• Proceedings of the KAIS Fall Conference
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• 2011.12b
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• pp.693-696
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• 2011

본 논문에서는 최근 발생되는 기상이변의 원인인 에너지의 과도한 사용을 감소시키기 위하여 사용자가 인지하지 못하는 동안 낭비되고 있는 대기전력을 자동으로 제어하는 시스템을 구현하였다. 현재 사용되고 있는 대기전력 제어 시스템의 경우 일정 전력 이하의 전력량이 감지되면 자동으로 차단하는 형태로 운영되고 있으나 재가동을 위해서는 사용자의 수동 제어에 의존해야 하는 불편함이 발생한다. 이에 본 논문은 사용하지 않는 가전기기의 대기전력을 차단할 뿐만 아니라 사용자의 편의성을 고려하여 자동으로 전력을 재공급하는 대기전력 컨트롤러를 구현한다. 기기의 전력 재공급은 각 사용자별 기기 사용패턴을 고려하여 구현하였으며, 이때 사용자의 구분은 2개의 Ultrasonic 센서로부터 산출된 사용자의 키와 무게 감지 센서로부터 산출된 사용자의 몸무게를 활용하였다.

• Proceedings of the KIPE Conference
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• 2004.07b
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• pp.918-921
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• 2004

In a motor drive, the current rating is directly related to the rating of a switching device, and the parallel switching operation for a cost reduction is the alternatives because it has the smaller current rating through current division. There are many investigations for the parallel switching operations to equaling the current division. However it remains many problems for practical usage. This paper proposes a new parallel operation which uses a parallel phase winding to remove the traditional effect of switching device such as saturation voltage according to the division of current. The proposed strategy is verified by theoretical and experimental results.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.84-93
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• 2018

In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of power energy consumption. To this end, we consider the volatility of the time series and apply the sample variance and the detrended fluctuation analyses to the volatilities. We demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the power consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the power consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of "context units" in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the power consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. To further confirm the experimental results, we performed two types of the cross validations designed for the time series data. We also support the validity of the model by analyzing the multi-step forecasting. We found that the prediction errors tend to be saturated although they increase as the prediction time step increases. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2009.10a
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• pp.279-283
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• 2009

Distributed generation(DG) of combined cooling, heat. and power(CCHP)has been gaining momentum in recent year as efficient, secure alternative for meeting increasing energy demands. This paper presents the energy performance of microturbine CCHP system equipped with an absorption chiller by modelling it in hospital building. The orders of study were as following. 1)The list and schedule of energy consumption equipment in hospital were examined such as heating and cooling machine, light etc. 2) Annual report of energy usage and monitoring data were examined as heating, cooling, DHW, lighting, etc. 3) The weather data in 2007 was used for simulation and was arranged by meteorological office data in Daejeon. 4) Reference simulation model was built by comparison of real energy consumption and simulation result by TRNSYS and ESP-r. The energy consumption pattern of building were analyzed by simulation model and energy reduction rate were calculated over the cogeneration. As a result of this study, power generation efficiency of turbine was about 30% after installing micro gas turbine and lighting energy as well as total electricity consumption can be reduced by 40%. If electricity energy and waste heat in turbine are used, 56% of heating energy and 67% of cooling energy can be reduced respectively, and total system efficiency can be increased up to 70%.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.121-129
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• 2009

Inspection of an Electrical Meter is an action of measuring power usage to charge electricity rates and Electricity AMR(Automatic Meter Reading) is a system to automatize the action. AMR has been highlighted because it can reduce metering cost by substituting an automatic system for personnel and strengthen customer service. In this paper, we proposed and developed a smart card based AMR framework SCEMS as an alternative to other current AMR Models. This proposed SCEMS uses a java card based multi-application smart card and supports customer service such as various meter rates according to electricity consumption pattern data per household and transaction data that are accumulated in a smart card. This research can be a solution to the problems such as diversity, heterogeneity, and complexity that environmental changes will cause soon to the power supply industry.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.9
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• pp.1242-1248
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• 2012

Electric vehicle usage is currently very low, but it will be increase with development of electric vehicle technology and a good government policy. Moreover in 2020, advanced electric vehicle manufacturing system will give high performance for its price and mass production. Electric vehicle will become widespread in Korea. From an operational and a planned viewpoint, the electric power demand should be considered in relation to diffusion of electric vehicles. This paper presents the impact of the various battery charge systems. A comparison is performed for electric vehicle charging methods such as, normal charging, fast charging, and battery swapping. In addition, economic evaluation for the replaceable battery system and the quick battery charging system is performed through basic information about charging Infrastructure installation cost. The results of the evaluation show that replaceable battery system is more economical and reliable in side of electric power demand than quick battery charging system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.88-95
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• 2009

This paper presents a traffic pattern matching hardware that can be used in high performance network applications. The presented hardware is designed for a contents security system which is to block various kinds of information drain or intrusion activities. The hardware consists of two parts: the header lookup and string pattern matching parts. For implementing the header lookup part in hardware, the TCAMs(ternary CAMs) are popularly used. Since the TCAM approach is inefficient in terms of the hardware and memory costs and the power consumption, however, we adopt and modify an alternative approach based on the comparator arrays and the HiCuts tree. Our implementation results, using Xilinx FPGA XC4VSX55, show that our design can reduce the usage of the FPGA slices by about 26%, and the Block RAM by about 58%. In the design of string pattern matching part, we design and use a hashing module based on cellular automata, which is hardware efficient and consumes less power by adaptively changing its configuration to reduce the collision rates.

• 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 Korean Institute of Intelligent Systems
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• v.21 no.6
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• pp.679-685
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• 2011

This paper presents an algorithm identifying devices that generate observed mixed signals that are collected at main power-supply line. The proposed algorithm, which is necessary for low cost electric power monitoring system at appliance-level, that is non-intrusive load monitoring system, divides incoming mixed signal into multiple time intervals, calculating difference-signals between consecutive time interval, and identifies which device is operating at the time interval by analysing the difference-signals. Since the features of one device can remain when the time interval is short enough and the features are independent and additive, well-known classification algorithms can be used to classify the difference-signals with features of N individual devices, otherwise $2^N$ features might be necessary. The proposed algorithm was verified using data mixed in a laboratory with individual devices's data collected from field. When maximum 4 devices operate or stop sequentially and when features satisfy the requirements of proposed algorithm, the proposed algorithm resulted nearly 100% success rate under the constrained test condition. In order to apply the proposed algorithm in real world, the number devices shall increase, the time interval shall be smaller and the pattern of mixture shall be more diverse. However we can expect, if features used follow guidelines of proposed algorithm, future system could have certain level of performance without the guideline.

• Journal of IKEEE
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• v.24 no.2
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• pp.632-637
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• 2020

The non-intrusive load monitoring (NILM) is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance in a home or business using aggregated energy from a single recording meter. In this paper, we collect from the smart farm's power consumption data acquisition system to the server via an LTE modem, converted the total power consumption, and the power of individual electric devices into HDF5 format and performed NILM analysis. We perform NILM analysis using open source denoising autoencoder (DAE), long short-term memory (LSTM), gated recurrent unit (GRU), and sequence-to-point (seq2point) learning methods.