• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.638-644
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• 2010

This paper presents ideas of service scenarios for home residents using electric power monitoring system per appliance, the implementation of the monitoring system, and analysis of acquired electric power usage pattern. By acquiring and analyzing electric power usage pattern, home residents can get information of power usage pattern of every legacy (non-Demand Response-ready) appliance. Further they can get pieces of recommendation how to reduce energy consumption, intelligent standby power blocking service, and alarming service to abnormality of appliances. In order to check the feasibility of the ideas, a system that can acquire electric power pattern per appliance is implemented, and electric power pattern of some appliances are stored to a database and it was analyzed to show if auto-identification of a type of a device is possible, which is a basic required function for the scenarios presented.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.16-22
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• 2016

This paper addresses the load usage scheduling in the HEMS for residential power consumers. The HEMS would lead the residential users to change their power usage, so as to minimize the cost in response to external information such as a time-varying electricity price, the outside temperature. However, there may be a consumer's inconvenience in the change of the power usage. In order to improve this, it is required to understand the pattern of load usage according to the external information. Therefore, this paper suggests a methodology to model the load usage pattern, which classifies home appliances according to external information affecting the load usage and models the usage pattern for each appliance based on a copula function representing the correlation between variables. The modeled pattern would be reflected as a constraint condition for an optimal load usage scheduling problem in HEMS. To explain an application of the methodology, a case study is performed on an electrical water heater (EWH) and an optimal load usage scheduling for EHW is performed based on the branch-and-bound method. From the case study, it is shown that the load usage pattern can contribute to an efficient power consumption.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.68-77
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• 2018

Regarding demand response (DR) by residential users (R-users), the users try to reduce electricity costs by adjusting their power consumption in response to the time-varying price. However, their power consumption may be affected not only by the price, but also by user convenience for using appliances. This paper proposes a methodology for appliance scheduling (AS) that considers the user convenience based on historical data. The usage pattern for appliances is first modeled applying the copula function or clustering method to evaluate user convenience. As the modeling results, the comfort distribution or representative scenarios are obtained, and then used to formulate a discomfort index (DI) to assess the degree of the user convenience. An AS optimization problem is formulated in terms of cost and DI. In the case study, various AS tasks are performed depending on the weights for cost and DI. The results show that user convenience has significant impacts on AS. The proposed methodology can contribute to induce more DR participation from R-users by reflecting properly user convenience to AS problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.407-413
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• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• Journal of Dental Rehabilitation and Applied Science
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• v.34 no.1
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• pp.10-16
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• 2018

Purpose: The purpose of this study was to evaluate distalizing effects from the Pendulum appliance on vertical component of craniofacial structures. Materials and Methods: 20 Patients who visited for orthodontic treatments are assigned to two groups. Group I, SN-MP > 37 degrees are showing hyperdivergent pattern. Group II, 29 < SN-MP < 37 degrees are showing mesocephalic pattern. Each group are consisted of 10 people. Results and Conclusions: Differences between skeletal classifications result in significant differences at labioversion of lower incisors and distalized amount, which is larger at Group I (P <.05). Group II has only shown significant distalized molars (P < .05). Labioversion of lower incisors has not shown significant change. Skeletal anchorage-assisted Pendulum appliance doesn't deteriorate vertical component nor significantly improve.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.9-15
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• 2010

Recently, Smart Grid is a emerging topic in power and communication industry. Smart Grid refers to a evolution of the electricity supply infrastructure that monitors, protects, and intelligently optimize the operation of the interconnected elements including various type of generators, power grid, building/home automation system and end-use consumers. In order to successful implementation of Smart Grid, energy management function will be the key factor that coordinates and optimally controls the various loads according to the operating condition and environments, and the load patterns in residential and commercial building will be required as fundamental element for load management. In this study, we collects many types of energy usage data of electric appliances, analyze their load curves, and make the general load patterns for electrical appliance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.1957-1980
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• 2021

The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.5
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• pp.487-493
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• 1999

To predict the background level of harmonics produced by household appliances, information on the site, capacity, and usage pattern of these loads arenecessary. However, as household appliances are distributed widely and various in type, it is difficult to know these kinds of information accurately. This paper presents a method for estimation of background level of harmonics produced by distributed harmonic sources with readily available data. Large industrial customers are excluded from this study. In this paper, customers are grouped into three classes, i.e. residential, commercial, and industrial. Typical customers for each class are assumed and characteristics of their equipments are modeled. As the proposed method does not require harmonic measurement, it can be employed to forecast voltage total harmonic distribution (VTHD) in the future. An illustrative example is described.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.300-306
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• 2011

In this paper, we suggest a usage profiles for electric home appliances. In Korea, it is published the records for total consumption of electricity in a house but the electric home appliance consumption records in a households are not. To build the data, we must collect the usage of every appliances in a house and the information of the household which live in the house. Unfortunately, it is hard to get the data because of the worry about the breach of privacy. In this paper, we make a scenarios on the electricity consumption pattern of a few households type. Based on the conjecture, we make the power consumption profiles for some home appliances. Comparison to the total electric consumption records for a house, we found our scenarios are quite reasonable.