값은 s1(예컨대, 1), 제2 구간의 면적(예컨대, 20평 이상 30평 미만)일 경우

의 값은 s2(예컨대, 2), 제3 구간의 면적(예컨대, 30평 이상 40평 미만)일 경우

의 값은 s3(예컨대, 3), 제4 구간의 면적(예컨대, 40평 이상)일 경우

의 값은 s4(예컨대, 4)일 수 있다. 다만, 주택면적을 구분하는 구간의 개수 및/또는 구간의 크기는 실시예에 따라 상이할 수 있다.

may mean a variable representing the housing area. Specifically,

may have different values according to a section of a predetermined housing area. That is, in the case of the area of the first section (eg, housing area of less than 20 pyeong)

When the value of s1 (eg, 1) is the area of the second section (eg, more than 20 pyeong and less than 30 pyeong)

When the value of is s2 (eg, 2), the area of the third section (eg, 30 pyeong or more and less than 40 pyeong)

When the value of is s3 (eg, 3), the area of the fourth section (eg, more than 40 pyeong)

The value of may be s4 (eg, 4). However, the number of sections and/or sizes of sections dividing the housing area may be different according to embodiments.

는 소득수준을 나타내는 변수를 의미할 수 있다. 예컨대, 우리나라 통계청에서 우리나라 전체 가구를 분기 소득수준에 따라 10%씩 10단계로 나눈 지표인 소득분위가 이용될 수 있다.

may mean a variable representing the income level. For example, the income decile, which is an index that divides all Korean households into 10 stages by 10% according to the quarterly income level, can be used by the National Statistical Office of Korea.

는 가구원 수를 의미하는 변수를 의미할 수 있다.

may mean a variable indicating the number of household members.

Using the above three social factors as variables and rearranging them, Equation 3 is shown below.

[Equation 3]

)들을 바탕으로 최대우도추정(Maximum likelihood estimation)을 이용하여 상수값들을 구할 수 있다. 최대우도추정은 모수가 미지의 θ인 확률분포에서 표본

들을 바탕으로 θ을 추정하는 기법이다. 구한 상수값들을 넣어 다시 모델을 만들고 해당 변수의 속성값들을 대입하여 성향 점수를 계산할 수 있다.Observed variable properties (

), constant values can be obtained using maximum likelihood estimation. The maximum likelihood estimate is a sample from a probability distribution whose parameter is an unknown θ.

This is a technique for estimating θ based on these values. The propensity score can be calculated by adding the obtained constant values to create a model again and substituting the attribute values of the corresponding variables.

As in the example above, it is possible to calculate the propensity score of a sample of all power users.

At least one power user having the same propensity score as the user having the missing value (or having the closest value) may be extracted (S130). The extracted power users are called similar users.

Propensity score matching can reduce selection bias by selecting a comparison group that is homogeneous with the experimental group. Selection bias occurs in non-random sampling, but the problem of covariates that can induce tolerance may occur due to the failure of the homogeneous composition of the experimental and control groups.

If the propensity scores are the same, they have the same covariate propensities. The propensity score is shown in Equation 4 below.

[Equation 4]

는 i번째 피 실험자의 관찰된 공변량의 값이고,

일 경우 실험집단에 속하며,

일 경우 비교집단에 속한다.

is the value of the observed covariate of the i-th subject,

In some cases, it belongs to the experimental group,

In this case, it belongs to the comparison group.

During the user's data period, it is checked whether there is a missing value in the power usage data of similar users, and power usage data of similar users having no missing value may be extracted.

3 is a diagram for explaining a process of generating a similar user's power usage pattern in the method of replacing missing values of a smart meter according to an embodiment of the present invention.

2 and 3 , power usage data of similar users may be extracted at 15-minute intervals 24 hours before and after a time point corresponding to the user's missing value. That is, the data of each similar user includes 96 power usage data of the missing day (D) including missing values, 96 power usage data of the day before the missing day (D-1), and the day after the missing day (D+1). 96 pieces of power consumption data may be included.

Each of the extracted median values of the power usage data of similar users may be obtained to form one power usage pattern ( S140 ).

라 한다. 결측치 시점 바로 이전 시점에서의 값은

이라 한다.At this time, in the power usage pattern, the value at the time of the missing value is

say The value at the time point immediately before the missing value is

it is called

The power consumption pattern may be normalized (S150). By normalizing the power usage pattern, a vector is constructed using a total of 96 power usage data values for 24 hours at 15-minute intervals. That is, the data of each similar user is 96 power usage data of the missing day (D) including missing values, 96 power usage data of the day before the missing day (D-1), and the day after the missing day (D+1). By configuring each of the 96 power consumption data as a vector, a total of three vectors (V _D , V _D-1 , V _{D+ 1} ) can be generated. That is, a total of three one-dimensional vectors having 96 elements can be formed.

및

을 각각 구할 수 있다. 예시적으로,

는 하기의 식 5로 구할 수 있다.According to an embodiment, using Min-Max Normalization, the normalized value at the time of the missing value of the power usage pattern is used,

and

each can be obtained. Illustratively,

can be obtained by the following Equation 5.

[Equation 5]

를

으로 나눈 값

(

)를 사용자의 결측치가 발생한 시점의 바로 전 시점(t-1)의 전력사용량 값에 곱한 값을 사용자의 결측치로 대체할 수 있다(S160).next,

cast

divided by

(

) multiplied by the power consumption value of the time t-1 just before the point in time when the user's missing value occurs may be replaced with the user's missing value (S160).

In this way, when measuring electricity consumption through a smart meter, the data missing values that inevitably occur in the smart meter of any one household are effectively replaced by using the electricity consumption pattern of other similar households, thereby improving energy efficiency and implementing demand response programs. It is possible to prevent obstacles caused by omission of power data.

The device described above may be implemented as a hardware component, a software component, and/or a set of hardware components and software components. For example, the devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a Programmable Logic Unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications executed on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other Processing Configurations are also possible, such as a Parallel Processor.

The software may include a computer program, code, instructions, or a combination of one or more thereof, and configure the processing device to operate as desired or independently or collectively processed You can command the device. The software and/or data may be any type of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or provide instructions or data to the processing device. , or may be permanently or temporarily embodied in a transmitted signal wave (Signal Wave). The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer readable medium may include program instructions, data files, data structures, etc. alone or preferably. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical recording media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - Includes hardware devices specially configured to store and execute program instructions, such as Magneto-optical Media, ROM, RAM, Flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

Although the present invention has been described with reference to the embodiment shown in the drawings, which is only exemplary, those skilled in the art will understand that various modifications and equivalent other embodiments are possible therefrom. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result. Accordingly, the true technical protection scope of the present invention should be determined by the technical spirit of the appended claims.

Claims (7)

selecting a similar user having the same value as the propensity score based on a propensity score of the user in which the missing value has occurred;
generating a power usage pattern of the similar user;
generating a vector by normalizing the pattern; and
and calculating a replacement value to replace the missing value by using the vector. According to claim 1,
The propensity score uses electricity usage, residential area, number of household members, housing area, and income decile,
How to replace missing values in smart meters. According to claim 1,
The generating of the power usage pattern of the similar user comprises:
Comprising the step of obtaining the power consumption value of the similar user at 15-minute intervals for a period from 24 hours to 24 hours after the missing value occurs,
When there are a plurality of similar users, an average value of power consumption values is used,
How to replace missing values in smart meters. According to claim 1,
The pattern is normalized using min-max normalization,
How to replace missing values in smart meters. According to claim 1,
Calculating a replacement value to replace the missing value using the vector comprises:
obtaining a third value by dividing the normalized first value of the similar user corresponding to the missing value time point by the second normalized value of the similar user immediately before the missing value time point; and
obtaining the replacement value by multiplying the third value by the power consumption value of the user immediately before the missing value time,
How to replace missing values in smart meters. a detection unit for detecting occurrence of missing values; and
and a calculator configured to calculate a replacement value to replace the missing value, based on the propensity score of the user in which the missing value detected by the detection unit occurs. 7. The method of claim 6,
The calculation unit,
Calculate the propensity score of the user in which the missing value is generated,
selecting a similar user having the same as the propensity score of the user;
generating a power usage pattern of the similar user,
Normalizing the pattern to generate a vector,
calculating a replacement value to replace the missing value using the vector,
Smart meter control system.

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