Apparatus and method for classifying home appliances based on power consumption using deep learning

1. An apparatus for classifying home appliances based on power consumption using deep learning, the apparatus comprising:

one or more modules and units being configured and executed by a processor using an algorithm associated with at least one non-transitory storage device, the one or more modules and units comprising:

a home appliance classification model creation module configured to encode power consumption data collected from a customer's house to learn a home appliance classification model and create a recurrent neural network (RNN)-based home appliance classification model; and

a home appliance classification module configured to collect and encode data on power consumption currently in use, classify the home appliances using the home appliance classification model created by the home appliance classification model creation module, and output a result of the classification, wherein

the home appliance classification model creation module is configured to collect encoding power consumption data from a customer's house to learn the home appliance classification model and to create an RNN-based home appliance classification model,

to collect and to encode data on power consumption currently in use, and

to classify the home appliances using the RNN-based home appliance classification model, and to output a result of the classification, wherein

the collection of encoding power consumption data from a customer's house to learn the home appliance classification model and the creation of the RNN-based home appliance classification model is performed using the following algorithm:

a number of the home appliances are represented as N;

when each of the home appliances in the set is represented as app i (1≤i≤N), the set of home appliances is represented as

Appliance={app 1 , app 2 , app 3 , . . . app N };

and

when the collected power consumption is represented as P, and the maximum power consumption of each home appliance is represented as MAX(app i ), the encoded power consumption is

Encoded

⁢

⁢

P

=

P

∑

i

=

1

N

⁢

MAX

⁡

(

app

i

)

,

and wherein

the home appliance classification model creation module is configured to measure a variation while updating a base value of the power consumption with a low pass filter and to encode a value of measured total power consumption according to the variation.

2. The apparatus of claim 1 , wherein the home appliance classification model creation module comprises:

a learning data input unit configured to input the power consumption data collected from the customer's house;

a data encoding unit configured to encode the input power consumption data;

a home appliance classification model learning unit configured to learn the home appliance classification model using the encoded power consumption data; and

a home appliance classification model creation unit configured to input new learning data in response to detection of a learning error being greater than or equal to a threshold and to stop the learning and to create the home appliance classification model in response to detection of the learning error being less than the threshold.

3. The apparatus of claim 1 , wherein the home appliance classification module comprises:

a power consumption information collection unit configured to collect the data on power consumption currently in use;

a power consumption information input unit configured to input the collected power consumption data;

a data encoding unit configured to encode the input power consumption data;

a home appliance classification unit configured to classify the home appliances based on the encoded data; and

a classification result output unit configured to output the result of the classification of the home appliances.

4. A method of classifying home appliances based on power consumption using deep learning, the method comprising:

encoding power consumption data collected from a customer's house to learn a home appliance classification model and creating an RNN-based home appliance classification model; and

collecting and encoding data on power consumption currently in use,

classifying the home appliances using the RNN-based home appliance classification model, and outputting a result of the classification, wherein

in the encoding of the input power consumption information included in the creating of the RNN-based home appliance classification model:

a set of the home appliances of the customer's house is represented as Appliance;

a number of the home appliances are represented as N;

when each of the home appliances in the set is represented as app i (1≤i≤N), the set of home appliances is represented as

Appliance={app 1 , app 2 , app 3 , . . . app N };

and

when the collected power consumption is represented as P, and the maximum power consumption of each home appliance is represented as MAX(app i ), the encoded power consumption is

Encoded

⁢

⁢

P

=

P

∑

i

=

1

N

⁢

⁢

MAX

⁡

(

app

i

)

,

and wherein

the encoding of the input power consumption information included in the creating of the RNN-based home appliance classification model comprises measuring a variation while updating a base value of the power consumption with a low pass filter and encoding a value of measured total power consumption according to the variation.

5. The method of claim 4 , wherein the creating of the RNN-based home appliance classification model comprises:

inputting the power consumption information collected from the customer's house;

encoding the input power consumption data;

learning a home appliance classification model using the encoded power consumption data; and

inputting new learning data in response to detection of a learning error being greater than or equal to a threshold and stopping the learning and creating the home appliance classification model in response to detection of the learning error being less than the threshold.

6. The method of claim 4 , wherein the classifying of home appliances and the outputting of a result of the classification comprises:

collecting data on power consumption currently in use;

inputting the collected power consumption data;

encoding the input power consumption data;

classifying the home appliances using the RNN-based home appliance classification model based on the encoded data; and

outputting the result of the classification of the home appliances.

7. The method of claim 4 , wherein, in the encoding of the input power consumption information included in the creating of the RNN-based home appliance classification model, an output at time t is used as an input at time t+1 in order to increase a learning rate for a home appliance use pattern.

8. The method of claim 4 , wherein, in the classifying of the home appliances using the RNN-based home appliance classification model and the outputting of the result of the classification,

input data is composed of a previous output and data collected from a sensor installed in the customer's house, in which the previous output is binary data that is represented as one bit stream, converted into a decimal number, and then input; and

output data is composed of binary data that indicates an on/off state of a home appliance.

9. The method of claim 4 , wherein, in the classifying of the home appliances using the RNN-based home appliance classification model and the outputting of the result of the classification,

input data is power consumption collected from a sensor installed in the customer's house or a variation of power consumption that is encoded using a low pass filter;

an output node is one of combinations of the home appliances in the customer's house; and

only one combination having the highest probability is selected without needing to set a criterion for determining on/off states when the model classifies the home appliances.