KR101867222B1 - Non-Intrusive Appliance Load Monitoring Method and Device using Influence of Complex Sensors on State of Appliance - Google Patents

Abstract

Provided are an appliance identification method and an appliance identification apparatus and recording medium using the same. According to the appliance identification method, by using sensor data which are measurement values sensed by a plurality of sensors installed in a space in which at least one appliance is included, state information based on the sensor data, which is information predicting a probability of each state of each appliance, can be calculated and power usage state information of at least one appliance can be predicted using the calculated state information based on the sensor data, thereby improving an appliance identification rate using information on total amount of power, information on a complex sensor, and an impact value of the appliance and the information on the complex sensor.

Description

TECHNICAL FIELD [0001] The present invention relates to a method and apparatus for identifying non-contact electric home appliances based on the state of a state of a composite sensor,

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to energy management technology, and more particularly, to a method, an apparatus, and a recording medium for identifying a power usage state of a home appliance.

In order to reduce energy consumption and control intelligently, it is necessary to know the power consumption and usage of the energy device and its usage status.

However, in general households, only the total energy consumption (cumulative power) of the customer is known through the distribution board, and this information alone can not tell which device is being used.

Therefore, it is necessary to install a smart plug for each energy device, and the cost of the smart plug is relatively high, which causes a cost problem.

In order to solve such a cost problem, research on a non-intrusive appliance load monitoring (NIALM) based on the total power consumption has recently been conducted. However, the non-contact identification method has a disadvantage that the identification rate of the energy device is lowered.

It is required to search for a method for providing a method of grasping the power usage state of household appliances which ensures a more accurate identification rate at low cost.

SUMMARY OF THE INVENTION The present invention has been made in order to solve the above-mentioned problems, and it is an object of the present invention to provide a home appliance, A method of identifying the at least one household electric appliance using the calculated sensor data based state information and a method of identifying the home appliance using the calculated sensor data based state information, A device identification device, and a recording medium.

Another object of the present invention is to provide a method and apparatus for measuring the probability of each home appliance by using sensor data that are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, Based on the calculated sensor data base state information and the previously calculated home appliance state information, calculates household appliance state information including probability values to be applied to each state of each of the household appliances , It is predicted that the state corresponding to the highest probability value among the probability values of each state of the calculated home appliance status information is the current state of the home appliance and the power consumption value corresponding to the predicted current state is applied, Providing an appliance identification method for predicting power usage status information of at least one household appliance by calculating current consumption power It is.

According to another aspect of the present invention, there is provided a method for identifying a home appliance, including the steps of: using sensor data, which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, A first calculation step of calculating sensor data based state information, which is information predicting a probability of each state of the home appliance; And estimating power usage state information of at least one household appliance using the calculated sensor data base state information.

The prediction step may predict the power usage state information of at least one home appliance using the calculated sensor data base state information and the previously calculated home appliance state information together.

And a second calculating step of calculating total power based state information which is information for predicting a probability of each state of at least one household electric appliance by using a variation pattern of the total amount of electric power consumption, The power usage state information of at least one household appliance may be predicted using the power based state information, the calculated sensor data based state information, and the previously calculated home appliance state information together.

The prediction step may further include a value obtained by multiplying the calculated total power based state information by a first weight, a value obtained by multiplying the calculated sensor data based state information by a second weight, and a third weight And the state maintaining probability are all added to calculate the household appliance state information indicating the probability value for each state of the household appliance to predict the power use state information of at least one household appliance.

Also, the predicting step may include calculating household appliance state information including probability values corresponding to each state of all states of the home appliance, calculating a state corresponding to a highest probability value among the probability values of each state of the home appliance state information, The power consumption state information of the at least one household appliance may be predicted by estimating that the current state of the home appliance is the current state and calculating the current consumption power of the home appliance by applying the power consumption value corresponding to the predicted current state .

The values of the first weight, the second weight, and the third weight may vary depending on the type of the space including at least one home appliance, the number of sensors, and the distribution of the sensors.

The first calculating step calculates the sensor data based state information using the influence degree matrix showing the degree of the measurement value of each sensor in the state prediction of each of the home appliances, You may.

In the first calculation step, only the measurement values exceeding the specific threshold value among the measurement values sensed by the plurality of sensors installed in the space including at least one home appliance are used as valid sensor data, The sensor data based state information which is information that predicts the probability of each state of the sensor data.

In addition, the sensors may include at least two of an acoustic sensor, an illuminance sensor, and a temperature sensor.

According to another embodiment of the present invention, there is provided a home appliance identification apparatus comprising: a receiver for receiving sensor data, which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance; And sensor data based on the calculated sensor data based on the calculated sensor data based on the calculated sensor data based state information and estimating the power use state information of at least one home appliance by using the calculated sensor data based state information, And a processor.

According to another aspect of the present invention, there is provided a computer-readable recording medium for recording data on a plurality of home appliances using sensor data, which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, A first calculation step of calculating sensor data based state information, which is information predicting probability of each device state; And estimating power usage status information of at least one household appliance using the calculated sensor data based status information.

According to another embodiment of the present invention, there is provided a method for identifying a home appliance, comprising the steps of: using sensor data, which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, A first calculation step of calculating sensor data based state information which is information predicting a probability of occurrence; And calculates the home appliance state information including the probability values to be applied to each state of all the home appliances using the calculated sensor data base state information and the previously calculated home appliance state information and outputs the calculated home appliance state information Estimates that the state corresponding to the highest probability value among the probability values of each state of the home appliance is the current state of the home appliance and calculates the current power consumption of the home appliance by applying the power consumption value corresponding to the predicted current state, And predicts the power usage state information of at least one home appliance.

According to various embodiments of the present invention, sensor data, which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, It is possible to provide a household appliance identification method for estimating the power usage state information of at least one household appliance by calculating the data base state information and using the calculated sensor data based status information, , The combined power information, the combined sensor information, and the influence value of the home appliance and the hybrid sensor information can be used to improve the identification ratio of the home appliances.

FIG. 1 is a conceptual diagram of a method of identifying a home electric appliance according to an embodiment of the present invention,
2 is a view showing a power consumption pattern for each of the household appliances,
3 is a diagram illustrating a power consumption pattern for a combination of home electric appliances;
FIG. 4 is a block diagram of a home appliance identification apparatus according to an embodiment of the present invention,
FIG. 5 is a flowchart illustrating a method of identifying a home appliance according to an exemplary embodiment of the present invention;
FIG. 6 is a graph showing influences of a home appliance and a sensor according to an embodiment of the present invention,
FIG. 7 is a schematic view illustrating a process of obtaining an influence matrix between a home appliance and a sensor according to an embodiment of the present invention; FIG.
FIG. 8 is a diagram illustrating a process of applying a threshold value to a sensor measurement value according to an embodiment of the present invention, and FIG.
9 is a diagram illustrating a concept of state retention probability of a home appliance according to an embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the drawings.

1 is a conceptual diagram of a method of identifying a home electric appliance according to an embodiment of the present invention.

According to the household appliance identification method according to the embodiment of the present invention, the home appliance identification apparatus 100 does not use contact devices such as smart outlet or smart plug, And the power usage status information of at least one household electric appliance is predicted using the calculated appliance status information together.

In this case, the total power-based state information indicates a method of predicting the current household appliances from the total power consumption amount. For example, the non-intrusive appliance load monitoring (NIALM) based on the total power consumption have. The home appliance identification apparatus 100 calculates the total power based state information by applying the device use identification algorithm through the power total amount based device identification measured through the power meter of the distribution board shown in FIG.

In addition, the sensor data based status information is information that predicts the probability of each of the home appliances using the sensor data obtained by using the measured values of various sensors of the hybrid sensor. The home appliance identification apparatus 100 is a system for detecting the state of use of a complex sensor-based device (acoustic home appliance, electrothermal home appliance, lighting appliance, etc.) measured through a composite sensor including the acoustic sensor, the illuminance sensor and the temperature sensor shown in FIG. And the sensor information based state information is calculated by applying the device use identification algorithm.

Accordingly, the home electric apparatus identifying apparatus 100 can use the total electric power based state information, the sensor data based state information, and the previously calculated home electric appliance state information together to calculate the electric power consumption of each of the household appliances (audio, set top, electric heater, (On, off, strong, medium, weak, etc.)

Prediction of the power consumption per household appliance through the household appliance identification method can be expressed as the total power consumption [P (t)] of the household appliances collected from the power meter of the distribution board during a specific time period t, To the consumed electric power (p _i ) per household appliance to predict the household appliances currently in use.

_{P (t) = p 1 (} t) + p 2 (t) + p 3 (t) ... + p m (t)

The above equation represents the total power consumption [P (t)] divided by the individual power consumption of m household appliances and expressed as a sum value.

The home appliance identification apparatus 100 uses the learning results of the power consumption patterns of the home appliances in order to calculate the total power based state information. Fig. 2 is a diagram showing a power consumption pattern for each of household appliances, and Fig. 3 is a diagram illustrating a power consumption pattern for a combination of household appliances. FIG. 2 shows a power consumption pattern learning process for audio, set-top box, electric heater, lighting, and TV. As shown in FIG. 3, the home appliance identification apparatus 100 can combine various home appliances, learn additional power consumption patterns, and use the results further. FIG. 3 illustrates a process of learning a power consumption pattern in a case of combining "illumination + electric heater + illumination". By using and learning the power consumption pattern of such home appliances, the home appliance identification apparatus 100 calculates the total power based state information.

In addition, the home appliance identification apparatus 100 uses sensor data, which are measured values sensed by a plurality of sensors installed in a space including home appliances, to calculate a sensor data base And calculates status information. The sensor data consists of a combination of acoustic information, illuminance information and temperature information, and sensors for collecting such information are needed as shown in FIG.

However, the above listing of the information constituting the sensor data is merely illustrative. It is, of course, possible to implement sensor data by excluding at least one of them, replacing at least one with other information, or including at least one other information.

Hereinafter, the method of identifying the household appliance of the home appliance identification apparatus 100 will be described in detail. FIG. 4 is a block diagram of a home appliance identification apparatus 100 according to an embodiment of the present invention.

The home appliance identification apparatus 100 according to an embodiment of the present invention includes a receiving unit 110, a processor 120, a storage unit 130, and an output unit 140, as shown in FIG.

The power meter 10 measures the total power consumption of a space including at least one household appliance. The composite sensor 20 includes at least two of an acoustic sensor, an illuminance sensor, and a temperature sensor, and is installed in a space including at least one household appliance to generate sensor data serving as a basis for sensor data based status information.

The receiving unit 110 receives the total power consumption value from the power meter 10 and receives sensor data from the composite sensor 20. [ The receiving unit 110 can receive the total power consumption value and the sensor data by wire or wirelessly.

The processor 230 executes the household appliance identification method shown in Fig. The processor 230 may output the execution result through the output unit 250. [

The storage unit 240 stores a power consumption pattern learning result database, past sensor data, total power consumption, and an influence matrix to be described later, and previously calculated home appliance status information.

The home appliance identification device 100 shown in FIG. 4 may be implemented as a device that is physically independent of itself, as a part of a certain device or system, or may be implemented as a program installed in a computer, a server, or the like But may be implemented in software such as a framework or an application. In addition, each component of the home appliance identification device 100 may be implemented as a physical component or as a functional component of the software.

Hereinafter, with reference to Fig. 5, a method of identifying the home electric appliance by the home electric appliance identifying apparatus 100 will be described in detail. 5 is a flowchart illustrating a method of identifying a home appliance according to an exemplary embodiment of the present invention.

The home appliance identification apparatus 100 uses sensor data that are measured values sensed by a plurality of sensors installed in a space including at least one home appliance to calculate sensor data Based status information (S210).

At this time, the home electric apparatus identifying apparatus 100 uses the influence degree matrix indicating the degree of the measurement value of each sensor in the state prediction of each home appliance according to each state of each sensor for each home appliance, Information is calculated.

The influence degree matrix will be described with reference to FIG. 6 and FIG.

6 is a graph showing influences of a home appliance and a sensor according to an embodiment of the present invention.

The home appliance identification apparatus 100 first generates an influence degree graph of the home appliance and the complex sensor. A change in the state of the appliance affects the environment of the surrounding space. For example, a lighting device may be illuminated when turned on and dimmed when turned off, so that the condition can be detected by the illuminance sensor. In addition, since a sound is emitted when the TV or sound device is turned on and no sound is emitted when the sound is turned off, it is possible to detect the state by the acoustic sensor. Thus, the composite sensor 20 collects sensor data that measures such environmental change. Then, the home appliance identification apparatus 100 expresses the learned result based on the collected sensor data in the form of an influence graph shown in FIG. 6 and stores the result in the storage unit 130. Here, the influence graph is information representing the degree of change of the device state corresponding to the change of the surrounding environment by defining the state of the device corresponding to the user's behavior.

The influence graph expresses the relationship between the state of the appliance and the sensor data which detects the change of the surrounding environment. One independent influence graph is generated for each appliance. If each sensor value does not affect the instrument state change (subgraph with e = 0), do not create it.

The influence graph represents a sensor (circle in Fig. 6) that affects the state change according to the state of the home appliance (bold square in Fig. 6) (the general square in Fig. 6). If the value measured through the sensor affects a particular instrument condition, define the degree as e. The home appliance identification apparatus 100 generates an independent influence graph for all home appliances and stores the graph in the storage unit 130. [

The home appliance identification apparatus 100 generates an influence degree matrix using the influence degree graph, which will be described with reference to FIG. FIG. 7 is a diagram schematically illustrating a process of obtaining an influence matrix between a home appliance and a sensor according to an embodiment of the present invention. Referring to FIG.

As shown in FIG. 7, the home appliance identification apparatus 100 separates a given influence graph into a subgraph of degree = 1. The separated subgraph consists of one sensor corresponding to the appliance state. Then, the home appliance identification apparatus 100 inputs the influence values corresponding to the K states in the i-th row and the j-th column corresponding to the index of the home appliance and the sensor corresponding to the subgraph in the form of a vector. Here, i denotes the i-th home appliance, j denotes the j-th sensor, and K denotes the total number of types of household appliances. Enter 0 for the element of the index where the subgraph does not exist. The home appliance identification apparatus 100 generates an influence degree matrix E upon completion of generation of an influence degree graph generated from all home appliances. The home appliance identification apparatus 100 uses the influence matrix E indicating the degree of the measurement value of each sensor to the state prediction of each home appliance according to each state of each sensor for each home appliance, The information A (t) is calculated. This process can be expressed in concrete terms as follows.

The sensor data base state information A (t) can be expressed by Equation (1) and Equation (2) using the influence degree matrix E generated by the influence degree graph and the sensor data x of the measured equation (4) . Equation (3) represents the value of each element of the influence matrix E. Here, the total number of state types of household appliances is K, m is the number of household appliances, and n is the number of sensors. And, the influence degree matrix E is a matrix of m 占 n, and each element is a vector including K influence degree values.

가 된다. Specifically, the sensor data values measured at each composite sensor 20 at time t are stored in the x (t) vector of Equation (4). Therefore, when the set of all the sensor modules included in each composite sensor 20 is S _i , the vector of the measured sensor data is

.

In addition, the home electric apparatus identifying apparatus 100 uses only the measured values that exceed the specific threshold among the measured values sensed by the plurality of sensor modules installed in the space including at least one home appliance as effective sensor data. Specifically, the home appliance identification apparatus 100 determines whether the value measured by the sensor module exceeds a threshold for influencing the state of the home appliance, ) Can be defined and used. Applying the activation function to the sensor data corresponds to Equation (5), and y in Equation (5) represents the result of applying the activation function f to the sensor data.

In addition, since the sensor values measured in the respective sensor modules include the characteristics (temperature: ° C, sound: dB) of the different sensor modules, the home appliance identification apparatus 100 performs the process of normalizing the values , Which is included in Equation (5).

)으로 정규화하여 최종 결과 벡터 y를 산출하게 된다. That is, as shown in Equation (5), the home appliance identification apparatus 100 applies the activation function according to the threshold value given to the measured value (x _i (t)) of each sensor. The result is stored in the sensor measurement maximum value (

) So as to calculate the final result vector y.

FIG. 8 is a diagram illustrating a process of applying a threshold to a sensor measurement value according to an exemplary embodiment of the present invention. Referring to FIG. As shown in FIG. 8, the home appliance identification apparatus 100 uses a function f, which is an activation function, to determine a specific threshold value among measured values sensed by a plurality of sensor modules installed in a space including at least one home appliance ≪ / RTI > are used as valid sensor data. Specifically, FIG. 8 shows a process of extracting a final result vector y using the measured sensor data x (t). In Fig. 8, the activation function is defined as a max function. The activation function can be defined differently depending on the characteristics of the sensor and the home appliance. A (t) can be calculated as Equation (1) by applying the degree e that affects the state of the appliance to the y vector thus extracted.

However, the use of the max function as an activation function is merely an example, and the home appliance identification apparatus 100 can apply a threshold value to the sensor data using various other functions (e.g., a stair function, etc.) Of course.

Through the above process, the home appliance identification apparatus 100 uses the sensor data, which are the measurement values sensed by the plurality of sensors installed in the space including at least one home appliance, And the sensor data based state information, which is predicted information, is calculated.

Referring back to FIG. 5, the home appliance identification apparatus 100 calculates total power based state information, which is information for predicting the probability of each at least one home appliance using the change pattern of the total consumption power (S220).

As described above, the total power-based state information represents state information calculated by a method of predicting what kind of household appliances currently in use from the total power consumption amount. The non-intrusive method (NIALM: Appliance Load Monitoring). The home appliance identification apparatus 100 can calculate the total power based state information by applying the non-contact identification method based on the total amount of power consumption based on the total amount of power measured through the power meter 10 of the distribution board. At this time, the calculated total power based state information is represented by D (t).

Then, the home electric apparatus identifying apparatus 100 multiplies the calculated total power based state information D (t) by the first weight ω ₁ and the value obtained by multiplying the calculated sensor data base state information A (t) by the second weight ω ₂ And the value obtained by multiplying the previously calculated home appliance state information S (t-1) by the third weight? ₃ and the state maintaining probability? Are added to each other to calculate home appliance state information S (t) (S230). Here, the home appliance state information S (t) is a value including all the probabilities of all the states to which the home appliance can correspond, and corresponds to a matrix expressed by the following equation (6).

(6)

(6)

(7)

(7)

Here, all the elements of S (t), D (t), A (t) and S (t-1) are m × K columns, Represents a probability value. The total number of state types of the household appliances is K, m is the number of household appliances, and n is the number of sensors.

The first weight ω ₁ , the second weight ω _2, and the third weight ω ₃ may vary depending on the type of the space including at least one home appliance, the number of sensors, and the distribution of the sensors. For example, the home appliance identification apparatus 100 determines whether the space in which the household appliances are housed is a company space, an apartment space, or a house space, based on the first weight ω ₁ , the second weight ω _2, and the third weight ω ₃ Can be set. In addition, the home appliance identification apparatus 100 can be set such that the second weight? ₂ becomes larger as the number of sensors increases or the distribution density of the sensor increases. Many, the number of sensors, or so that the distribution density of the sensor information by the high sensor accuracy is relatively high, the appliance identification device 100 first weights ω ₁ and a second weight ω ₂ and the third weighting ω ₃ of the And the second weight? ₂ , which is a weight related to the sensor, is set higher.

The state retention probability pi indicates a probability that the state of the previous home appliance state information S (t-1) is maintained as it is. That is, since the probability that the previous home appliance state information S (t-1) will remain unchanged as the state maintaining probability? Increases, the previous home appliance state information S (t-1) is multiplied by the state maintaining probability? If the appliance is a type of household appliance that is kept in one state, it is possible to increase the weight of the previous appliance state information by multiplying the state maintaining probability? By the old appliance state information S (t-1). Therefore, the state retention probability pi varies according to the state of each appliance, and the household appliance identification apparatus 100 obtains the state retention probability pi by state of each appliance.

9 is a diagram illustrating a concept of state retention probability of a home appliance according to an embodiment of the present invention.

이다. 이전 가전 기기의 상태에 상태 변경 확률을 적용하여 현재 가전 기기 상태 식별 예측값에 반영한다. The state maintaining probability? Of the home appliance can be defined in the manner shown in FIG. 9 to express the degree to which the state of the previous home appliance affects the state of the current household appliance. The state retention probability pi, which is the probability that the state of the home appliance will not change

to be. Apply the state change probability to the state of the previous home appliance and reflect it in the current household appliance state identification prediction value.

Referring again to FIG. 2, the home appliance identification apparatus 100 calculates home appliance state information including probability values to be applied to each state of all home appliances, calculates probability values for each state of the home appliance state information It is predicted that the state corresponding to the highest probability value of the home appliances is the current state of the home appliance (S240).

Specifically, when the appliance state information S (t) at the time t is finally calculated, the home appliance identification apparatus 100 determines the state of the largest probability value for each appliance as shown in the following equation (8) It is determined that the current state is present.

(8)

(8)

The home appliance identification apparatus 100 can estimate the power consumption state information of at least one home appliance by calculating the current consumption power of the home appliance by applying the power consumption value corresponding to the predicted current state. Here, the power use state information is information indicating the power consumption amount of each appliance, and is information that is calculated by extracting the amount of power consumption corresponding to the current state of the home appliance predicted through the appliance state information. The home appliance identification device 100 calculates the power use state information of each home appliance at every cycle. The user checks the power usage state information calculated by the home appliance identification device 100, It is possible to confirm at a glance whether the consumption is severe. In addition, since the home electric appliance identifying apparatus 100 predicts the power use state information by using both the total power based state information, the sensor data based state information, and the previously calculated home appliance state information, Power consumption can be confirmed.

On the other hand, in the present embodiment, the home electric apparatus identifying apparatus 100 uses the calculated total power based state information, the calculated sensor data based state information, and the previously calculated home appliance state information together to use the electric power of at least one household appliance It is also possible to predict the power use state information of at least one home appliance using only the calculated sensor data base state information and to use the calculated sensor data base state information and the previously calculated power use state information The power usage state information of at least one household electric appliance may be predicted using the state information together with the electric power consumption state information of the at least one household appliance using the calculated total power based state information and the calculated sensor data based state information, Of course, be predicted.

It is needless to say that the technical idea of the present invention can also be applied to a computer-readable recording medium containing a computer program for performing the functions of the home appliance identification apparatus 100 and the home appliance identification method according to the present embodiment . In addition, the technical idea according to various embodiments of the present invention may be realized in the form of a computer-readable programming language code recorded on a computer-readable recording medium. The computer-readable recording medium is any data storage device that can be read by a computer and can store data. For example, the computer-readable recording medium may be a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, an optical disk, a hard disk drive, a flash memory, a solid state disk (SSD), or the like. In addition, the computer readable code or program stored in the computer readable recording medium may be transmitted through a network connected between the computers.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments, but, on the contrary, It will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the present invention.

10: Power Meter
20: Composite sensor
100: Home appliance identification device
110:
120: Processor
130:
140:

Claims (12)

Calculating a sensor data based state information which is information predicting a probability of each home appliance by using sensor data which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, Calculating step; And
And estimating power usage state information of at least one home appliance using the calculated sensor data base state information,
And a second calculating step of calculating total power based state information, which is information for predicting a probability of each at least one household electric appliance by using a variation pattern of the total amount of electric power consumption,
In the prediction step,
A value obtained by multiplying the calculated total power based state information by the first weight, a value obtained by multiplying the calculated sensor data based state information by the second weight, and a value obtained by multiplying the previously calculated home appliance state information by the third weight and the state retention probability Values of all of the household appliances are added to calculate the household appliance status information indicating the probability value of each appliance in the state to predict the power use state information of at least one household appliance.
delete delete delete The method according to claim 1,
In the prediction step,
The state information corresponding to the highest probability value among the probability values for each state of the calculated home appliance status information is calculated based on the current state of the home appliance And estimates the power consumption state information of at least one household appliance by calculating the current consumption power of the home appliance by applying the power consumption value corresponding to the predicted current state, Way.
The method according to claim 1,
The values of the first weight, the second weight,
Wherein the at least one home electric appliance varies depending on the type of the space including at least one home appliance, the number of sensors, and the distribution of the sensors.
The method according to claim 1,
The first calculating step includes:
Wherein the state information based on the sensor data is calculated by using the influence degree matrix showing the degree of the measurement value of each sensor in the state prediction of each home appliance by the state of each sensor for each appliance, Way.
The method according to claim 1,
The first calculating step includes:
Only the measured values exceeding a specific threshold value among the measured values sensed by the plurality of sensors installed in the space including at least one home appliance are used as valid sensor data, Based on the sensor data based on the sensor data.
The method according to claim 1,
The sensors,
An acoustic sensor, an illuminance sensor, and a temperature sensor.
A receiving unit for receiving sensor data, which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance; And
Sensor data based state information, which is information that predicts the probability of each home appliance by using the sensor data, and predicts power use state information of at least one home appliance using the calculated sensor data based state information And a processor,
The processor,
Calculating a total power based state information which is information for predicting a probability of each of at least one household electric appliance by using a change pattern of the total amount of power consumption, calculating a value obtained by multiplying the calculated total power based state information by a first weight, The value obtained by multiplying the sensor data based state information by the second weight and the value obtained by multiplying the previously calculated home appliance state information by the third weight and the state maintaining probability are all added to obtain home appliance status information representing the probability value of each appliance And predicts the power usage state information of at least one household electric appliance.
Calculating a sensor data based state information which is information predicting a probability of each home appliance by using sensor data which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, Calculating step; And
And estimating power usage state information of at least one home appliance using the calculated sensor data base state information,
And a second calculating step of calculating total power based state information, which is information for predicting a probability of each at least one household electric appliance by using a variation pattern of the total amount of electric power consumption,
In the prediction step,
A value obtained by multiplying the calculated total power based state information by the first weight, a value obtained by multiplying the calculated sensor data based state information by the second weight, and a value obtained by multiplying the previously calculated home appliance state information by the third weight and the state retention probability Values of all the household appliances are added to calculate the household appliance state information indicating the probability value of each appliance in the state to predict the power use state information of at least one household appliance. . ≪ / RTI >
Calculating a sensor data based state information which is information predicting a probability of each home appliance by using sensor data which are measured values sensed by a plurality of sensors installed in a space including at least one home appliance, Calculating step; And
The home appliance status information including the probability values to be applied to each state of all the home appliances is calculated using the calculated sensor data base state information and the previously calculated home appliance status information, Estimating that the state corresponding to the highest probability value among the probability values for each state is the current state of the home appliance and calculating the current power consumption of the home appliance by applying the power consumption value corresponding to the predicted current state, And predicting power usage status information of one of the household appliances,
And a second calculating step of calculating total power based state information, which is information for predicting a probability of each at least one household electric appliance by using a variation pattern of the total amount of electric power consumption,
In the prediction step,
A value obtained by multiplying the calculated total power based state information by the first weight, a value obtained by multiplying the calculated sensor data based state information by the second weight, and a value obtained by multiplying the previously calculated home appliance state information by the third weight and the state retention probability Values of all of the household appliances are added to calculate the household appliance status information indicating the probability value of each appliance in the state to predict the power use state information of at least one household appliance.

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