• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.3
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.97-100
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• 2009

홈네트워크 환경에서 정보가전기기를 제어하는데 PLC(전력선통신)나 IR컬트롤러가 사용된다. PLC의 경우 가전기기동작이 명확하지만 IR컨트롤러 같은 경우에는 방향이나 여러 조건에 따라 가전기기동작이 불분명하여 사용자의 명령대로 수행되지 못하는 경우가 발생한다. 본 논문에서는 이러한 상황을 해결하기 위해 가전기가의 상태를 모니터링하여 사용자에게 알려주는 방법을 제안하였다. 동작 상태와 대기 상태의 전류량이 다른점을 이용하여 동작 여부를 판별한다. 제안된 방법을 사용하면 IR 컨트롤러로 동작하는 가전기기의 상태 신뢰성 을 높일 수 있다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.109-116
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• 2021

In this paper, we propose a non-intrusive load monitoring(NILM) system which can find the power of each home appliance from the aggregated total power as the activation in the trading market of the distributed resource and the increasing importance of energy management. We transform the amount of appliances' power into a power on-off state by preprocessing. We use LSTM as a model for predicting states based on these data. Accuracy is measured by comparing predicted states with real ones after postprocessing. In this paper, the accuracy is measured with the different number of electronic products, data postprocessing method, and Time step size. When the number of electronic products is 6, the data postprocessing method using the Round function is used, and Time step size is set to 6, the maximum accuracy can be obtained.