Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Realization of home appliance classification system using deep learning

딥러닝을 이용한 가전제품 분류 시스템 구현

  • Son, Chang-Woo (Department of Electronic & Communication Eng, Korea Maritime University) ;
  • Lee, Sang-Bae (Department of Electronic & Communication Eng, Korea Maritime University)
  • Received : 2017.06.20
  • Accepted : 2017.08.24
  • Published : 2017.09.30
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Abstract

Recently, Smart plugs for real time monitoring of household appliances based on IoT(Internet of Things) have been activated. Through this, consumers are able to save energy by monitoring real-time energy consumption at all times, and reduce power consumption through alarm function based on consumer setting. In this paper, we measure the alternating current from a wall power outlet for real-time monitoring. At this time, the current pattern for each household appliance was classified and it was experimented with deep learning to determine which product works. As a result, we used a cross validation method and a bootstrap verification method in order to the classification performance according to the type of appliances. Also, it is confirmed that the cost function and the learning success rate are the same as the train data and test data.

최근 IoT기반으로 가전제품을 실시간 모니터링을 하는 스마트 플러그가 활성화 되고 있다. 이를 통해 상시 실시간 에너지 소비 모니터링을 통한 소비자의 에너지 절약 유도를 하고, 소비자 설정 기반의 알람 기능을 통해 소비전력을 절감하는 효과를 보고 있다. 본 논문에서는 이러한 실시간 모니터링을 위해 벽 전원 콘센트에서 나오는 교류 전류를 측정한다. 이때, 가전제품마다의 전류 패턴을 분류하고 어떤 제품이 동작하는지 판단을 위해 딥러닝(Deep learning)으로 실험하였다. 전류 패턴의 학습으로 제품의 종류에 따른 인식 성능을 검증하기 위하여, 교차 검증 방법과 붓스트랩(Bootstrap) 검증 방법을 이용하였다. 또한 Cost function과 학습 성공률(Accuracy)이 Train 데이터와 Test 데이터가 동일함을 확인하였다.

Keywords

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