• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.34-42
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• 2017

Since existing nonintrusive appliance load monitoring (NIALM) studies assume that voltage fluctuations are negligible for load identification, and do not affect the identification results, the power factor or harmonic signals associated with voltage are generally not considered parameters for load identification, which limits the application of NIALM in the Smart Home sector. Experiments in this paper indicate that the parameters related to voltage and the characteristics of harmonics should be used to improve the accuracy and reliability of the load monitoring system. Therefore, in this paper, we propose an improved NIALM method that can efficiently analyze the types of household appliances and electrical energy usage in a home network environment. The proposed method is able to analyze the energy usage pattern by analyzing operation characteristics inherent to household appliances using harmonic characteristics of some household appliances as recognition parameters. Through the proposed method, we expect to be able to provide services to the smart grid electric power demand management market and increase the energy efficiency of home appliances actually operating in a home network.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.55-63
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• 2017

Due to spatial problems and system size,conventional measurement methods used to acquire the information needed for existing electrical energy and management have been limited to new buildings or areas where replacement is possible. This electric load management method is problematic when applying it to energy and safety management of vulnerable areas or existing homes or offices. The problem with installing a measurement module in every branch is that the system is too large. Even if the measurement module is installed, the type of load is not recognized, and efficient management is not performed. In particular, it is very difficult to apply it to traditional markets and backward facilities in Korea. In this paper, we apply NIALM technology and arc detection technology to solve these problems and verify the feasibility of NIALM for normal arc generation. Also, based on the verification results, we propose a new smart home safety management system that can effectively manage electrical safety and that can be applied to conventional market and existing home safety management systems. The proposed system conducts a comparative performance test with an existing safety management system. In addition, it achieves 95% or more load recognition for four loads, which is impossible in 40% of the existing systems, and the arc detection function was confirmed.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.956-958
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• 2016

비절전 가전기기는 제품수명을 고려할 때 전력을 소비하는 가정 내 가전기기의 대부분을 차지하지만 에너지 효율 정책의 사각지대에 있다. 본 논문에서는 비절전 가전기기의 에너지 절감을 위해 전력 총량에서 각 가전기기의 상태를 식별하는 NIALM의 기기 식별률을 복합센서를 활용해 개선하는 기술에 대해서 소개하고자 한다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.199-204
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• 2017

In this study, we present a home appliance load identification algorithm. The algorithm utilizes complex sensory data in order to improve the existing NIALM using total power usage information. We define the influence graph between the appliance status and the measured sensor data. The device identification prediction result is calculated as the weighted sum of the predicted value of the sensor data processing algorithm and the predicted value based on the total power usage. We evaluate proposed algorithm to compare appliance identification accuracy with the existing NIALM algorithm.