• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.4
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• pp.213-219
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• 2015

Real-time energy monitoring systems is a demand-response system which is reported to be effective in saving energy up to 12%. Real-time energy monitoring system is commonly composed of smart-plugs which sense how much electrical power is consumed and IHD(In-Home Display device) which displays power consumption patterns. Even though the monitoring system is effective, users should themselves match which smart plus is connected to which appliance. In order to make the matching work to be automatic, the monitoring system need to have appliance identification algorithm, and some works have made under the name of NILM(Non-Intrusive Load Monitoring). This paper proposed an algorithm which utilizes multiple classifiers to improve accuracy of appliance identification. The algorithm proposes to understand each classifiers performance, that is, when a classifier make a result how much the result is reliable, and utilize it in choosing the final result among result candidates from many classifiers. By using the proposed algorithm this paper make 4.5% of improved accuracy with respect to using single best classifier, and 2.9% of improved accuracy with respect to other method using multiple classifiers, so called CDM(Commitee Decision Mechanism) method.

• 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.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-10
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• 2017

The high costs of electric vehicle supply equipment (EVSE) and installation are currently a stumbling block to the proliferation of electric vehicles (EVs). The cost-effective solutions are needed to support the expansion of charging infrastructure. In this paper, we develope EV charging system based on the big data analysis of the power consumption patterns. The developed EV charging system is consisted of the smart EV outlet, gateways, powergates, the big data management system, and mobile applications. The smart EV outlet is designed to low costs of equipment and installation by replacing the existing 220V outlet. We can connect the smart EV outlet to household appliances. Z-wave technology is used in the smart EV outlet to provide the EV power usage to users using Apps. The smart EV outlet provides 220V EV charging and therefore, we can restore vehicle driving range during overnight and work hours.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.21-28
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• 2021

In this study, we propose a deep learning-based NILM technique using actual measured power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks, the active power of the central power measuring device and five kinds of home appliances (refrigerator, induction, TV, washing machine, air cleaner) was individually measured. The preprocessing method of the measured data was introduced, and characteristics of each household appliance were analyzed through spectogram analysis. The characteristics of each household appliance are organized into a learning data set. All the power data measured by the central power measuring device and 5 kinds of home appliances were time-series mapping, and training was performed using a LSTM neural network, which is excellent for time series data prediction. An algorithm that can disaggregate five types of energies using only the power data of the main central power measuring device is proposed.

• 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.